{
  "sims": {
    "unmet_v11_label_background": [
      "Both datasets feature overhead or top-down shots of food arrangements with multiple dishes or garnishes artfully placed on a table or plate",
      "Both include busy office or desk scenes showing computer monitors, keyboards, chairs, and scattered stationery in an indoor environment",
      "Both present individual objects (e.g., shields) centrally framed against neutral or plain backdrops for emphasis and clear visibility",
      "Both contain images of people wearing traditional Japanese attire (kimonos), often captured in street or indoor event settings",
      "Both include medieval-themed subjects such as knights in armor, shields, and heraldic wall hangings, typically centered and well-lit",
      "Both contain architectural photographs of churches or historic stone buildings, shot with a wide field of view and balanced composition",
      "Both datasets use predominantly natural or ambient light sources (window light, overhead indoor lighting) with minimal harsh shadows",
      "Both often employ a moderate depth of field, keeping the main subject sharp while softly blurring the background",
      "Both show cluttered or richly detailed scenes where a primary element (food, object, person) is surrounded by secondary items for context",
      "Both datasets favor symmetrical or balanced framing, placing the key subject or focal point near the center or along compositional axes"
    ],
    "unmet_v11_label_only": [
      "Both datasets include carefully arranged still-life compositions that place a central subject (food on plates, tableware, shields, decorative objects) on a textured or styled surface.",
      "Both feature top-down or slight oblique overhead perspectives, especially for images of plates, desktops, and flat arrangements, to emphasize the layout and contents in a single plane.",
      "Both use a restrained, neutral background (e.g., plain walls, wooden tables, stone floors) to isolate and highlight the main subject without distracting elements.",
      "Both capture interior architectural scenes (church interiors and exteriors, rooms, offices) with wide-angle framing that emphasizes symmetry, leading lines, and spatial depth.",
      "Both rely on soft, diffused ambient lighting\u2014often natural window light or subtle artificial fixtures\u2014to create gentle shadows and evenly illuminate textures.",
      "Both include portraits or half-body shots of people in traditional attire (such as kimonos) set against simple backgrounds, using shallow depth of field to draw attention to clothing details.",
      "Both datasets showcase rich textures and patterns (floral china designs, kimono fabrics, carved wood or stone, wrought metal), photographed to preserve fine detail and surface intricacies.",
      "Both employ balanced color palettes, pairing richly colored subjects (bright foods, colorful garments, gilded shields) with muted surroundings to enhance visual contrast.",
      "Both maintain consistent horizontal framing and level camera angles, positioning key subjects centrally or using classic compositional rules (rule of thirds, symmetry) for visual harmony.",
      "Both occasionally use selective focus or vignetting\u2014blurring or darkening the periphery\u2014to guide the eye directly to the main subject and reinforce the focal point."
    ],
    "unmet_v11_label_relation": [
      "Both datasets feature stylized table-top still-life shots with objects (plates, food, flowers) carefully arranged on flat surfaces, often from overhead or slight angles.",
      "Both include indoor workspace scenes\u2014desks, computers, chairs, office accessories\u2014shot under balanced lighting to highlight textures and details.",
      "Architectural photography of religious buildings (churches and cathedrals) appears in both, using symmetrical compositions and emphasizing intricate stone or wood textures.",
      "Each contains images of clothing items (kimonos, robes, dresses) either displayed on hangers, laid out, or worn by people in controlled settings or on the street.",
      "A neutral or minimally distracting background is commonly used to isolate the main subject and draw the viewer\u2019s attention.",
      "Balanced, even lighting\u2014either natural or studio\u2014emphasizes color, texture, and form across still lifes, food, and interior scenes.",
      "Subjects are often centrally framed, employing symmetry or strong compositional balance to create a clear focal point.",
      "Both sets include decorative objects and props (vases, shields, ornamental carvings) shot as standalone subjects with minimal clutter.",
      "Flat-lay and overhead perspectives are a recurring technique for presenting food, craft items, and small objects.",
      "Everyday scenes and objects are presented in an aesthetically refined manner, blurring the line between product, still life, and environmental photography."
