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Exploring and Utilizing pattern Imbalance

Exploring and Utilizing Pattern Imbalance

引用

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

作者： Mei, Shibin Zhao, Chenglong Yuan, Shengchao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

ISBN: (纸本)9798350301298

In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.

关键词： Datasets and evaluation

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Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension

Extend Your Own Correspondences: Unsupervised Distant Point ...

引用

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

作者： Liu, Quan Zhul, Hongzi Wang, Zhenxi Zhou, Yunsong Chang, Shan Guo, Minyi Shanghai Jiao Tong Univ Shanghai Peoples R China Donghua Univ Shanghai Peoples R China

ISBN: (纸本)9798350353006

Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety related applications, yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper, we propose EYOC, an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly, requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion, where in each round, the feature extractor, trained with near point cloud pairs, can label slightly farther point cloud pairs, enabling self- supervision on such far point cloud pairs. This process continues until the derived extractor can be used to register distant point clouds. Particularly, to enable high-fidelity correspondence label generation, we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair, and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover, it outwits supervised methods regarding generalization performance on new data distributions.

关键词： 3D vision Autonomous Driving Point Cloud Registration Unsupervised Learning

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CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention

CAD-SIGNet: CAD Language Inference from Point Clouds using L...

引用

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

作者： Khan, Mohammad Sadil Dupont, Elona Ali, Sk Aziz Cherenkova, Kseniya Kacem, Anis Aouada, Djamila Univ Luxembourg SnT Luxembourg Luxembourg German Res Ctr Artificial Intelligence Berlin Germany Artec3D Luxembourg Luxembourg

ISBN: (纸本)9798350353013;9798350353006

Reverse engineering in the realm of computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD- SIGNet, an end-to-end trainable and aeto-regressive architecture to recover the design history of a CAD model represented as a sequence of sketch-and-extrusion from an input point cloud. Our model learns CAD visual-language representations by layer-wise cross-attention between point cloud and CAD language embedding. In particular, a new Sketch instance Guided Attention (SGA) module is proposed in order to reconstruct the fine-grained details of the sketches. Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices. This allows for an interactive reverse engineering scenario by providing designers with multiple next step choices along with the design process. Extensive experiments on publicly available CAD datasets showcase the effectiveness of our approach against existing baseline models in two settings, namely, full design history recovery and conditional autocompletion from point clouds.

关键词： 3D Point Clouds computer-Aided Design (CAD) Reverse Engineering vision Language Models

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Equivariant Multi-Modality Image Fusion

Equivariant Multi-Modality Image Fusion

引用

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

作者： Zhao, Zixiang Hai, Haowen Zhang, Jiangshe Zhang, Yulun Zhane, Kai Xu, Shuang Chen, Dongdong Timofte, Radu Van Gool, Luc Xi An Jiao Tong Univ Xian Peoples R China Swiss Fed Inst Technol Zurich Switzerland Shanghai Jiao Tong Univ Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Northwestern Polytech Univ Xian Peoples R China Heriot Watt Univ Edinburgh Midlothian Scotland Univ Wurzburg Wurzburg Germany INSAIT Sofia Bulgaria

ISBN: (纸本)9798350353006

Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modality, such as functional highlights and texture details. However, effective training of such fusion models is challenging due to the scarcity of ground truth fusion data. To tackle this issue, we propose the Equivariant Multi-Modality imAge fusion (EMMA) paradigm for end-to-end self-supervised learning. Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations. Consequently, we introduce a novel training paradigm that encompasses a fusion module, a pseudo-sensing module, and an equivariant fusion module. These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior. Extensive experiments confirm that EMMA yields high-quality fusion results for infraredvisible and medical images, concurrently facilitating downstream multi-modal segmentation and detection tasks. The code is available at https://***/Zhaozixiang1228/MMIF-EMMA.

关键词： image fusion low-level vision

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The Gender Gap in Face recognition Accuracy Is a Hairy Problem

The Gender Gap in Face Recognition Accuracy Is a Hairy Probl...

引用

23rd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Bhatta, Aman Albiero, Vitor Bowyer, Kevin W. King, Michael C. Univ Notre Dame Notre Dame IN 46556 USA Florida Inst Technol Melbourne FL 32901 USA

ISBN: (纸本)9798350320565

It is broadly accepted that there is a "gender gap" in face recognition accuracy, with females having lower accuracy. However, relatively little is known about the cause(s) of this gender gap. We first demonstrate that female and male hairstyles have important differences that impact face recognition accuracy. In particular, variation in male facial hair contributes to a greater average difference in appearance between different male faces. We then demonstrate that when the data used to evaluate recognition accuracy is gender-balanced for how hairstyles occlude the face, the initially observed gender gap in accuracy largely disappears. We show this result for two different matchers, and for a Caucasian image dataset and an African-American dataset. Our results suggest that research on demographic variation in accuracy should include a check for balanced quality of the test data as part of the problem formulation. This new understanding of the causes of the gender gap in recognition accuracy will hopefully promote rational consideration of what might be done about it. To promote reproducible research, the matchers, attribute classifiers, and datasets used in this work are available to other researchers.

关键词： Hair computer vision Face recognition conferences

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OpenBias: Open-set Bias Detection in Text-to-Image Generative Models

OpenBias: Open-set Bias Detection in Text-to-Image Generativ...

