{
  "sims": {
    "unmet_v11_label_background": [
      "Both datasets often feature one or a few man-made objects (e.g., tools, furniture, bags) as the clear visual focus in each frame.",
      "Subjects are generally centrally framed or given prominent placement, with minimal camera tilt or artistic distortion.",
      "Directional lighting is used to emphasize surface textures and materials, creating highlights and shadows on the main subject.",
      "Backgrounds tend to be environmental but secondary\u2014workbenches, stages, museum interiors, or outdoor settings\u2014providing context without overwhelming the subject.",
      "Composition remains straightforward\u2014side-on, slightly angled, or eye-level viewpoints\u2014giving a documentary or cataloguing feel to the images.",
      "Depth of field is moderate: the primary object is kept in sharp focus while the background is softly blurred to maintain separation.",
      "Color palettes in both sets are largely natural or muted, reflecting the real-world appearance of the objects.",
      "Both sets include a mix of indoor and outdoor scenes, yet the lighting and composition keep the subject consistently isolated from its environment.",
      "There is often empty space around the subject, allowing contextual elements (tools, chairs, stage rigging) to appear peripherally without competing for attention.",
      "Some images in each dataset show humans handling or standing near the objects, adding usage context while keeping the object as the main visual element."
    ],
    "unmet_v11_label_only": [
      "Both datasets predominantly feature a single object or small group of related objects filling most of the frame, with minimal distracting elements around them",
      "Subjects are usually placed front-and-center on a flat surface\u2014tables, trays, floors, or platforms\u2014emphasizing a clear, isolated composition",
      "Backgrounds are kept simple or homogenous (plain walls, wooden floors, single-color backdrops, muted museum interiors) to keep focus on the subject",
      "Lighting is generally even and diffuse\u2014often ambient indoor or controlled soft lighting\u2014so that textures and colors of the object are clearly visible",
      "Many images share a similar overhead or slight-angle viewpoint, providing a clear, objective snapshot of the item rather than a dramatic perspective",
      "Surfaces under the objects frequently show wood grain, metal trays, or stone, giving a tactile context and consistent textural contrast across images",
      "Both include small sets of tools or utilitarian objects (hammers, mallets, measuring tapes, backpacks) photographed in a straightforward, documentary style",
      "Several images in each set feature furniture or decorative seating (chairs, thrones, benches) captured in an indoor architectural setting",
      "Occasional images of live events or performances (concert stages, audiences, performers) share the same centered-stage framing and stage lighting",
      "Overall the photos have an amateur or user-generated aesthetic\u2014slight tilt, casual cropping, visible environment edges\u2014rather than highly stylized editorial setups"
    ],
    "unmet_v11_label_relation": [
      "Both datasets feature close-up or mid-range shots of single objects (tools, trays, chairs, backpacks) isolated from busy surroundings.",
      "Both use simple, uncluttered backgrounds (plain walls, wooden tables, snow, grass) to draw attention to the main subject.",
      "Both include images of tools\u2014especially hammers, mallets, saws\u2014laid out or propped on textured surfaces.",
      "Both show seating objects (chairs, thrones) in studio-like or museum-style settings with controlled lighting.",
      "Both contain backpack and travel-gear images, sometimes worn by people and sometimes arranged on the ground or furniture.",
      "Both datasets include decorative trays, dishes, or plates displayed with food, flowers, or patterns on neutral backgrounds.",
      "Both rely on diffuse, even lighting to minimize harsh shadows and highlight fine surface details.",
      "Both employ eye-level or slightly elevated camera angles that center the subject within the frame.",
      "Both make use of natural textures (wood grain, stone, foliage, snow) as subtle backdrops for product-style shots.",
      "Both present a diverse set of object categories (tools, furniture, food, stage equipment) but maintain consistent, simplified composition and styling across images."