    ],
    "unmet_v15_label_only": [
      "Both datasets include stylized food photography showing arranged plates or table settings with props and garnishes",
      "Both contain images of decorative shields or heraldic emblems mounted on textured backgrounds",
      "Both feature individuals wearing traditional kimonos or similar elaborate garments in posed or candid shots",
      "Both include architectural photography of churches or cathedrals, capturing both exterior facades and interior nave details",
      "Both show desk or workspace scenes with laptops, monitors, office supplies, and cluttered desktops",
      "Both make use of selective focus and shallow depth-of-field to isolate the main subject from the background",
      "Both datasets use a mix of natural and artificial lighting, often with a moody or HDR-inspired treatment",
      "Both employ overhead or top-down compositions for table arrangements, plates, and dining setups",
      "Both feature subjects set against textured backdrops such as wooden walls, stone, or fabric",
      "Both mix indoor and outdoor environments, balancing controlled studio-style shots with candid location photography"
    ],
    "unmet_v15_label_background": [
      "Both datasets feature overhead flat-lay compositions of plated food with the table surface filling the background.",
      "Both show indoor restaurant or kitchen scenes shot under ambient or diffused natural light, giving a soft, even illumination.",
      "Both contain still-life arrangements of objects (dishes, utensils, shields) placed centrally and often symmetrically on a flat surface.",
      "Both include office or desk environments photographed from a frontal or slightly elevated angle, with computers, papers, and cables spread across the workspace.",
      "Both present traditional cultural subjects (kimonos, coats of arms, shields, armor) shot front-on against either plain walls or contextual backdrops like markets or museum displays.",
      "Both offer architecture images of churches or cathedrals, composed symmetrically along the central axis and often using wide-angle perspectives.",
      "Both capture street or market stall scenes with colorful textiles or garments hung on racks, framed so that the display fills most of the image.",
      "Both utilize shallow depth-of-field for portrait-style shots, particularly of people in traditional attire, isolating the subject from the background.",
      "Both employ a blend of ambient and artificial indoor lighting, resulting in warm, inviting color tones.",
      "Both datasets favor symmetrical framing around a central subject\u2014whether it\u2019s an altar, a desk, or a mounted shield\u2014to create a balanced composition."
    ],
    "unmet_v15_label_relation": [
      "Both datasets contain numerous examples of plated food photographed from a top-down or slightly angled viewpoint, showcasing the dish against a minimal table setting.",
      "Both include indoor office/desk scenes captured at eye level, with computers, chairs, stationery, and moderate clutter under ambient window or fluorescent light.",
      "Both show decorative single objects\u2014like shields or ceramic plates\u2014displayed against plain or textured walls and evenly illuminated to bring out surface details.",
      "Both capture people wearing traditional garments (e.g., kimonos) in a portrait or three-quarter view, often set against softly blurred backgrounds with natural light.",
      "Both feature architectural shots of churches and cathedrals using symmetrical compositions and central vanishing points to emphasize structural lines and depth.",
      "Both utilize moderate depth of field to isolate main subjects (food, objects, people) while gently blurring the surroundings for emphasis.",
      "Both favor centered compositions where the primary subject occupies the middle region of the frame for immediate visual focus.",
      "Both rely on natural or ambient lighting rather than dramatic studio setups, resulting in even illumination and soft, realistic shadows.",
      "Both maintain true-to-life color reproduction, accurately rendering textures such as wood grain, stone surfaces, fabric patterns, and fresh ingredients.",
      "Both datasets illustrate scenes composed to highlight a single main element with minimal background distractions, enabling clear subject recognition."
