Are Vision Language Models Texture or Shape Biased and Can We Steer Them?

• URL: http://arxiv.org/abs/2403.09193v1
• Date: Thu, 14 Mar 2024 09:07:14 GMT
• Title: Are Vision Language Models Texture or Shape Biased and Can We Steer Them?
• Authors: Paul Gavrikov, Jovita Lukasik, Steffen Jung, Robert Geirhos, Bianca Lamm, Muhammad Jehanzeb Mirza, Margret Keuper, Janis Keuper,
• Abstract summary: We study the texture vs. shape bias in vision language models (VLMs) We find that VLMs are often more shape-biased than their vision encoders, indicating that visual biases are modulated to some extent through text. For instance, we are able to steer shape bias from as low as 49% to as high as 72% through prompting alone.
• Score: 29.837399598519557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision language models (VLMs) have drastically changed the computer vision model landscape in only a few years, opening an exciting array of new applications from zero-shot image classification, over to image captioning, and visual question answering. Unlike pure vision models, they offer an intuitive way to access visual content through language prompting. The wide applicability of such models encourages us to ask whether they also align with human vision - specifically, how far they adopt human-induced visual biases through multimodal fusion, or whether they simply inherit biases from pure vision models. One important visual bias is the texture vs. shape bias, or the dominance of local over global information. In this paper, we study this bias in a wide range of popular VLMs. Interestingly, we find that VLMs are often more shape-biased than their vision encoders, indicating that visual biases are modulated to some extent through text in multimodal models. If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments. For instance, we are able to steer shape bias from as low as 49% to as high as 72% through prompting alone. For now, the strong human bias towards shape (96%) remains out of reach for all tested VLMs.

Related papers

• $\texttt{ModSCAN}$: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities [30.960327354387054]
  Large vision-language models (LVLMs) have been rapidly developed and widely used in various fields, but the (potential) stereotypical bias in the model is largely unexplored. We present a pioneering measurement framework, $textttModSCAN$, to $underlineSCAN$ the stereotypical bias within LVLMs.
  arXiv  Detail & Related papers  (2024-10-09T15:07:05Z)
• BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models [20.697019266074747]
  Vision language models (VLMs) perceive the world through a combination of a visual encoder and a large language model (LLM) Recent studies show that VLMs are vulnerable to hallucination. We introduce new metrics: True Understanding (TU), IGnorance (IG), StuBbornness (SB), and InDecision (ID)
  arXiv  Detail & Related papers  (2024-07-18T12:11:12Z)
• See It from My Perspective: Diagnosing the Western Cultural Bias of Large Vision-Language Models in Image Understanding [78.88461026069862]
  Vision-language models (VLMs) can respond to queries about images in many languages. We present a novel investigation that demonstrates and localizes Western bias in image understanding.
  arXiv  Detail & Related papers  (2024-06-17T15:49:51Z)
• An Introduction to Vision-Language Modeling [128.6223984157515]
  The vision-language model (VLM) applications will significantly impact our relationship with technology. We introduce what VLMs are, how they work, and how to train them. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
  arXiv  Detail & Related papers  (2024-05-27T15:01:23Z)
• Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs [50.77984109941538]
  Our research reveals that the visual capabilities in recent multimodal LLMs still exhibit systematic shortcomings. We identify ''CLIP-blind pairs'' - images that CLIP perceives as similar despite their clear visual differences. We evaluate various CLIP-based vision-and-language models and found a notable correlation between visual patterns that challenge CLIP models and those problematic for multimodal LLMs.
  arXiv  Detail & Related papers  (2024-01-11T18:58:36Z)
• Revisiting the Role of Language Priors in Vision-Language Models [90.0317841097143]
  Vision-language models (VLMs) are applied to a variety of visual understanding tasks in a zero-shot fashion, without any fine-tuning. We study $textitgenerative VLMs$ that are trained for next-word generation given an image. We explore their zero-shot performance on the illustrative task of image-text retrieval across 8 popular vision-language benchmarks.
  arXiv  Detail & Related papers  (2023-06-02T19:19:43Z)
• Is Multimodal Vision Supervision Beneficial to Language? [2.216702991322677]
  Vision (image and video) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks. We compare the performance of language representations of stand-alone text encoders of these models to the language representations of text encoders learnt through vision supervision.
  arXiv  Detail & Related papers  (2023-02-10T02:22:44Z)
• Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models [57.08925810659545]
  We conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models. Our empirical observations suggest that vision-and-language models are better at label prediction tasks. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.
  arXiv  Detail & Related papers  (2022-12-01T05:00:18Z)
• Visually-Augmented Language Modeling [137.36789885105642]
  We propose a novel pre-training framework, named VaLM, to Visually-augment text tokens with retrieved relevant images for Language Modeling. With the visually-augmented context, VaLM uses a visual knowledge fusion layer to enable multimodal grounded language modeling. We evaluate the proposed model on various multimodal commonsense reasoning tasks, which require visual information to excel.
  arXiv  Detail & Related papers  (2022-05-20T13:41:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.