    ],
    "unmet_v15_label_only": [
      "Both datasets feature photographs of tools (hammers, saws, mallets) arranged against workbenches or wooden surfaces.",
      "Both include images of chairs, benches, or thrones captured indoors, often ornate or antique in style.",
      "Both contain multiple shots of backpacks in varied contexts\u2014worn by people, laid out on floors, or hanging against walls.",
      "Both showcase stage and concert scenes with musicians, lighting rigs, and portions of an audience or crowd.",
      "Both present still-life setups of trays or plates\u2014sometimes holding food, sometimes showing decorative patterns\u2014on flat surfaces.",
      "Both mix tight close-ups that focus on object details with wider views that establish a workshop, outdoor, or stage environment.",
      "Both use diverse lighting conditions, from natural daylight to studio flashes or colored stage spotlights, creating strong highlights and shadows.",
      "Both employ backgrounds that range from plain (studio or tiled floors) to context-rich (workshop walls, museum halls, outdoor settings).",
      "Both center the main subject in the frame while often including incidental clutter or props that fill out the scene.",
      "Both capture human hands or partial figures interacting with objects, emphasizing real-world use or handling."
    ],
    "unmet_v15_label_background": [
      "Both datasets feature workshop and tool\u2010oriented scenes showing hammers, saws, chisels, and other hand tools arranged on benches or floors with realistic, cluttered backgrounds.",
      "Both contain close-up images of single objects (tools, trays, chairs) framed centrally against textured or studio-like surfaces.",
      "Both include ornate chairs or thrones shot in interior architectural settings, often with decorative walls, columns, or museum\u2010style lighting.",
      "Both present stage performance photographs with musicians or actors under colored spotlights and audience crowds in the foreground or background.",
      "Both show people wearing backpacks in real-world urban or outdoor environments, captured in natural light and with environmental context visible behind them.",
      "Both contain food presentations on trays or plates, typically shot from an angled top-down perspective, with attention to plating and diffuse lighting.",
      "Both mix wide-angle interior scenes (palace halls, workshop interiors) with tight, object-centric compositions demonstrating a variety of focal lengths.",
      "Both datasets exhibit a range of lighting conditions\u2014from bright daylight outdoors to low-light, dramatic stage or museum spotlights\u2014highlighting texture and color variation.",
      "Both include images that emphasize real-world textures such as wood grain, rusted metal, fabric weave, and stone or marble surfaces without heavy post-processing.",
      "Both use backgrounds that either clutter the scene (busy workshop or stage rigging) or simplify it (plain floors, walls, or studio drop-cloths) to direct attention to the main subject."
    ],
    "unmet_v15_label_relation": [
      "Both datasets include close-up shots of tools and hardware items (e.g., hammers, mallets, saws) arranged neatly on textured surfaces",
      "Numerous images feature ornate furniture or decorative objects (chairs, thrones, trays) shot head-on in symmetrical, frontal compositions",
      "Each set contains staged concert or performance scenes captured from the audience or side-stage perspective, complete with lighting rigs and crowds",
      "Travel and backpack imagery is present in both\u2014with people carrying backpacks in urban plazas or natural settings",
      "Several images use overhead or top-down shots of objects laid out on surfaces (food trays, tool arrangements, decorative items)",
      "The main subject in most images is centrally composed and isolated against a simple or uncluttered background",
      "Backgrounds tend to be uniform or lightly textured (wood grain, plain walls, tiles) to emphasize the subject\u2019s shape and detail",
      "Lighting is often directional or natural, casting soft shadows that accentuate surface textures and form",
      "Many photographs employ a shallow depth of field, keeping the primary subject sharply in focus while gently blurring the background",
      "There is a balance of studio-style still life setups and real-world environmental shots, both indoor and outdoor"
    ]
  },
  "diffs_synth_from_real": {
    "unmet_v11_label_background": [
      "Dataset A consists of authentic photographs captured with hand-held or fixed-lens cameras, whereas Dataset B exhibits the hallmarks of synthetic or generative imagery (perfectly square crops, painterly textures, warped geometry).",