    ]
  },
  "diffs_synth_from_real": {
    "unmet_v11_label_background": [
      "Dataset A consists of genuine photographs of everyday scenes and objects, whereas Dataset B features largely synthetic or AI-generated imagery with an uncanny, highly stylized appearance",
      "Dataset A employs natural or standard indoor lighting that produces realistic shadows and highlights, while Dataset B often uses flat, evenly distributed or dramatic unreal lighting with minimal true shadows",
      "Dataset A images show true\u2010to\u2010life color reproduction and organic textures, whereas Dataset B exhibits saturated or pastel color palettes and unnaturally smooth or painterly surfaces",
      "Dataset A backgrounds are real environments\u2014offices, restaurants, historic buildings\u2014often cluttered and imperfect, but Dataset B\u2019s backgrounds are surreal, CGI-like or abstract settings that defy real architectural logic",
      "Dataset A compositions follow conventional photography framing (straight-on, top-down, centered), but Dataset B experiments with odd perspectives, extreme symmetry or deliberately artificial layouts",
      "Dataset A food and object shots adhere to believable scale and context, while Dataset B juxtaposes improbable items, hybrid creatures and fantastical scenes that break the rules of physical reality",
      "People in Dataset A appear as real subjects with natural poses and recognizable features, whereas figures in Dataset B often have distorted anatomy, blurred faces or mannequin-like expressions",
      "Dataset A shows consistent depth of field appropriate to real camera settings, in contrast to Dataset B\u2019s inconsistent focus where entire scenes or isolated elements are unnaturally sharp or blurred",
      "Dataset A architectural images capture true historic buildings or churches with authentic aging and weathering, while Dataset B\u2019s structures look digitally rendered, with impossible geometries or pristine, idealized materials",
      "Dataset A imagery is documentary-style and contextually coherent, whereas Dataset B feels concept-driven and stylized, often combining multiple themes or genres within a single frame"
    ],
    "unmet_v11_label_only": [
      "Dataset B images exhibit a hyper-real or computer-rendered aesthetic with perfectly smooth surfaces, uniform color grading, and improbably clean details, whereas Dataset A consists of candid, real-world photographs showing sensor noise, lens artifacts, and natural imperfections.",
      "Backgrounds in Dataset B are deliberately minimal\u2014often blank or softly blurred walls, tidy studio settings or symmetric architectural backdrops\u2014while Dataset A backgrounds are varied and cluttered (posters, office supplies, chairs, people) reflecting un-staged environments.",
      "Lighting in Dataset B is consistently soft and diffuse, with evenly illuminated scenes and little directional shadow, giving a \u2018studio-render\u2019 look; Dataset A lighting comes from mixed natural and artificial sources, creating harsher highlights, deep shadows, and visible color casts.",
      "Composition in Dataset B tends toward rigid central framing and perfect symmetry, placing subjects dead-center for a templated feel; Dataset A shows off-center, dynamic compositions with real photographers\u2019 framing quirks and environmental context.",
      "Color palettes in Dataset B are highly saturated yet controlled, with smooth transitions and no blown highlights, whereas Dataset A displays uneven color balance, occasional overexposure, warm or greenish tints, and varied saturation across scenes.",
      "Depth of field in Dataset B is often unrealistically deep (everything tack-sharp) or perfectly shallow in a stylized way; Dataset A demonstrates real lens focus fall-off, occasional mis-focus, and organic bokeh irregularities.",
      "Textures in Dataset B appear unnaturally pristine and uniform (flawless wood grains, metal gleams, fabric prints), while Dataset A surfaces show real wear\u2014scratches, dust, creases, and textural noise from the camera sensor.",
      "Architectural shots of churches and interiors in Dataset B look like CGI models\u2014overly crisp edges, flawless symmetry, and extreme dynamic range\u2014compared to Dataset A\u2019s genuine HDR or film-like photos with lens flare, grain, and imperfect verticals.",
      "Portraits and figures in Dataset B wear clothing and skin that look generically \u2018perfect\u2019 (no wrinkles, artificial drape), while Dataset A captures real people with natural posture, clothing folds, stray hairs, and un-posed body language.",
      "Office and tabletop scenes in Dataset B showcase minimalist, design-forward furniture and styling (sleek desks, curated plants) under controlled conditions; Dataset A offices are personal and cluttered\u2014papers, mugs, personal photos\u2014evidencing a lived-in workspace."
