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HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual I...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Qifan Li, Juncheng Wei, Longhui Pang, Liang Ye, Wentao Qin, Bosheng Tang, Siliang Tian, Qi Zhuang, Yueting Zhejiang Univ Hangzhou Peoples R China Huawei Cloud Suzhou Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350353006

Multi-modal Large Language Models (MLLMs) tuned on machine-generated instruction-following data have demonstrated remarkable performance in various multi-modal understanding and generation tasks. However, the hallucinations inherent in machine-generated data, which could lead to hallucinatory outputs in MLLMs, remain under-explored. This work aims to investigate various hallucinations (i.e., object, relation, attribute hallucinations) and mitigate those hallucinatory toxicities in large-scale machine-generated visual instruction datasets. Drawing on the human ability to identify factual errors, we present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. We use our framework to identify and eliminate hallucinations in the training data automatically. Interestingly, HalluciDoctor also indicates that spurious correlations arising from long-tail object cooccurrences contribute to hallucinations. Based on that, we execute counterfactual visual instruction expansion to balance data distribution, thereby enhancing MLLMs' resistance to hallucinations. Comprehensive experiments on hallucination evaluation benchmarks show that our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA. The data and code for this paper are publicly available.(1)

关键词： Hallucinations Multi-modal Language Model vision-language reasoning

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Robust Emotion recognition in Context Debiasing

Robust Emotion Recognition in Context Debiasing

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Dingkang Yang, Kun Li, Mingcheng Wang, Shunli Wang, Shuaibing Zhang, Lihua Fudan Univ Acad Engn & Technol Shanghai Peoples R China Cognit & Intelligent Technol Lab CIT Lab Beijing Peoples R China Jilin Prov Key Lab Intelligence Sci & Engn Changchun Peoples R China Minist Educ Engn Res Ctr AI & Robot Shanghai Peoples R China

ISBN: (纸本)9798350353006

Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation, causing severe performance bottlenecks and confounding valuable context priors. In this paper, we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically, we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference, we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes, resulting in bias mitigation and robust prediction. As a model-agnostic framework, CLEF can be readily integrated into existing methods, bringing consistent performance gains.

关键词： Counterfactual inference Emotion recognition

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One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained vision-Language Models

One Prompt Word is Enough to Boost Adversarial Robustness fo...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lin, L. Guan, Haoyan Qiu, Jianing Spratling, Michael Kings Coll London London England Imperial Coll London London England

ISBN: (纸本)9798350353006

Large pre-trained vision-Language Models (VLMs) like CLIP, despite having remarkable generalization ability, are highly vulnerable to adversarial examples. This work studies the adversarial robustness of VLMs from the novel perspective of the text prompt instead of the extensively studied model weights (frozen in this work). We first show that the effectiveness of both adversarial attack and defense are sensitive to the used text prompt. Inspired by this, we propose a method to improve resilience to adversarial attacks by learning a robust text prompt for VLMs. The proposed method, named Adversarial Prompt Tuning (APT), is effective while being both computationally and data efficient. Extensive experiments are conducted across 15 datasets and 4 data sparsity schemes (from 1-shot to full training data settings) to show APT's superiority over hand-engineered prompts and other state-of-the-art adaption methods. APT demonstrated excellent abilities in terms of the in-distribution performance and the generalization under input distribution shift and across datasets. Surprisingly, by simply adding one learned word to the prompts, APT can significantly boost the accuracy and robustness ((sic)=4/255 ) over the hand-engineered prompts by +13% and +8.5% on average respectively. The improvement further increases, in our most effective setting, to +26.4% for accuracy and +16.7% for robustness. Code is available at https://***/TreeLLi/APT.

关键词： adversarial examples adversarial robustness CLIP text prompting vision-language models VLMs

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THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large vision-Language Models

THRONE: An Object-based Hallucination Benchmark for the Free...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kaul, Prannay Li, Zhizhong Yang, Hao Dukler, Yonatan Swaminathan, Ashwin Taylor, C. J. Soatto, Stefano Univ Oxford VGG Oxford England AWS AI Labs Oxford England

ISBN: (纸本)9798350353006

Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses, which we term "Type I hallucinations". Instead, they focus on hallucinations responding to very specific question formats-typically a multiple-choice response regarding a particular object or attribute-which we term "Type II hallucinations". Additionally, such benchmarks often require external API calls to models which are subject to change. In practice, we observe that a reduction in Type II hallucinations does not lead to a reduction in Type I hallucinations but rather that the two forms of hallucinations are often anti-correlated. To address this, we pro-pose THRONE, a novel object-based automatic framework for quantitatively evaluating Type I hallucinations in LVLM free-form outputs. We use public language models (LMs) to identify hallucinations in LVLM responses and compute informative metrics. By evaluating a large selection of recent LVLMs using public datasets, we show that an improvement in existing metrics do not lead to a reduction in Type I hallucinations, and that established benchmarks for measuring Type I hallucinations are incomplete. Finally, we provide a simple and effective data augmentation method to reduce Type I and Type II hallucinations as a strong baseline.