      "Images in Dataset A show modest, mostly incidental backgrounds with natural lighting and realistic color casts; Dataset B scenes are far more elaborately staged or fantastical, with dramatic directional or colored lighting and hyper-saturated palettes.",
      "In Dataset A the main subject is clearly isolated\u2014often shot at eye level or slight angle with moderate depth of field\u2014while in Dataset B the visual focus drifts amid busy, multi-element compositions that keep nearly everything in sharp or uniformly treated focus.",
      "Perspective in Dataset A remains consistent and documentary: side-on or straight-on viewpoints without distortion. In contrast, Dataset B frequently displays warped or impossible perspectives, as if viewed through an artistic lens or synthetic camera.",
      "Backgrounds in Dataset A serve as contextual but unobtrusive settings (workbenches, museum halls, concert stages). In Dataset B they often become part of the spectacle\u2014ranging from supermarket aisles to fairy-tale cottages to banquet tables full of ornate props.",
      "Color rendition in Dataset A is grounded\u2014skin tones and material hues look natural\u2014whereas Dataset B often pushes high-contrast highlights and shadows or employs unusual color grading, lending a cinematic or hyperreal feel.",
      "Most Dataset A images have spare framing with generous negative space around the primary object. Dataset B compositions are denser, with peripheral clutter and overlapping elements that compete for attention.",
      "Dataset A photographs show real-world capture artifacts\u2014sensor noise, motion blur, lens flare\u2014while Dataset B imagery tends toward either overly smooth, brush-stroke-like detail or uniform digital noise.",
      "Subjects in Dataset A (tools, trays, chairs, bags) present clear, unambiguous silhouettes. In Dataset B objects often interpenetrate, distort, or blend into their environments, creating hybrid shapes that defy simple identification.",
      "Human figures in Dataset A appear incidentally (a hand holding a hammer, a performer on stage) and remain secondary. In Dataset B people are more central to dramatic narratives\u2014shopping, feasting, exploring\u2014underscored by stylized poses and theatrical lighting."
    ],
    "unmet_v11_label_only": [
      "Dataset B images often exhibit synthetic or surreal artifacts and distortions\u2014unexpected shapes, melting objects, strange textures\u2014whereas dataset A consists entirely of clear, real\u2010world photographs",
      "Dataset B backgrounds are frequently complex and multi\u2010layered\u2014forest paths, cluttered workshops, concert stages, ornate architectures\u2014while dataset A uses plain or homogenous backdrops (simple walls, wooden floors, clean tables)",
      "In dataset B the lighting is dramatic and uneven (harsh spotlights, moody shadows, colored stage lights), whereas dataset A employs soft, diffuse, and even illumination to clearly reveal object details",
      "Dataset B compositions are dynamic and varied\u2014tilted horizons, low\u2010angle shots, wide environmental contexts, people interacting with objects\u2014whereas dataset A favors straightforward overhead or frontal, tightly framed object\u2010centric views",
      "Dataset B often includes human figures or hands actively engaged in a scene, giving contextual narrative, while dataset A typically isolates the object with no visible people or only hidden limbs",
      "The surfaces under objects in dataset B are highly varied (concrete sidewalks, grass, rocky ground, performance stages), but dataset A consistently places items on neutral flat surfaces like wooden or metal trays",
      "Dataset B images read like documentary or event photography with visible noise, casual cropping, and environmental clutter; dataset A resembles product or catalog photography with deliberate staging and minimal distractions",
      "Dataset B intermixes diverse subject matter\u2014food spreads, backpacks, tools, theatrical performances, ornate thrones\u2014often in the same set of images, whereas dataset A is more narrowly organized around a few object categories in isolation",
      "In dataset B perspectives are unpredictable\u2014close\u2010ups, extreme wide\u2010angles, partial occlusions\u2014while dataset A maintains consistent, complete object presentations from canonical viewpoints",
      "Dataset B embraces ambient, on\u2010location aesthetics (outdoor scenes, indoor events, workshops), whereas dataset A maintains a controlled studio\u2010like feel even when shot in real settings"
    ],
    "unmet_v11_label_relation": [
