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Mitigating Object Hallucinations in Large vision-Language Models through Visual Contrastive Decoding

Mitigating Object Hallucinations in Large Vision-Language Mo...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Leng, Sicong Zhang, Hang Chen, Guanzheng Li, Xin Lug, Shijian Miao, Chunyan Bing, Lidong Alibaba Grp DAMO Acad Hangzhou Peoples R China Nanyang Technol Univ Singapore Singapore Hupan Lab Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350353006

Large vision-Language Models (LVLMs) have advanced considerably, intertwining visual recognition and language understanding to generate content that is not only coherent but also contextually attuned. Despite their success, LVLMs still suffer from the issue of object hallucinations, where models generate plausible yet incorrect outputs that include objects that do not exist in the images. To mitigate this issue, we introduce Visual Contrastive Decoding (VCD), a simple and training-free method that contrasts output distributions derived from original and distorted visual inputs. The proposed VCD effectively reduces the over-reliance on statistical bias and unimodal priors, two essential causes of object hallucinations. This adjustment ensures the generated content is closely grounded to visual inputs, resulting in contextually accurate outputs. Our experiments show that VCD, without either additional training or the usage of external tools, significantly mitigates the object hallucination issue across different LVLM families. Beyond mitigating object hallucinations, VCD also excels in general LVLM benchmarks, highlighting its wide-ranging applicability.

关键词： Large Multimodal Models Multimodality vision and Language

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Cinematic Behavior Transfer via NeRF-based Differentiable Filming

Cinematic Behavior Transfer via NeRF-based Differentiable Fi...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jiang, Xuekun Rao, Anyi Wang, Jingbo Lin, Dahua Dai, Bo Shanghai AI Lab Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In the evolving landscape of digital media and video production, the precise manipulation and reproduction of visual elements like camera movements and character actions are highly desired. Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections, neglecting 3D statuses. To address these issues, we first introduce a reverse filming behavior estimation technique. It optimizes camera trajectories by leveraging NeRF as a differentiable renderer and refining SMPL tracks. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment. The incorporation of 3D engine workflow enables superior rendering and control abilities, which also achieves a higher rating in the user study.

关键词： Three dimensional computer graphics

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Prompt Learning via Meta-Regularization

Prompt Learning via Meta-Regularization

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Park, Jinyoung Ko, Juyeon Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350353006

Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently, prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of down-stream tasks. However, most existing prompt learning methods suffer from task overfitting since the general knowledge of the pre-trained vision language models is forgotten while the prompts are finetuned on a small data set from a specific target task. To address this issue, we propose a Prompt Meta-Regularization (ProMetaR) to improve the generalizability of prompt learning for vision-language models. Specifically, ProMetaR meta-learns both the regularizer and the soft prompts to harness the task-specific knowledge from the downstream tasks and task-agnostic general knowledge from the vision-language models. Further, ProMetaR augments the task to generate multiple virtual tasks to alleviate the meta-overfitting. In addition, we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings. The code of ProMetaR is available at https://***/mlvlab/ProMetaR.

关键词： Meta-learning Prompt tuning vision-Language Model

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KBody: Balanced monocular whole-body estimation

KBody: Balanced monocular whole-body estimation

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zioulis, Nikolaos O'Brien, James F. Klothed Technologies Inc. United States Uc Berkeley United States

ISBN: (纸本)9798350302493

KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body's parameters. Compared to other approaches, it introduces virtual joints to identify higher quality correspondences and disentangles the optimization between the pose and shape parameters to achieve a more balanced result in terms of pose and shape capturing capacity, as well as pixel alignment. © 2023 ieee.

关键词： computer vision

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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vision + Language Applications: A Survey

Vision + Language Applications: A Survey

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Zhou, Yutong Shimada, Nobutaka Ritsumeikan University Shiga Japan

ISBN: (纸本)9798350302493

Text-to-image generation has attracted significant interest from researchers and practitioners in recent years due to its widespread and diverse applications across various industries. Despite the progress made in the domain of vision and language research, the existing literature remains relatively limited, particularly with regard to advancements and applications in this field. This paper explores a relevant research track within multimodal applications, including text, vision, audio, and others. In addition to the studies discussed in this paper, we are also committed to continually updating the latest relevant papers, datasets, application projects and corresponding information at https://***/Yutong-Zhou-cv/Awesome-Text-to-Image. © 2023 ieee.