    ],
    "unmet_v11_label_relation": [
      "Dataset A consists largely of candid snapshots taken with consumer cameras in uncontrolled environments (offices, homes, restaurants), whereas Dataset B contains highly curated, studio-style compositions with professional lighting and staging.",
      "Dataset A images often include people and dynamic, real-world scenes, while Dataset B focuses heavily on still-life arrangements and inanimate objects with minimal or no human presence.",
      "Backgrounds in Dataset A are busy and cluttered with personal or office items, whereas Dataset B uses neutral, textured, or stylized backdrops that isolate and highlight the subject.",
      "Framing in Dataset A is informal and varied\u2014with off-center subjects, tilt and casual cropping\u2014while Dataset B employs precise, symmetrical or overhead flat-lay perspectives.",
      "Lighting in Dataset A is mixed ambient (office fluorescents, restaurant tungsten), yielding uneven exposure; Dataset B uses balanced, diffuse or directional studio lighting for consistent brightness and color fidelity.",
      "Dataset A exhibits camera artifacts typical of snapshots (noise, lens distortion, motion blur), whereas Dataset B images are uniformly crisp, high-contrast, and free of such imperfections.",
      "Subjects in Dataset A are authentic, documentary-style real-world scenes (workspaces, meals, churches), but Dataset B introduces highly stylized tableau and occasional fantasy elements (unicorns, cosmic skies).",
      "Dataset A compositions feel spontaneous and unposed, whereas Dataset B scenes are meticulously arranged with decorative props (platters, utensils, fruits, flowers) and deliberate color coordination.",
      "Color palettes in Dataset A range widely and sometimes suffer from poor white balance, while Dataset B favors vibrant, harmonious color schemes and often pastel or neutral table settings.",
      "Dataset B consistently minimizes background distractions to emphasize texture and form, in contrast to Dataset A\u2019s contextual, environment-rich snapshots."
    ],
    "unmet_v15_label_only": [
      "Dataset B images have an artificial, hyper-real or CGI-like polish with uniform styling and digitally generated textures, whereas Dataset A consists of authentic amateur or professional photographs showing the natural imperfections of real cameras and environments.",
      "Dataset B employs bright, highly saturated color grading and soft, diffuse studio-style lighting across nearly every scene, while Dataset A exhibits a wide range of color renditions, including harsh flash, mixed natural-indoor light and occasional color casts.",
      "Dataset B shots are composed around clean, minimalist backgrounds (smooth wood, neutral fabric) with subjects centered or photographed straight on or perfectly overhead, whereas Dataset A compositions are more incidental and varied, often including cluttered rooms, incidental background elements or off-axis framing.",
      "Dataset B makes exclusive use of shallow depth-of-field to isolate the subject against blurred backdrops, while Dataset A images frequently have greater depth-of-field, keeping entire desk setups, room interiors or architectural scenes in focus.",
      "Dataset B scenes rarely include people, focusing instead on objects or table layouts in a controlled studio context; Dataset A regularly features humans in candid or posed shots alongside their working or dining environments.",
      "Dataset B lighting is consistently soft, even and shadow-free as if in a light-tent, whereas Dataset A shows mixed lighting conditions\u2014sharp shadows, lens flares, noisier exposures and natural window or overhead office lights.",
      "Dataset B\u2019s food and table settings look like editorial food-styling with perfectly arranged garnishes on pristine plates, while Dataset A\u2019s dishes appear as everyday restaurant or home-cooked presentations with real tableware, spills and casual plating.",
      "Dataset B exhibits no camera artifacts\u2014no motion blur, no sensor noise\u2014while Dataset A images often reveal motion blur, digital noise, vignetting or other real-world photography artifacts.",
      "Dataset B interiors and exteriors (churches, desks) have a polished, HDR-inspired look with even illumination and saturated contrast, whereas Dataset A\u2019s architectural and office scenes preserve the mood and limitations of single-shot exposures.",
      "Dataset B has a cohesive, curated look across all images as if from a single studio photo-shoot pipeline; Dataset A is a heterogeneous collection of snapshots from varied sources, cameras, locations and lighting setups."