      "Dataset A images are predominantly genuine photographs taken in real\u2010world settings (workshops, homes, museums), whereas Dataset B images appear to be synthetic or heavily stylized renderings with subtle artifacts and surreal textures.",
      "Dataset A uses very simple, uncluttered backdrops (plain walls, studio floors, single\u2010tone surfaces), whereas Dataset B features more diverse and complex environments (outdoor foliage, palace hallways, waterfalls, staged event venues).",
      "Dataset A lighting is soft and even to minimize shadows and highlight object detail uniformly; Dataset B often employs dramatic or directional lighting with lens flares, stark contrasts, and painterly glow effects.",
      "Dataset A compositions isolate one or two objects clearly centered in the frame; Dataset B compositions sometimes include multiple scene elements, environmental context, or human figures integrated into the setting.",
      "Dataset A maintains consistent, true-to-life color palettes and material textures (wood grain, metal patina); Dataset B exhibits more saturated, pastel, or mismatched color schemes and occasionally unnatural surface finishes.",
      "In Dataset A camera angles are almost always eye-level or slightly overhead to present a neutral view; Dataset B experiments with unusual perspectives, low or high vantage points, and subtle warping.",
      "Dataset A backgrounds are minimally intrusive, serving mostly as neutral negative space; Dataset B backgrounds frequently contain elaborate architectural details, patterned d\u00e9cor, or fantasy-style scenery.",
      "Dataset A has minimal post-processing artifacts and looks \u201cphotographic\u201d; Dataset B shows hallmarks of generative synthesis or heavy digital manipulation, such as blending glitches, odd object contours, and painterly brushstroke-like textures.",
      "Dataset A objects are generally placed on real surfaces (tables, masonry, grass) with believable contact shadows; Dataset B sometimes floats objects, merges them with improbable backgrounds, or uses 3D-render quality surfaces.",
      "Dataset A focuses on practical, everyday tools and products with straightforward presentation; Dataset B spans stylized interior design mockups, surreal nature scenes, and theatrical stage setups that prioritize aesthetic flair over realism."
    ],
    "unmet_v15_label_only": [
      "Dataset A images are authentic camera photographs showing natural imperfections; dataset B images look synthetic or AI-generated with hyper-real textures and odd rendering artifacts.",
      "Dataset A features wildly varying backgrounds\u2014from palace interiors and tiled floors to open-air crowds\u2014often chosen to be minimal or contextually meaningful; dataset B backgrounds are dominated by wooden planks, forest debris, rock rubble and other grungy, texture-heavy surfaces.",
      "Dataset A compositions follow classic photographic conventions (rule of thirds, centered subjects on plain or softly detailed backgrounds); dataset B compositions are more symmetrical, tightly centered, and cluttered with multiple props or busy textures.",
      "Dataset A lighting is balanced\u2014natural daylight or controlled studio/flash light\u2014yielding even exposures; dataset B lighting is dramatic and directional with exaggerated contrast, heavy shadows and pronounced specular highlights.",
      "Dataset A frequently includes clear human figures or candid interactions holding or using objects; dataset B rarely shows full recognizable faces\u2014humans appear as odd partial figures, mannequins or are omitted entirely.",
      "Dataset A colors are true-to-life with accurate white balance; dataset B uses muted browns, sepia tones or surreal color casts that emphasize material textures over realism.",
      "Dataset A tool photographs show neatly arranged implements in familiar workshop or toolbox contexts; dataset B tool close-ups are macro-style shots on weathered wooden floors or surfaces with scattered debris.",
      "Dataset A still-life shots of trays and plates typically display food or decorative patterns on domestic surfaces; dataset B tray/plate images are mostly empty or display carved wooden shapes against bare textures.",
      "Dataset A stage and concert scenes capture live performers, crowd interaction and dynamic movement; dataset B stage images are sparse, often empty, focusing on lighting rigs and set structures rather than audiences.",
      "Dataset A backgrounds and surfaces vary widely and serve to contextualize the scene; dataset B consistently reuses a narrow set of heavily textured backgrounds (wood, stone, logs) creating a stylistic monotony."