关键词： benchmark hallucination large language model large vision-language model LLM LVLM

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On the Estimation of Image-matching Uncertainty in Visual Place recognition

On the Estimation of Image-matching Uncertainty in Visual Pl...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zaffar, Mubariz Nan, Liangliang Kooij, Julian F. P. Delft Univ Technol ME Delft Netherlands Delft Univ Technol ABE Delft Netherlands

ISBN: (纸本)9798350353006

In Visual Place recognition (VPR) the pose of a query image is estimated by comparing the image to a map of reference images with known reference poses. As is typical for image retrieval problems, a feature extractor maps the query and reference images to a feature space, where a nearest neighbor search is then performed. However, till recently little attention has been given to quantifying the confidence that a retrieved reference image is a correct match. Highly certain but incorrect retrieval can lead to catastrophic failure of VPR-based localization pipelines. This work compares for the first time the main approaches for estimating the image-matching uncertainty, including the traditional retrieval-based uncertainty estimation, more recent data-driven aleatoric uncertainty estimation, and the compute-intensive geometric verification. We further formulate a simple baseline method, "SUE", which unlike the other methods considers the freely-available poses of the reference images in the map. Our experiments reveal that a simple L2-distance between the query and reference descriptors is already a better estimate of image-matching uncertainty than current data-driven approaches. SUE outperforms the other efficient uncertainty estimation methods, and its uncertainty estimates complement the computationally expensive geometric verification approach. Future works for uncertainty estimation in VPR should consider the baselines discussed in this work.

关键词： geometric verification image matching image retrieval uncertainty estimation visual place recognition

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Investigating Compositional Challenges in vision-Language Models for Visual Grounding

Investigating Compositional Challenges in Vision-Language Mo...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zeng, Yunan Huang, Yan Zhang, Jinjin Jie, Zequn Chai, Zhenhua Wang, Liang Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9798350353006

Pre-trained vision-language models (VLMs) have achieved high performance on various downstream tasks, which have been widely used for visual grounding tasks in a weakly supervised manner. However, despite the performance gains contributed by large vision and language pre-training, we find that state-of-the-art VLMs struggle with compositional reasoning on grounding tasks. To demonstrate this, we propose Attribute, Relation, and Priority grounding (ARPGrounding) benchmark to test VLMs' compositional reasoning ability on visual grounding tasks. ARPGrounding contains 11,425 samples and evaluates the compositional understanding of VLMs in three dimensions: 1) attribute, denoting comprehension of objects' properties;2) relation, indicating an understanding of relation between objects;3) priority, reflecting an awareness of the part of speech associated with nouns. Using the ARPGrounding benchmark, we evaluate several mainstream VLMs. We empirically find that these models perform quite well on conventional visual grounding datasets, achieving performance comparable to or surpassing state-of-the-art methods but showing strong deficiencies in compositional reasoning. Furthermore, we propose a composition-aware fine- tuning pipeline, demonstrating the potential to leverage cost- effective image-text annotations for enhancing the compositional understanding of VLMs in grounding tasks. Code is available at link.

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Leveraging vision-Language Models for Improving Domain Generalization in Image Classification

Leveraging Vision-Language Models for Improving Domain Gener...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Addepalli, Sravanti Asokan, Ashish Ramayee Sharma, Lakshay Babu, R. Venkatesh Indian Inst Sci Vision & AI Lab Bangalore Karnataka India