    ],
    "unmet_v15_label_background": [
      "Dataset A consists of real\u2010world photographs with natural textures, organic noise, and realistic depth cues, Dataset B contains highly stylized or digitally rendered images with smooth surfaces, painterly textures, and synthetic lighting effects.",
      "Dataset A backgrounds are often full of contextual clutter\u2014office desks with wires, restaurant tables with props, museum walls with exhibits\u2014Dataset B uses minimalistic or abstract backdrops, frequently single\u2010tone surfaces or studio\u2010style tabletops.",
      "Food in Dataset A is typically shot at eye level or a three\u2010quarter angle to emphasize perspective and plating depth, whereas in Dataset B most food shots are strict overhead flat\u2010lays with uniform illumination and little visible context.",
      "Architectural and object photos in Dataset A employ careful symmetrical framing and natural vanishing lines from wide\u2010angle lenses, Dataset B often shows skewed or off\u2010axis perspectives, digital warping, or decorative vignetting around the edges.",
      "Dataset A features natural color balances with subtle, realistic shadows and diffuse highlights, Dataset B displays bold color grading\u2014pastel washes, high saturation, unusual color casts, and hard specular highlights.",
      "Portraits and cultural attire in Dataset A use shallow depth of field to isolate subjects and convey atmosphere, Dataset B generally presents subjects in uniform focus with flatter lighting and less realistic skin or fabric detail.",
      "Office and workspace scenes in Dataset A show genuine clutter\u2014notes, cables, personal items\u2014Dataset B recreates clean, minimal workspaces with stylized furniture, carefully placed greenery, and almost no stray objects.",
      "Dataset A exhibits lens artifacts such as slight blur falloff, natural grain, and ambient occlusion, Dataset B shows smooth, digitally sharpened edges or visible rendering artifacts from image synthesis.",
      "In Dataset A, lighting falloff and cast shadows vary naturally across the scene, Dataset B often employs even, hard studio lights or painted\u2010on glow effects with little to no realistic shadow transitions.",
      "Images of shields, armor, and architectural reliefs in Dataset A are shot in situ\u2014museums, outdoor crypts, walls with real wear\u2014Dataset B presents them as isolated, heavily textured objects, sometimes floating against decorative or fantastical environments."
    ],
    "unmet_v15_label_relation": [
      "Dataset A images tend to show a single, isolated subject (a plate of food, a shield on a wall, a desk, a church interior) often centered and symmetrically composed, whereas Dataset B frequently uses off-center framing with multiple elements or layers in the scene for a more editorial or lifestyle feel.",
      "Dataset A relies on consistent ambient or fluorescent lighting for an even, documentary look, whereas Dataset B experiments with natural window light, moody directional illumination, and higher contrast to create depth and drama.",
      "Backgrounds in Dataset A are kept minimal or uniformly textured (plain walls, single-tone tabletops), whereas Dataset B embraces richer, more cluttered or stylized environments (decorative interiors, outdoor greenery, event crowds) that add visual context.",
      "In Dataset A, depth of field is moderate\u2014subjects are isolated by gently blurred backgrounds\u2014whereas Dataset B alternates between shallow DOF to accentuate details and deep focus to capture complex scenes with many interacting elements.",
      "People in Dataset A are typically photographed in static, formal or posed portrait style against simple backdrops, whereas Dataset B shows more dynamic human subjects in candid or action contexts (festivals, reenactments, group gatherings).",
      "Color reproduction in Dataset A is natural and subdued, faithfully rendering textures and materials, whereas Dataset B often showcases more saturated or stylized palettes\u2014bright florals, rich wood tones, decorative fabrics\u2014to evoke an editorial mood.",
      "Dataset A\u2019s interiors (offices, studios, church naves) are practical and uncluttered, focusing on function, whereas Dataset B presents highly curated, magazine-style room compositions with designer furniture, artful decor, and carefully arranged props.",
      "Architectural shots in Dataset A emphasize perfect symmetry and central vanishing points inside churches, whereas Dataset B includes more varied angles\u2014exterior facades, close-up details, and environmental storytelling around the building.",
      "Still lifes in Dataset A (food platings, single objects) are shot from a consistent top-down or eye-level viewpoint, whereas Dataset B mixes vantage points\u2014low angles, three-quarters views, and oblique perspectives\u2014to add visual interest.",
      "Overall, Dataset A conveys a straightforward, documentary approach with minimal distractions, whereas Dataset B adopts a more editorial, stylized approach with layered compositions, varied lighting, and richer environmental context."