    ],
    "unmet_v15_label_background": [
      "Dataset A consists of authentic, in-camera photographs of real-world scenes and objects, while Dataset B appears to contain synthetic or generative imagery exhibiting AI-like artifacts and surreal combinations.",
      "Images in Dataset A maintain natural lighting and realistic shadows consistent with their environments; Dataset B often shows uneven, painterly, or otherworldly lighting and color casts that break physical plausibility.",
      "Backgrounds in Dataset A are captured faithfully \u2013 either cluttered workshops, outdoor contexts, or museum interiors \u2013 whereas Dataset B backgrounds frequently blur, warp, or blend in unnatural ways behind the main subject.",
      "Dataset A follows classical photographic composition (centered subjects, rule of thirds, natural perspectives), while Dataset B features off-kilter framings, skewed vanishing points, and unexpected perspective distortions.",
      "The color palettes in Dataset A remain true to real materials (wood grains, metal finishes, fabrics), but Dataset B uses oversaturated, muted, or mismatched hues that give a stylized or dreamlike quality.",
      "Dataset A images are crisp with distinct edges and textures, whereas Dataset B often shows smudged details, painterly strokes, or hairy kind of artifacting indicative of rendered or blended visuals.",
      "Scenes in Dataset A are physically plausible (a tray of food under diffuse daylight, tools in a workshop under overhead lighting), while Dataset B contains fantastical architectures or ice-castle interiors that seem impossible to photograph.",
      "Depth-of-field and focus in Dataset A behave like real optics\u2014foreground objects, midground, and background plane separation\u2014Dataset B frequently merges depths unpredictably or shows uniform sharpness/blurriness across an image.",
      "People and objects in Dataset A look naturally integrated into their environments with coherent cast shadows and reflections; in Dataset B, subjects sometimes float, distort, or appear pasted without consistent lighting or scale.",
      "Overall, Dataset A exhibits standard photographic artifacts (lens flares, grain, motion blur) in service of realism, whereas Dataset B prioritizes stylized, generative distortions that break real-world photographic conventions."
    ],
    "unmet_v15_label_relation": [
      "Dataset A consists primarily of candid, amateur snapshots taken with point-and-shoot or phone cameras\u2014often showing harsh flash lighting, motion blur, noise, and uneven white balance\u2014whereas Dataset B images appear professionally lit or digitally rendered, with smooth, controlled directional lighting and no obvious sensor artifacts.",
      "Backgrounds in Dataset A tend to be mundane real-world surfaces (plain tile floors, random walls, crowd scenes) with little styling, while Dataset B employs richly textured or stylized backdrops (ornate interiors, atmospheric outdoor settings, seamless gradients) that enhance the subject.",
      "Compositions in Dataset A are frequently off-center, tilted, or imperfectly framed, reflecting spur-of-the-moment shooting, whereas Dataset B almost always features centrally composed subjects with precise, symmetrical arrangements.",
      "Objects in Dataset A display natural wear, dirt, and real-world imperfections, whereas Dataset B\u2019s subjects appear idealized or artificially smoothed\u2014sometimes exhibiting surreal color shifts or subtly impossible geometry indicative of CGI or heavy post-processing.",
      "Dataset A exhibits a variety of depths of field and focus inconsistencies\u2014ranging from deep focus crowds to blurred tool close-ups\u2014while Dataset B uniformly uses a shallow depth of field to isolate sharply focused subjects against softly blurred backgrounds.",
      "Color in Dataset A is realistic but often muted, with flash-induced highlights and shadow clipping, whereas Dataset B\u2019s color palette is vivid and high-contrast, with carefully graded saturation and dynamic range.",
      "People and events in Dataset A are spontaneous concert and street scenes full of movement blur and ambient lighting, while Dataset B contains far fewer live people shots and instead emphasizes static still-life or staged environmental images.",
      "Tool and object layouts in Dataset A are often simple overhead or side-by-side arrangements on flat surfaces, but in Dataset B the angles vary dramatically\u2014low-angle perspectives, three-quarter views, and creative vantage points that imply artistic staging.",
      "Dataset A images reveal real photographic artifacts (lens flares, chromatic aberration, noise, vignetting), whereas Dataset B appears free of such lens defects, occasionally showing artifacts of digital synthesis (slightly warped edges, uncanny smoothness).",
      "Overall, Dataset A is an eclectic collection of real-world snapshots with inconsistent styling and lighting, whereas Dataset B presents a cohesive, polished visual style\u2014suggesting either professional studio work or AI-generated imagery with uniform aesthetic choices."
