Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments
This work addresses a domain-specific problem for researchers and practitioners in computer vision and waste management, but it is incremental as it focuses on benchmarking rather than proposing new methods.
The paper tackles the problem of evaluating Vision Large Language Models (VLLMs) in cluttered environments with deformed objects by introducing a novel waste classification dataset and an evaluation approach, finding that VLLMs need improvements for robustness in such conditions.
Recent advancements in Large Language Models (LLMs) have paved the way for Vision Large Language Models (VLLMs) capable of performing 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 objects. 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.