标题： Waste-Bench：在杂乱环境中评估VLLM的综合基准 显示英文标题

标题： Waste-Bench: A Comprehensive Benchmark for Evaluating VLLMs in Cluttered Environments

摘要： 近年来，大型语言模型（LLMs）的进展为视觉大型语言模型（VLLMs）铺平了道路，这些模型能够执行各种视觉理解任务。虽然LLMs在标准自然图像上表现出色，但它们在杂乱数据集中的能力尚未得到充分探索，这些数据集具有复杂的环境和变形的物体形状。在本工作中，我们引入了一个专门设计用于现实场景中垃圾分类的新数据集，该数据集的特点是环境复杂且物体形状变形。除了这个数据集，我们还提出了一种深入的评估方法，以严格评估VLLMs的鲁棒性和准确性。引入的数据集和全面的分析为VLLMs在挑战性条件下的性能提供了有价值的见解。我们的研究结果突显了进一步提升VLLM鲁棒性的关键需求，以在复杂环境中表现更好。我们实验的数据集和代码将公开提供。 显示英文摘要

摘要： 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.