    ]
  },
  "diffs_real_from_synth": {
    "unmet_v11_label_background": [
      "Dataset A images look largely synthetic or CGI-style, with odd geometry, painterly textures, and vivid/unrealistic color shifts, whereas Dataset B images are authentic photographs with natural lighting, color and textures.",
      "Dataset A often presents every element in sharp focus with uniform illumination, whereas Dataset B exhibits real photographic depth-of-field (foreground sharp, background softly blurred) and directional or ambient lighting typical of phone or DSLR captures.",
      "Dataset A scenes are frequently shot from unusual angles (overhead, extreme tilt, distorted perspective) giving an artistic or surreal composition, while Dataset B images stick to straightforward eye-level, slightly angled or side-on viewpoints.",
      "Backgrounds in Dataset A are busy and cluttered\u2014workshop benches, abstract rooms or fantasy interiors\u2014competing visually with the subject, whereas Dataset B backgrounds are simplified or naturally blurred to keep the object or person isolated.",
      "Dataset A subjects span fanciful or impossible configurations (e.g., melted furniture, fantasy thrones, unreal tool collisions), but Dataset B shows everyday real-world objects (hammers, backpacks, musical stages) in plausible contexts.",
      "Dataset A contains few if any living subjects or crowds; people, if present, are rendered in an uncanny or stylized way. In contrast, Dataset B often includes real people at concerts, working with tools, or carrying backpacks that anchor the scene in human activity.",
      "Lighting in Dataset A tends toward overly even, shadow-free studio style or dramatic fantasy glow, whereas Dataset B features the uneven, contrasty lighting of live events, outdoor sunshine or indoor ambient illumination.",
      "Color palettes in Dataset A are sometimes pastel, hyper-saturated, or feature conflicting hues (e.g., neon-green foliage, purple shadows), while Dataset B sticks to realistic skin tones, metal sheens, wood grains and stage light colors.",
      "Dataset A often feels like a conceptual still life\u2014objects arranged for effect with negative space used arbitrarily\u2014whereas Dataset B feels documentary, capturing everyday scenes or live performances with contextual elements around the main subject.",
      "Overall, Dataset A images bear hallmarks of generative or studio CG work (artifacts, surreal detail, inconsistent physics), while Dataset B are cohesive real-world photographs with typical camera noise, lens blur and human-scale composition."
    ],
    "unmet_v11_label_only": [
      "Dataset A images are predominantly clean, studio-style product or still-life shots with a single subject isolated against a plain or minimally textured background, whereas dataset B contains candid, on-location photographs with cluttered, varied real-world environments (concerts, museums, streets).",
      "Dataset A uses highly controlled compositions\u2014objects centered, often flat-laid or straight-on\u2014with consistent framing and perspective; dataset B exhibits eclectic, off-axis or tilted viewpoints and unpredictable framing reflecting amateur or documentary shooting.",
      "Lighting in dataset A is uniform, diffuse, and studio-like, minimizing shadows and highlighting detail, while dataset B displays mixed ambient and harsh stage or natural lighting, producing dramatic shadows, lens flares, and uneven illumination.",
      "Backgrounds in dataset A are deliberately neutral (white backdrops, plain floors, stylized props) to focus attention on the item; dataset B backgrounds range from crowded concert stages and audience areas to ornate architectural interiors and street scenes with no attempt at isolation.",
      "People are largely absent in dataset A\u2014when present, they\u2019re mannequins or backs of heads used for scale\u2014whereas dataset B frequently includes performers, audience members, and passersby integrated into the scene.",
      "Dataset A images exhibit no extraneous artifacts\u2014crisp focus, clean edges, no watermarks\u2014while dataset B shows signs of consumer photography such as watermarks, timestamps, noise, motion blur, and uneven focus.",
      "Color rendering in dataset A is consistent and often stylized or CGI-like, suggesting post-processing or synthetic generation; dataset B shows authentic photographic color variations, white balance shifts, and real-world imperfections.",
      "Dataset A seldom contains more than one or two related items; dataset B often depicts multiple unrelated objects or equipment in a single frame\u2014tool benches, stage rigs, architectural details, and seating\u2014creating visual complexity.",
      "Viewpoints in dataset A are almost exclusively top-down, straight-on, or slight angle for clarity and consistency; dataset B ranges from low angles beneath stages to side-on shots of events, providing environmental context at the expense of uniformity.",
      "Dataset A\u2019s subject matter is narrowly grouped into product-type categories (tools, backpacks, meals), each with a unified photographic treatment; dataset B spans diverse domains (live music, thrones, street scenes, museum artifacts) with no single stylistic thread."
