STATUS Bench: A Rigorous Benchmark for Evaluating Object State Understanding in Vision-Language Models

TL;DR

This paper introduces STATUS Bench, a comprehensive benchmark and dataset for evaluating and improving vision-language models' ability to recognize and understand subtle object states and their changes across diverse scenarios.

Contribution

It presents the first rigorous benchmark and large-scale dataset for object state understanding in vision-language models, along with an evaluation scheme for multiple related tasks.

Findings

Current VLMs perform at chance level on object state tasks.

Fine-tuning on the new dataset improves model performance significantly.

STATUS Bench reveals the need for specialized training to understand object states.

Abstract

Object state recognition aims to identify the specific condition of objects, such as their positional states (e.g., open or closed) and functional states (e.g., on or off). While recent Vision-Language Models (VLMs) are capable of performing a variety of multimodal tasks, it remains unclear how precisely they can identify object states. To alleviate this issue, we introduce the STAte and Transition UnderStanding Benchmark (STATUS Bench), the first benchmark for rigorously evaluating the ability of VLMs to understand subtle variations in object states in diverse situations. Specifically, STATUS Bench introduces a novel evaluation scheme that requires VLMs to perform three tasks simultaneously: object state identification (OSI), image retrieval (IR), and state change identification (SCI). These tasks are defined over our fully hand-crafted dataset involving image pairs, their corresponding object state descriptions and state change descriptions. Furthermore, we introduce a large-scale training dataset, namely STATUS Train, which consists of 13 million semi-automatically created descriptions. This dataset serves as the largest resource to facilitate further research in this area. In our experiments, we demonstrate that STATUS Bench enables rigorous consistency evaluation and reveal that current state-of-the-art VLMs still significantly struggle to capture subtle object state distinctions. Surprisingly, under the proposed rigorous evaluation scheme, most open-weight VLMs exhibited chance-level zero-shot performance. After fine-tuning on STATUS Train, Qwen2.5-VL achieved performance comparable to Gemini 2.0 Flash. These findings underscore the necessity of STATUS Bench and Train for advancing object state recognition in VLM research.