@inproceedings{chandhok-etal-2025-response,
title = "Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities",
author = "Chandhok, Shivam and
Fan, Wan-Cyuan and
Shwartz, Vered and
Balasubramanian, Vineeth N. and
Sigal, Leonid",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1241/",
doi = "10.18653/v1/2025.acl-long.1241",
pages = "25530--25545",
ISBN = "979-8-89176-251-0",
abstract = "Vision-language Models (VLMs) have emerged as general-purpose tools for addressing a variety of complex computer vision problems. Such models have been shown to be highly capable, but, at the same time, lacking some basic visual understanding skills. In this paper, we set out to understand the limitations of SoTA VLMs on fundamental visual tasks (object classification, spatial understanding, and ability to delineate individual object instances through counting), by constructing a series of tests that probe which components of design, specifically, may be lacking. Importantly, we go significantly beyond the current benchmarks, which simply measure the final performance of VLM response, by also comparing and contrasting it to the performance of probes trained directly on features obtained from the visual encoder, intermediate vision-language projection and LLM-decoder output. In doing so, we uncover shortcomings in VLMs and make a number of important observations about their capabilities, robustness and how they process visual information. We hope our insights will guide progress in further improving VLMs."
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<abstract>Vision-language Models (VLMs) have emerged as general-purpose tools for addressing a variety of complex computer vision problems. Such models have been shown to be highly capable, but, at the same time, lacking some basic visual understanding skills. In this paper, we set out to understand the limitations of SoTA VLMs on fundamental visual tasks (object classification, spatial understanding, and ability to delineate individual object instances through counting), by constructing a series of tests that probe which components of design, specifically, may be lacking. Importantly, we go significantly beyond the current benchmarks, which simply measure the final performance of VLM response, by also comparing and contrasting it to the performance of probes trained directly on features obtained from the visual encoder, intermediate vision-language projection and LLM-decoder output. In doing so, we uncover shortcomings in VLMs and make a number of important observations about their capabilities, robustness and how they process visual information. We hope our insights will guide progress in further improving VLMs.</abstract>
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%0 Conference Proceedings
%T Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities
%A Chandhok, Shivam
%A Fan, Wan-Cyuan
%A Shwartz, Vered
%A Balasubramanian, Vineeth N.
%A Sigal, Leonid
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F chandhok-etal-2025-response
%X Vision-language Models (VLMs) have emerged as general-purpose tools for addressing a variety of complex computer vision problems. Such models have been shown to be highly capable, but, at the same time, lacking some basic visual understanding skills. In this paper, we set out to understand the limitations of SoTA VLMs on fundamental visual tasks (object classification, spatial understanding, and ability to delineate individual object instances through counting), by constructing a series of tests that probe which components of design, specifically, may be lacking. Importantly, we go significantly beyond the current benchmarks, which simply measure the final performance of VLM response, by also comparing and contrasting it to the performance of probes trained directly on features obtained from the visual encoder, intermediate vision-language projection and LLM-decoder output. In doing so, we uncover shortcomings in VLMs and make a number of important observations about their capabilities, robustness and how they process visual information. We hope our insights will guide progress in further improving VLMs.
%R 10.18653/v1/2025.acl-long.1241
%U https://aclanthology.org/2025.acl-long.1241/
%U https://doi.org/10.18653/v1/2025.acl-long.1241
%P 25530-25545
Markdown (Informal)
[Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities](https://aclanthology.org/2025.acl-long.1241/) (Chandhok et al., ACL 2025)
ACL