    ]
  },
  "diffs_real_from_synth": {
    "unmet_v11_label_background": [
      "Dataset A is dominated by synthetic or AI-generated imagery with painterly/CG-like textures and occasional distortions; Dataset B is composed of genuine photographs showing natural lighting and real camera artifacts (noise, grain, lens flare).",
      "Dataset A frequently presents overhead flat-lay food shots with uniform top-down framing; Dataset B features a wide variety of vantage points and angles (side views, three-quarter perspectives, slanted compositions).",
      "Dataset A background areas tend to be simplified, stylized, or artificially blended (flat colors, pattern-like repeats); Dataset B scenes include authentic environmental context, depth, and incidental clutter or furniture details.",
      "Dataset A often exhibits boundary artifacts or uncanny object blends (warping, misalignment, unexpected smudges); Dataset B maintains crisp, well-defined edges and accurate object shapes characteristic of real\u2010world subjects.",
      "Dataset A color palettes are sometimes hyper-saturated or unnaturally uniform; Dataset B uses real-world color balances with subtle variations in hue and realistic shadow/ highlight transitions.",
      "Dataset A rarely includes realistic human figures (and when it does, they appear stylized or mannequin-like); Dataset B contains actual people in natural poses, complete with genuine facial expressions and clothing textures.",
      "Dataset A compositions frequently lean toward perfect symmetry or overly neat object arrangements; Dataset B compositions are more organic, sometimes imperfect\u2014cropped edges, partial occlusions, incidental background actors.",
      "Dataset A applies depth-of-field effects inconsistently or uniformly across the frame; Dataset B displays natural photographic focus cues, with selective blurs and a believable depth gradient.",
      "Dataset A materials and surface textures often look repetitive or tiled (as if pattern-generated); Dataset B textures show real-world variation\u2014scratches, dust, fingerprints, wood grain, fabric wrinkles.",
      "Dataset A subjects and surfaces appear pristine and flawless (no dust, wear, or incidental markings); Dataset B images include genuine imperfections\u2014scuffed floors, office clutter, stray cat hairs, and lived-in details."
    ],
    "unmet_v11_label_only": [
      "Dataset A consists of highly stylized, studio-like still lifes\u2014mostly top-down or slight oblique overhead shots of plates, tableware or decorative objects\u2014while dataset B is made up of spontaneous, real-world photographs of offices, meals, ceremonies and reenactments with much more contextual clutter.",
      "Dataset A backgrounds are consistently neutral, textured surfaces (wood, slate, simple stone) chosen to isolate and highlight the subject; in dataset B the backgrounds are varied and busy\u2014office cubicles, restaurant tables, festival streets, museum rooms\u2014often with many distracting elements.",
      "Lighting in dataset A is uniformly soft and directional, creating even illumination and minimal shadows; in dataset B the lighting is variable\u2014harsh flash, mixed ambient, underexposure, motion blur\u2014reflecting natural or ad-hoc shooting conditions.",
      "Compositions in dataset A center the main object on a generous negative-space canvas with careful symmetry or rule-of-thirds framing; dataset B employs off-center, dynamic angles, wide-angle views or imperfect framing typical of casual snapshots.",
      "Dataset A almost never includes identifiable people\u2014only hands occasionally interacting with objects\u2014whereas dataset B frequently shows real individuals in indoor or outdoor settings, wearing traditional dress or casual attire.",
      "The color palettes in dataset A are deliberately muted and harmonious, often using complementary accents to make the subject pop; dataset B shows real color variation, white-balance shifts and competing tones within a single scene.",
      "Images in dataset A maintain a very uniform style and aspect ratio, suggesting a single photographic setup or synthetic generation; in dataset B there is a wide variety of aspect ratios, cropping styles and camera distortions.",
      "Dataset A captures only the subject plane\u2014flat layouts of food or objects\u2014while dataset B spans full interior and exterior architectural shots, office desks, church interiors and exteriors with depth and perspective.",
      "Dataset A\u2019s objects are generally clean, new and arranged with artistic intent; dataset B\u2019s objects are used, lived-in and candidly arranged for function rather than for a photographic aesthetic.",
      "Dataset A exhibits almost no visual noise, lens artifacts or focus inconsistencies, hinting at high-end gear or synthetic rendering; dataset B shows sensor noise, compression artifacts, variable focus and real-life imperfections from consumer cameras."