    ],
    "unmet_v11_label_relation": [
      "Dataset B images are candid, in-the-wild photographs with variable lighting conditions and visible noise, whereas dataset A images have controlled, studio-like illumination and minimal artifacting.",
      "Dataset B backgrounds tend to be simple, functional surfaces (wood tables, snow, grass) used purely to isolate subjects, while dataset A backgrounds are elaborate, artistic or digitally generated environments.",
      "Dataset B compositions almost always center a single object at eye-level, whereas dataset A employs diverse perspectives (top-down, oblique, wide-angle) and more creative staging.",
      "Dataset B photographs exhibit real-world imperfections (harsh shadows, motion blur, uneven color casts), in contrast to dataset A\u2019s polished visuals with smooth color grading and post-processing.",
      "Dataset B subjects are placed in natural, uncurated positions (tools loosely scattered, chairs in museum settings), unlike dataset A\u2019s deliberately arranged or sometimes surreal object placements.",
      "Dataset B typically shows uniform focus across the entire scene, while dataset A makes extensive use of shallow depth-of-field and selective focus for visual emphasis.",
      "Dataset B color palettes mirror heterogeneous ambient light sources and real-life hues, whereas dataset A maintains cohesive, harmonious palettes that convey an artistic style.",
      "Dataset B captures documentary-style snapshots of real events, tools, and environments; dataset A images resemble conceptual product shots or fine art renders rather than spontaneous photography.",
      "Dataset B is largely handheld and spontaneous\u2014resulting in perspective quirks and framing variance\u2014whereas dataset A uses precise, tripod-like compositions and consistent framing rules.",
      "Dataset B conveys a utilitarian, functional aesthetic focused on real tool use and lived spaces, while dataset A emphasizes stylization, aesthetic presentation, and creative abstraction."
    ],
    "unmet_v15_label_only": [
      "Dataset A images show single objects isolated against clean, neutral or wooden-textured backgrounds, whereas dataset B frames objects within cluttered real\u2010world scenes full of incidental props and context.",
      "Images in dataset A follow a regular, central composition and consistent camera angle, while dataset B uses varied perspectives, off\u2010center framing, and dynamic viewpoints.",
      "Lighting in dataset A is even, diffuse, and studio-style, whereas dataset B exhibits a mix of natural light, harsh stage spotlights, colored concert lights, and strong shadows.",
      "Dataset A almost entirely excludes people, focusing on products or tools alone; dataset B frequently includes human hands, partial figures, full people, crowds at concerts, or people using backpacks.",
      "Backgrounds in dataset A are controlled and uniform (plain walls, wood planks, studio floors), while dataset B backgrounds range widely across museums, outdoor festivals, urban streets, and workshop interiors.",
      "Dataset A looks like product or editorial stock photography (or high\u2010quality renders), whereas dataset B feels like casual snapshot photography from varied amateur sources.",
      "Objects in dataset A are typically laid flat or shown in simple profile shots; in dataset B objects appear at a variety of angles, sometimes partially occluded, in use, or integrated into larger scenes.",
      "Dataset A features isolated still-life arrangements of tools, backpacks, chairs or trays; dataset B blends these same objects into lifestyle, event, or environmental contexts (live shows, travel, museum visits).",
      "Color and tonal palettes in dataset A are coherent and balanced, with minimal colorcasts; dataset B images show inconsistent white balance, over-/underexposure, and vivid or muted color shifts depending on setting.",
      "Dataset A predominantly uses tight, object-focused close-ups, while dataset B mixes tight close-ups with wide-angle shots that include audiences, stage setups, or full scene backgrounds."
