Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments
Abstract: Recent advancements in Large Language Models (LLMs) have paved the way for Vision Large Language Models (VLLMs) capable of erforming a wide range of visual understanding tasks. While LLMs have demonstrated impressive performance on standard natural images, their capabilities have not been thoroughly explored in cluttered datasets where there is complex environment having deformed shaped bjects. In this work, we introduce a novel dataset specifically designed for waste classification in real-world scenarios, characterized by complex environments and deformed shaped objects. Along with this dataset, we present an in-depth evaluation approach to rigorously assess the robustness and accuracy of VLLMs.
The introduced dataset and comprehensive analysis provide valuable insights into the performance of VLLMs under challenging conditions. Our findings highlight the critical need for further advancements in VLLM's robustness to perform better in complex environments. The dataset and code for our experiments will be made publicly available.
Paper Type: Long
Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond
Research Area Keywords: image text matching; cross-modal content generation; vision question answering; cross-modal information extraction
Contribution Types: Model analysis & interpretability, Data resources
Languages Studied: English
Submission Number: 3204
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