    ],
    "unmet_v11_label_relation": [
      "Dataset A consists of highly styled, professional or AI-generated-looking still-lifes and interiors with carefully arranged objects on clean surfaces, while Dataset B is made up of casual real-world snapshots that include cluttered desks, people, and everyday environments.",
      "Dataset A images favor minimal, neutral or textured but understated backgrounds to isolate subjects; Dataset B images often feature busy, uncontrolled backgrounds with multiple items, posters, or room details visible.",
      "Dataset A employs consistent flat-lay and overhead perspectives or precisely centered studio-style angles; Dataset B uses varied, eye-level or oblique consumer camera viewpoints without a unified framing approach.",
      "Dataset A lighting is uniformly soft and diffuse\u2014resembling studio or well-simulated illumination\u2014whereas Dataset B lighting varies widely, including harsh on-camera flash, mixed ambient light, and strong shadows.",
      "Dataset A exhibits cohesive color palettes, controlled highlights, and polished surfaces, giving an almost digital or editorial feel; Dataset B shows uncorrected white balance, saturation shifts, and natural wear on surfaces.",
      "Dataset A mostly shows inanimate objects (food, flowers, decorative shields, AI-fantasy scenes) in isolation; Dataset B frequently includes human subjects, pets, and candid action, grounding the scene in real activity.",
      "Dataset A compositions are often symmetrical or follow a deliberate grid/flat-lay layout; Dataset B compositions are spontaneous, with subjects sometimes off-center or partially cropped by chance.",
      "Dataset A contains only a handful of architectural/indoor shots that look highly processed or AI-rendered; Dataset B\u2019s architectural photos are genuine HDR or casual travel shots of churches and interiors under varied conditions.",
      "Dataset A removes most signs of daily life (no wires, personal effects, or food crumbs) to achieve an idealized aesthetic; Dataset B embraces everyday clutter\u2014sticky notes, cables, cups\u2014in the scene.",
      "Dataset A imagery feels consistent in style across all images (studio props, flat surfaces, neat arrangements), while Dataset B spans a wide spectrum of snapshot styles ranging from on-the-street kimono portraits to home desk corners."
    ],
    "unmet_v15_label_only": [
      "Dataset A images are almost entirely professionally styled or digitally rendered stock photographs with crisp lighting and polished composition, whereas Dataset B consists largely of amateur or candid snapshots with uneven flash or ambient lighting and spontaneous framing",
      "Dataset A tends to isolate a single subject (plate, shield, kimono, desk, church) against a neutral or textured backdrop, while Dataset B scenes are often busy, showing cluttered environments, multiple people, or contextual surroundings",
      "Food photography in Dataset A is dominated by overhead, symmetrical, high-contrast table arrangements on clean surfaces; in Dataset B most food shots are casual restaurant or home dinner photos in mixed lighting conditions and non-ideal angles",
      "Shields and heraldic emblems in Dataset A appear as standalone studio or museum-style captures on uniform backgrounds, whereas in Dataset B they are shown in situ \u2013 mounted on walls, leaning against objects, or being held by people in real-world settings",
      "Kimono subjects in Dataset A are usually singled out with shallow depth-of-field close-ups or editorial portraiture, but Dataset B\u2019s kimono images are candid street or festival photographs featuring multiple figures and broader environmental context",
      "Architectural church photos in Dataset A are daytime or evenly lit HDR-style wide angles emphasizing perfect symmetry, whereas Dataset B includes a mix of night shots, moody or over-processed HDR effects, and varying perspectives",
      "Office and desk scenes in Dataset A are minimal, modern, and immaculately arranged for a clean editorial look, while Dataset B desks are crowded with personal items, wires, and casual everyday clutter shot in home or informal office settings",
      "Dataset A consistently maintains high saturation, precise white balance, and polished post-processing; Dataset B features variable color casts, motion blur, visible noise, date stamps, and occasional watermarks reflecting unedited consumer photography",
      "Backgrounds in Dataset A are often deliberately chosen wood grain, slate, or fabric surfaces to complement the main subject, whereas Dataset B backgrounds are generic home walls, outdoor streets, or ad-hoc event venues with little aesthetic control",
      "Overall, Dataset A images share a deliberate, commercial stock aesthetic and uniform quality, while Dataset B images are heterogeneous user-generated content with diverse lighting, composition, and real-world authenticity"
    ],
    "unmet_v15_label_background": [