    ],
    "unmet_v15_label_background": [
      "Dataset A is almost entirely comprised of workshop or fabrication scenes\u2014benches, organized tool racks, and craftsmen at work\u2014while Dataset B spans a much broader range of real-world contexts including concert stages, palace interiors, outdoor landscapes, food presentations, and urban street scenes.",
      "Images in Dataset A generally feature evenly lit indoor environments with muted, neutral color palettes, whereas Dataset B contains highly varied lighting\u2014from dramatic colored spotlights at concerts to direct sunlight on beaches and soft ambient museum illumination.",
      "Dataset A compositions tend to be mid-range shots focusing on tools or their immediate surroundings with minimal scene depth, while Dataset B alternates between wide-angle architectural interiors, dynamic crowd scenes, close-ups of plated food, and environmental portraits with significant depth cues.",
      "Backgrounds in Dataset A are consistently workshop-oriented (concrete walls, wood benches, shelving full of tools), whereas Dataset B backgrounds can be busy crowds, ornate decorative walls, forested paths, stage rigging, or plain studio backdrops, reflecting in-the-wild photography.",
      "Dataset A shows mostly static, posed artisan tasks with clear tool demonstrations, but Dataset B often captures candid or event-driven moments\u2014musicians performing, travelers walking with packs, shoppers browsing stalls\u2014injecting narrative context.",
      "Tool imagery in Dataset A is cleanly arranged and often centrally framed, whereas Dataset B\u2019s tool appearances (e.g., hammers) are interspersed among eclectic subjects and usually appear as part of spontaneously lit, cluttered real scenes.",
      "Dataset A\u2019s visual style is uniform, with consistent focus and minimal post-processing artifacts; Dataset B exhibits a range of photographic aesthetics including lens flares, motion blur, watermarks, shallow depth-of-field, and various levels of digital noise.",
      "The color saturation in Dataset A is subdued\u2014earth tones and grays dominate\u2014whereas Dataset B frequently displays vivid, high-contrast colors (bright stage gels, richly hued foods, ornamental gold in thrones).",
      "Dataset A lacks human crowds or large-scale environments, focusing instead on individual craftspeople or tool layouts; Dataset B regularly includes groups of people (concert audiences, museum visitors, travelers) and expansive environments.",
      "Overall, Dataset A\u2019s images feel like instructional studio shots or controlled workshop documentation, while Dataset B reads as a heterogeneous collection of candid, editorial-style photos captured across diverse real-world scenarios."
    ],
    "unmet_v15_label_relation": [
      "Dataset A images have highly controlled, even studio-style lighting, whereas dataset B shows a wide range of real-world lighting including harsh stage lights, backlighting, low-light/night scenes, and strong natural shadows.",
      "Dataset A often isolates objects against plain or uniform backgrounds, while dataset B frequently presents subjects in busy, cluttered, or environmental contexts like concert crowds, street scenes, and museum interiors.",
      "Dataset A compositions tend to be front-on or top-down with perfectly centered framing; dataset B employs varied viewpoints and angles, including side-stage, audience perspectives, and off-center framing.",
      "Dataset A uses neutral, minimal, often single-color backdrops; dataset B includes complex, textured backgrounds filled with people, architectural details, and equipment.",
      "Dataset A objects appear in a highly polished or stylized (sometimes synthetic) aesthetic; dataset B captures real-world imperfections\u2014digital noise, lens flare, motion blur, and organic texture.",
      "Dataset A scenes are static and curated like product shots or still lifes; dataset B images are dynamic and documentary-style, featuring live events, crowds in motion, and spontaneous action.",
      "Dataset A color palettes are generally muted, pastel, or very uniform; dataset B shows vivid, mixed, and saturated color schemes\u2014colored stage lighting, neon signs, varied ambient hues.",
      "Dataset A maintains uniformly sharp focus and high resolution on every subject; dataset B exhibits handheld camera artifacts, varying depth of field, focus shifts, and occasional blur.",
      "Dataset A seldom includes people or shows them as secondary\u2014hands only or illustrative figures; dataset B prominently features people (performers, audiences, travelers) as principal subjects.",
      "Dataset A feels like a controlled studio or CGI render environment; dataset B conveys a candid snapshot quality, documenting real situations and live experiences."
    ]
  }
}