ISBN: (纸本)9798350353006

vision-Language Models (VLMs) such as CLIP are trained on large amounts of image-text pairs, resulting in remarkable generalization across several data distributions. However, in several cases, their expensive training and data collection/curation costs do not justify the end application. This motivates a vendor-client paradigm, where a vendor trains a large-scale VLM and grants only input-output access to clients on a pay-per-query basis in a black-box setting. The client aims to minimize inference cost by distilling the VLM to a student model using the limited available task-specific data, and further deploying this student model in the downstream application. While naive distillation largely improves the In-Domain (ID) accuracy of the student, it fails to transfer the superior out-of-distribution (OOD) generalization of the VLM teacher using the limited available labeled images. To mitigate this, we propose vision-Language to vision - Align, Distill, Predict (VL2V-ADiP), which first aligns the vision and language modalities of the teacher model with the vision modality of a pre-trained student model, and further distills the aligned VLM representations to the student. This maximally retains the pre-trained features of the student, while also incorporating the rich representations of the VLM image encoder and the superior generalization of the text embeddings. The proposed approach achieves state-of-the-art results on the standard Domain Generalization benchmarks in a black-box teacher setting as well as a white-box setting where the weights of the VLM are accessible. Project page: http://***/VL2V-ADiP/

关键词： CLIP Distillation Domain Generalization OOD Generalization vision-language models

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YOLO-World: Real-Time Open-Vocabulary Object Detection

YOLO-World: Real-Time Open-Vocabulary Object Detection

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cheng, Tianheng Sone, Lin Ge, Yixiao Liu, Wenyu Wang, Xinggang Shan, Yong Tencent AI Lab Shenzhen Guangdong Peoples R China Tencent PCG ARC Lab Shenzhen Guangdong Peoples R China Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China

ISBN: (纸本)9798350353006

The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation, we introduce YOLO-World, an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically, we propose a new Re-parameterizable vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO- World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore, the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks, including object detection and open-vocabulary instance segmentation. Code and models are available at: https://***/AILab-CVC/YOLO-World.

关键词： Object detection open-vocabulary object detection real-time object detection vision-language modeling vision-language pre-training YOLO

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X-3D: Explicit 3D Structure Modeling for Point Cloud recognition

X-3D: Explicit 3D Structure Modeling for Point Cloud Recogni...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Sun, Shuofeng Rao, Yongming Lu, Jiwen Yan, Haibin Beijing Univ Posts & Telecommun Beijing Peoples R China Tencent Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Numerous prior studies predominantly emphasize constructing relation vectors for individual neighborhood points and generating dynamic kernels for each vector and embedding these into high-dimensional spaces to capture implicit local structures. However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the local geometric structure of point clouds due to the absence of explicit structural information. Hence, we introduce X-3D, an explicit 3D structure modeling approach. X-3D functions by capturing the explicit local structural information within the input 3D space and employing it to produce dynamic kernels with shared weights for all neighborhood points within the current local region. This modeling approach introduces effective geometric prior and significantly diminishes the disparity between the local structure of the embedding space and the original input point cloud, thereby improving the extraction of local features. Experiments show that our method can be used on a variety of methods and achieves state-of-the-art performance on segmentation, classification, detection tasks with lower extra computational cost, such as 90.7% on ScanObjectNN for classification, 79.2% on S3DIS 6 fold and 74.3% on S3DIS Area 5 for segmentation, 76.3% on ScanNetV2 for segmentation and 64.5% mAP25, 46.9% mAP50 on SUN RGB-D and 69.0% mAP25, 51.1% mAP50 on ScanNetV2. Our code is available at https://***/sunshuofeng/X-3D.

关键词： Vector spaces

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On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?

On the test-time zero-shot generalization of vision-language...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zanella, Maxime Ben Ayed, Ismail UCLouvain Louvain Belgium UMons Mons Belgium ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350353006

The development of large vision-language models, notably CLIP, has catalyzed research into effective adaptation techniques, with a particular focus on soft prompt tuning. Conjointly, test-time augmentation, which utilizes multiple augmented views of a single image to enhance zero-shot generalization, is emerging as a significant area of interest. This has predominantly directed research efforts toward test-time prompt tuning. In contrast, we introduce a robust MeanShift for Test-time Augmentation (MTA), which surpasses prompt-based methods without requiring this intensive training procedure. This positions MTA as an ideal solution for both standalone and API-based applications. Additionally, our method does not rely on ad hoc rules (e.g., confidence threshold) used in some previous test-time augmentation techniques to filter the augmented views. Instead, MTA incorporates a quality assessment variable for each view directly into its optimization process, termed as the inlierness score. This score is jointly optimized with a density mode seeking process, leading to an efficient training- and hyperparameter-free approach. We extensively benchmark our method on 15 datasets and demonstrate MTA's superiority and computational efficiency. Deployed easily as plug-and-play module on top of zero-shot models and state-of-the-art few-shot methods, MTA shows systematic and consistent improvements.

关键词： CLIP test-time augmentation training-free vision-language zero-shot

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