      "Dataset A consists of highly stylized, editorial\u2010grade or AI\u2010generated imagery with bright, even lighting and smooth textures; Dataset B is a heterogeneous mix of real\u2010world photographs showing natural light, mixed color casts, reflections, and sensor noise.",
      "Dataset A often uses neat overhead or square flat\u2010lay compositions\u2014especially for plated food\u2014minimizing background clutter; Dataset B uses varied aspect ratios and angles (eye level, slightly elevated, wide angle) that capture rich environmental context and incidental objects.",
      "Shields, coats of arms, and armor in Dataset A appear as conceptual or digitally rendered props with uniform finishes, whereas in Dataset B they are actual museum mounts or reenactor gear photographed front\u2010on with real\u2010world patina and mounting hardware visible.",
      "Office and desk scenes in Dataset A are staged and minimalistic, featuring clean surfaces and few items; in Dataset B desks are cluttered with personal belongings, cables, multiple monitors, papers, and other day\u2010to\u2010day detritus.",
      "Kimono and traditional garments in Dataset A are presented as fashion editorials or digital art shoots with crisp patterns and studio lighting; in Dataset B they appear on live wearers in candid, motion\u2010blurred street or museum environments under mixed indoor/outdoor light.",
      "Architectural images in Dataset A have a painterly, hyper\u2010real or 3D\u2010rendered aesthetic with perfect symmetry and texture; Dataset B architecture is photographed with realistic depth, variable dynamic range, weathering details, and surrounding context like foliage and signage.",
      "Food images in Dataset A are uniformly vibrant, plate\u2010centric still lifes on plain or minimal backdrops; Dataset B features in\u2010situ restaurant or home settings, with asymmetric plating, drinkware, condiments, human hands, menus, and ambient reflections visible.",
      "Dataset A images are free of watermarks, branding, or extraneous text; Dataset B sometimes include visible signage, watermarks, printed labels, or digitally stamped timestamps in the frame.",
      "Figures like knights or reenactors in Dataset A are usually posed or rendered in dramatic light as part of a fantasy composition; Dataset B shows actual people in authentic costumes or mannequins in museums, often captured candidly without dramatic staging.",
      "Overall, Dataset A maintains a consistent, curated artistic style across subjects, whereas Dataset B is a diverse collection of everyday photographs with natural variability in framing, lighting conditions, and environmental context."
    ],
    "unmet_v15_label_relation": [
      "Dataset B is composed largely of casual consumer snapshots with inconsistent framing and tilt, whereas Dataset A consists of uniformly well\u2010framed editorial-style images (often centered or top-down) with deliberate composition.",
      "Dataset B frequently employs harsh on-camera flash or mixed ambient light producing deep shadows and blown highlights, while Dataset A relies on soft, diffused natural or studio lighting that evenly illuminates subjects.",
      "Backgrounds in Dataset B are often cluttered real-world environments (messy desks, living spaces, event crowds), whereas Dataset A backgrounds are minimal, clean, or intentionally styled (restaurant tabletop, neutral studio backdrops).",
      "Food photos in Dataset B are informal, personal dining contexts with unbalanced plating and random props, while Dataset A showcases professionally styled dishes with colorful arrangements, cohesive tableware, and static placemat designs.",
      "Object shots in Dataset B (shields, weapons, museum pieces) are usually captured in situ against busy walls or in crowded settings, whereas Dataset A presents decorative objects (plates, ceramics) isolated on plain or lightly textured surfaces.",
      "Portraits of people in traditional garments in Dataset B are candid street or event snapshots with motion blur and background crowds, whereas Dataset A offers posed, art-directed three-quarter views set against softly blurred, uncluttered backgrounds.",
      "Architectural images in Dataset B vary wildly\u2014from moody nighttime exteriors with atmospheric fog to handheld decay-style interiors\u2014while Dataset A consistently features bright, high-dynamic-range church interiors or symmetrical exteriors emphasizing leading lines.",
      "Dataset B exhibits inconsistent depth-of-field (sometimes deep focus, sometimes accidental blur), while Dataset A uniformly uses a moderate shallow depth-of-field to isolate main subjects and gently blur surroundings.",
      "Compositions in Dataset B often place subjects off-center or partially cropped reflecting spontaneous capture; Dataset A compositions are deliberate, employing symmetry or centered vanishing points for strong visual emphasis.",
      "Overall, Dataset B is a grab-bag of user-generated images with varied lighting, camera quality, and settings, whereas Dataset A maintains a coherent editorial aesthetic with consistent color treatment, lighting uniformity, and professional styling."
    ]
  }
}