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SpikingResformer: Bridging ResNet and vision Transformer in Spiking Neural Networks

SpikingResformer: Bridging ResNet and Vision Transformer in ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Shi, Xinyu Hao, Zecheng Yu, Zhaofei Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

The remarkable success of vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs, they lack reasonable scaling methods, and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address these challenges, we propose a novel spiking self-attention mechanism named Dual Spike Self-Attention (DSSA) with a reasonable scaling method. Based on DSSA, we propose a novel spiking vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters. Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking vision Transformer counterparts. Notably, our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps, which is the state-of-the-art result in the SNN field. Codes are available at https://***/xyshi2000/SpikingResformer

关键词： Spiking Neural Networks vision Transformer

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Active Open-Vocabulary recognition: Let Intelligent Moving Mitigate CLIP Limitations

Active Open-Vocabulary Recognition: Let Intelligent Moving M...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fan, Lei Zhou, Jianxiong Xing, Xiaoying Wu, Ying Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9798350353006

Active recognition, which allows intelligent agents to explore observations for better recognition performance, serves as a prerequisite for various embodied AI tasks, such as grasping, navigation and room arrangements. Given the evolving environment and the multitude of object classes, it is impractical to include all possible classes during the training stage. In this paper, we aim at advancing active open-vocabulary recognition, empowering embodied agents to actively perceive and classify arbitrary objects. However, directly adopting recent open-vocabulary classification models, like Contrastive Language Image Pretraining (CLIP), poses its unique challenges. Specifically, we observe that CLIP's performance is heavily affected by the viewpoint and occlusions, compromising its reliability in unconstrained embodied perception scenarios. Further, the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues, we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features, without relying on class-specific knowledge. Compared to baseline CLIP model with 29.6% accuracy on ShapeNet dataset, the proposed agent could achieve 53.3% accuracy for open-vocabulary recognition, without any fine-tuning to the equipped CLIP model. Additional experiments conducted with the Habitat simulator further affirm the efficacy of our method.

关键词： Active vision Embodied perception

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SnAG: Scalable and Accurate Video Grounding

SnAG: Scalable and Accurate Video Grounding

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Mu, Fangzhou Mo, Sicheng Li, Yin Univ Wisconsin Madison Madison WI 53706 USA

ISBN: (纸本)9798350353006

Temporal grounding of text descriptions in videos is a central problem in vision-language learning and video understanding. Existing methods often prioritize accuracy over scalability - they have been optimized for grounding only a few text queries within short videos, and fail to scale up to long videos with hundreds of queries. In this paper, we study the effect of cross-modal fusion on the scalability of video grounding models. Our analysis establishes late fusion as a more cost-effective fusion scheme for long-form videos with many text queries. Moreover, it leads us to a novel, video-centric sampling scheme for efficient training. Based on these findings, we present SnAG, a simple baseline for scalable and accurate video grounding. Without bells and whistles, SnAG is 43% more accurate and 1.5x faster than CONE, a state of the art for long-form video grounding on the challenging MAD dataset, while achieving highly competitive results on short videos. Our code is available at https://***/fmu2/snag_release.

关键词： Temporal Sentence Grounding Video understanding vision-Language Learning

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AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

AIDE: An Automatic Data Engine for Object Detection in Auton...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liang, Mingfu Sue, Jong-Chyi Schulter, Samuel Garg, Sparsh Zhao, Shiyu Wu, Ying Chandraker, Manmohan Northwestern Univ Evanston IL 60208 USA NEC Labs Amer Princeton NJ USA Rutgers State Univ New Brunswick NJ USA Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9798350353006

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. This process operates iteratively, allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.

关键词： Automatic Data Engine Autonomous Driving Continual Training Large Language Model Novel Object Detection vision Language Model

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