引用

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

作者： D'Inca, Moreno Peruzzo, Elia Mancini, Massimiliano Xu, Dejia Goel, Vidit Xu, Xinggian Wang, Zhangyang Shi, Humphrey Sebe, Nicu Univ Trento Trento Italy UT Austin Austin TX USA SHI Labs Georgia Tech & UIUC Atlanta GA USA Picsart AI Res PAIR Miami FL USA

ISBN: (纸本)9798350353006

Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments, it is necessary to deeply investigate their safety and fairness to not disseminate and perpetuate any kind of biases. However, existing works focus on detecting closed sets of biases defined a priori, limiting the studies to well-known concepts. In this paper, we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias, a new pipeline that identifies and quantifies the severity of biases agnostically, without access to any precompiled set. OpenBias has three stages. In the first phase, we leverage a Large Language Model (LLM) to propose biases given a set of captions. Secondly, the target generative model produces images using the same set of captions. Lastly, a vision Question Answering model recognizes the presence and extent of the previously proposed biases. We study the behavior of Stable Diffusion 1.5, 2, and XL emphasizing new biases, never investigated before. Via quantitative experiments, we demonstrate that OpenBias agrees with current closed-set bias detection methods and human judgement.

关键词： Bias computer vision Fairness Generative AI Text-to-Image

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MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning

MM-Narrator: Narrating Long-form Videos with Multimodal In-C...

引用

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

作者： Zh, Chaoyi Lin, Kevin Yang, Zhengyuan Wang, Jianfeng Li, Linjie Lin, Chung-Ching Liu, Zicheng Wang, Lijuan Univ Sydney Sydney NSW Australia Microsoft Corp Redmond WA 98052 USA Adv Micro Devices Inc Santa Clara CA USA

ISBN: (纸本)9798350353006

We present MM-Narrator, a novel system leveraging GPT-4 with multimodal in-context learning for the generation of audio descriptions (AD). Unlike previous methods that primarily focused on downstream fine-tuning with short video clips, MM-Narrator excels in generating precise audio descriptions for videos of extensive lengths, even beyond hours, in an autoregressive manner. This capability is made possible by the proposed memory-augmented generation process, which effectively utilizes both the short-term textual context and long-term visual memory through an efficient register-and-recall mechanism. These contextual memories compile pertinent past information, including storylines and character identities, ensuring an accurate tracking and depicting of story-coherent and character-centric audio descriptions. Maintaining the training-free design of MM-Narrator, we further propose a complexity-based demonstration selection strategy to largely enhance its multi-step reasoning capability via few-shot multimodal in-context learning (MM-ICL). Experimental results on MAD-eval dataset demonstrate that MM-Narrator consistently outperforms both the existing fine-tuning-based approaches and LLM-based approaches in most scenarios, as measured by standard evaluation metrics. Additionally, we introduce the first segment-based evaluator for recurrent text generation. Empowered by GPT-4, this evaluator comprehensively reasons and marks AD generation performance in various extendable dimensions.

关键词： audio description in-context learning LLM multimodal retrieval-augmented generation video understanding vision-and-language

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Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

Scaling Up to Excellence: Practicing Model Scaling for Photo...

引用

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

作者： Yu, Fanghua Gu, Jinjin Li, Zheyuan Liu, Jinfan Kong, Xiangtao Wang, Xintao He, Jingwen Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Tencent PCG ARC Lab Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-modal techniques and advanced generative prior, SUPIR marks a significant advance in intelligent and realistic image restoration. As a pivotal catalyst within SUPIR, model scaling dramatically enhances its capabilities and demonstrates new potential for image restoration. We collect a dataset comprising 20 million high-resolution, high-quality images for model training, each enriched with descriptive text annotations. SUPIR provides the capability to restore images guided by textual prompts, broadening its application scope and potential. Moreover, we introduce negative-quality prompts to further improve perceptual quality. We also develop a restoration-guided sampling method to suppress the fidelity issue encountered in generative-based restoration. Experiments demonstrate SUPIR's exceptional restoration effects and its novel capacity to manipulate restoration through textual prompts.

关键词： Blind Image Restoration Diffusion Models Low-level vision

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Low-Rank Rescaled vision Transformer Fine-Tuning: A Residual Design Approach

Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual...

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

作者： Dong, Wei Zhang, Xing Chen, Bihui Yang, Dawei Lin, Zhijun Yang, Qingsen Wang, Peng Yang, Yang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Xian Univ Architecture & Technol Coll Informat & Control Engn Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9798350353006

Parameter-efficient fine-tuning for pre-trained vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://***/zstarN70/***.

关键词： Low-Rank Adaptation Parameter-efficient fine-tuning Singular Value Decomposition

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Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language

Separating the "Chirp" from the "Chat": Self-supervised Visu...

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

作者： Hamilton, Mark Zisserman, Andrew Hershey, John R. Freeman, William T. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Microsoft Redmond WA 98052 USA Google Oxford England

ISBN: (纸本)9798350353006

We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the "meaning" of words and the "location" of sounds without explicit localization supervision. Furthermore, it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast, many other systems that learn "global" audio and video representations cannot localize words and sound. Finally, we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art, ImageBind, on cross-modal retrieval using fewer than half of the parameters. Project Page: https://***/denseav

关键词： audio audio-visual contrastive learning language multimodal object discovery semantic segmentation sound unsupervised learning vision visual grounding

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