Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models

• URL: http://arxiv.org/abs/2410.03176v1
• Date: Fri, 4 Oct 2024 06:24:49 GMT
• Title: Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
• Authors: Yufang Liu, Tao Ji, Changzhi Sun, Yuanbin Wu, Aimin Zhou,
• Abstract summary: We present an in-depth investigation into the object hallucination problem specifically within the CLIP model. We unveil that even in isolation, the CLIP model is prone to object hallucinations, suggesting that the hallucination problem is not solely due to the interaction between vision and language modalities. We show the the enhanced model can be employed as a visual encoder, effectively alleviating the object hallucination issue in LVLMs.
• Score: 22.42712853647949
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models. However, there is no clear conclusion as to which part of the model these hallucinations originate from. In this paper, we present an in-depth investigation into the object hallucination problem specifically within the CLIP model, which serves as the backbone for many state-of-the-art vision-language systems. We unveil that even in isolation, the CLIP model is prone to object hallucinations, suggesting that the hallucination problem is not solely due to the interaction between vision and language modalities. To address this, we propose a counterfactual data augmentation method by creating negative samples with a variety of hallucination issues. We demonstrate that our method can effectively mitigate object hallucinations for CLIP model, and we show the the enhanced model can be employed as a visual encoder, effectively alleviating the object hallucination issue in LVLMs.

Related papers

• From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models [15.401221354325672]
  Hallucinations in large vision models (LVLMs) are a significant challenge, i.e., generating objects that are not presented in the visual input. Recent studies often attribute hallucinations to a lack of understanding of visual input, yet ignore a more fundamental issue: the model's inability to extract or decouple visual features. In this paper, we revisit the hallucinations in LVLMs from an architectural perspective, investigating whether the primary cause lies in the visual encoder (feature extraction) or the modal alignment module (feature decoupling)
  arXiv  Detail & Related papers  (2024-10-09T11:46:32Z)
• VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models [59.05674402770661]
  This work introduces VideoHallucer, the first comprehensive benchmark for hallucination detection in large video-language models (LVLMs) VideoHallucer categorizes hallucinations into two main types: intrinsic and extrinsic, offering further subcategories for detailed analysis.
  arXiv  Detail & Related papers  (2024-06-24T06:21:59Z)
• Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models? [53.89380284760555]
  Large vision-language models (LVLMs) produce captions that mention concepts that cannot be found in the image. These hallucinations erode the trustworthiness of LVLMs and are arguably among the main obstacles to their ubiquitous adoption. Recent work suggests that addition of grounding objectives -- those that explicitly align image regions or objects to text spans -- reduces the amount of LVLM hallucination.
  arXiv  Detail & Related papers  (2024-06-20T16:56:11Z)
• Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [48.065569871444275]
  We propose detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback. We generate a small-size hallucination annotation dataset by proprietary models. Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model.
  arXiv  Detail & Related papers  (2024-04-22T14:46:10Z)
• Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models [36.98580310654515]
  We introduce a refined taxonomy of hallucinations, featuring a new category: Event Hallucination. We then utilize advanced LLMs to generate and filter fine grained hallucinatory data consisting of various types of hallucinations. The proposed benchmark distinctively assesses LVLMs ability to tackle a broad spectrum of hallucinations.
  arXiv  Detail & Related papers  (2024-02-24T05:14:52Z)
• A Survey on Hallucination in Large Vision-Language Models [18.540878498840435]
  Large Vision-Language Models (LVLMs) have attracted growing attention within the AI landscape for its practical implementation potential. However, hallucination'', or more specifically, the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs. We dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation.
  arXiv  Detail & Related papers  (2024-02-01T00:33:21Z)
• AutoHall: Automated Hallucination Dataset Generation for Large Language Models [56.92068213969036]
  This paper introduces a method for automatically constructing model-specific hallucination datasets based on existing fact-checking datasets called AutoHall. We also propose a zero-resource and black-box hallucination detection method based on self-contradiction.
  arXiv  Detail & Related papers  (2023-09-30T05:20:02Z)
• Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
  This work presents the first systematic study on object hallucination of large vision-language models (LVLMs) We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions. We propose a polling-based query method called POPE to evaluate the object hallucination.
  arXiv  Detail & Related papers  (2023-05-17T16:34:01Z)
• Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training [66.0036211069513]
  Large-scale vision-language pre-trained models are prone to hallucinate non-existent visual objects when generating text. We show that models achieving better scores on standard metrics could hallucinate objects more frequently. Surprisingly, we find that patch-based features perform the best and smaller patch resolution yields a non-trivial reduction in object hallucination.
  arXiv  Detail & Related papers  (2022-10-14T10:27:22Z)

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.