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TransNeXt: Robust Foveal Visual Perception for vision Transformers

TransNeXt: Robust Foveal Visual Perception for Vision Transf...

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

作者： Shi, Dai

ISBN: (纸本)9798350353006

Due to the depth degradation effect in residual connections, many efficient vision Transformers models that rely on stacking layers for information exchange often fail to form sufficient information mixing, leading to unnatural visual perception. To address this issue, in this paper, we propose Aggregated Attention, a biomimetic design-based token mixer that simulates biological foveal vision and continuous eye movement while enabling each token on the feature map to have a global perception. Furthermore, we incorporate learnable tokens that interact with conventional queries and keys, which further diversifies the generation of affinity matrices beyond merely relying on the similarity between queries and keys. Our approach does not rely on stacking for information exchange, thus effectively avoiding depth degradation and achieving natural visual perception. Additionally, we propose Convolutional GLU, a channel mixer that bridges the gap between GLU and SE mechanism, which empowers each token to have channel attention based on its nearest neighbor image features, enhancing local modeling capability and model robustness. We combine aggregated attention and convolutional GLU to create a new visual backbone called TransNeXt. Extensive experiments demonstrate that our TransNeXt achieves state-of-the-art performance across multiple model sizes. At a resolution of 224(2), TransNeXt-Tiny attains an ImageNet accuracy of 84.0%, surpassing ConvNeXt-B with 69% fewer parameters. Our TransNeXt-Base achieves an ImageNet accuracy of 86.2% and an ImageNet-A accuracy of 61.6% at a resolution of 384(2), a COCO object detection mAP of 57.1, and an ADE20K semantic segmentation mIoU of 54.7.

关键词： Aggregated Attention Biomimetic vision Design Convolutional GLU Efficient Transformer Foveal Visual Perception Image Classification Image Segmentation ImageNet-1K ImageNet-Adversarial Large-Kernel Convolution Length-Scaled Cosine Attention Multi-Scale Extrapolation Object Detection Perceptual Artifacts Pixel-focused Attention Robustness Self-Attention vision Transformer Visual Backbone

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CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place recognition

CricaVPR: Cross-image Correlation-aware Representation Learn...

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

作者： Lu, Feng Lan, Xiangyuan Zhang, Lijun Jiang, Dongmei Wang, Yaowei Yuan, Chun Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350353006

Over the past decade, most methods in visual place recognition (VPR) have used neural networks to produce feature representations. These networks typically produce a global representation of a place image using only this image itself and neglect the cross-image variations (e.g. viewpoint and illumination), which limits their robustness in challenging scenes. In this paper, we propose a robust global representation method with cross-image correlation awareness for VPR, named CricaVPR. Our method uses the attention mechanism to correlate multiple images within a batch. These images can be taken in the same place with different conditions or viewpoints, or even captured from different places. Therefore, our method can utilize the cross-image variations as a cue to guide the representation learning, which ensures more robust features are produced. To further facilitate the robustness, we propose a multi-scale convolution-enhanced adaptation method to adapt pre-trained visual foundation models to the VPR task, which introduces the multi-scale local information to further enhance the cross-image correlation-aware representation. Experimental results show that our method outperforms state-of-the-art methods by a large margin with significantly less training time. The code is released at https://***/Lu-Feng/CricaVPR.

关键词： convolution adapter cross-image correlation visual geo-localization visual place recognition

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DUSt3R: Geometric 3D vision Made Easy

DUSt3R: Geometric 3D Vision Made Easy

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

作者： Wang, Shuzhe Leroyt, Vincent Cabon, Yohann Chidlovskii, Boris Revaud, Jerome Aalto Univ Espoo Finland Naver Labs Europe Meylan France

ISBN: (纸本)9798350353006

Multi-view stereo reconstruction (MVS) in the wild re-quires to first estimate the camera intrinsic and extrinsic parameters. These are usually tedious and cumbersome to obtain, yet they are mandatory to triangulate corresponding pixels in 3D space, which is at the core of all best performing MVS algorithms. In this work, we take an opposite stance and introduce DUSt3R, a radically novel paradigm for Dense and Unconstrained Stereo 3D Reconstruction of arbitrary image collections, operating without prior information about camera calibration nor viewpoint poses. We cast the pairwise reconstruction problem as a regression of pointmaps, relaxing the hard constraints of usual projective camera models. We show that this formulation smoothly unifies the monocular and binocular reconstruction cases. In the case where more than two images are provided, we further propose a simple yet effective global alignment strategy that expresses all pairwise pointmaps in a common reference frame. We base our network architecture on standard Transformer encoders and decoders, allowing us to leverage powerful pretrained models. Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, focal lengths, relative and absolute cameras. Extensive experiments on all these tasks showcase how DUSt3R effectively unifies various 3D vision tasks, setting new performance records on monocular & multi-view depth estimation as well as relative pose estimation. In summary, DUSt3R makes many geometric 3D vision tasks easy. Code and mod-els at https://***/naver/dust3r.

关键词： 3D reconstruction camera calibration foundation model monocular depth multi-view depth multi-view pose estimation visual localization

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Explaining CLIP's performance disparities on data from blind/low vision users

Explaining CLIP's performance disparities on data from blind...

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

作者： Massiceti, Daniela Longden, Camilla Slowik, Agnieszka Wills, Samuel Grayson, Martin Morrison, Cecily Microsoft Res Redmond WA 98052 USA World Bank 1818 H St NW Washington DC 20433 USA

ISBN: (纸本)9798350353006

Large multi-modal models (LMMs) hold the potential to usher in a new era of automated visual assistance for people who are blind or low vision (BLV). Yet, these models have not been systematically evaluated on data captured by BLV users. We address this by empirically assessing CLIP, a widely-used LMM likely to underpin many assistive technologies. Testing 25 CLIP variants in a zero-shot classification task, we find that their accuracy is 15 percentage points lower on average for images captured by BLV users than web-crawled images. This disparity stems from CLIP's sensitivities to 1) image content (e.g. not recognizing disability objects as well as other objects);2) image quality (e.g. not being robust to lighting variation);and 3) text content (e.g. not recognizing objects described by tactile adjectives as well as visual ones). We delve deeper with a textual analysis of three common pre-training datasets: LAION-400M, LAION-2B and DataComp-1B, showing that disability content is rarely mentioned. We then provide three examples that illustrate how the performance disparities extend to three downstream models underpinned by CLIP: OWL-ViT, CLIPSeg and DALL-E2. We find that few-shot learning with as few as 5 images can mitigate CLIP's quality-of-service disparities for BLV users in some scenarios, which we discuss alongside a set of other possible mitigations.

关键词： accessibility blind CLIP fairness low vision multi-modal vision-language zero-shot classification

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Lookahead Exploration with Neural Radiance Representation for Continuous vision-Language Navigation

Lookahead Exploration with Neural Radiance Representation fo...

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

作者： Wang, Zihan Li, Xiangyang Yang, Jiahao Liu, Yeqi Hu, Junjie Jiang, Ming Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI USA Indiana Univ Dept Human Ctr Comp Indianapolis IN 46204 USA

ISBN: (纸本)9798350353006

vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step, the agent selects from possible candidate locations and then makes the move. For better navigation planning, the lookahead exploration strategy aims to effectively evaluate the agent's next action by accurately anticipating the future environment of candidate locations. To this end, some existing works predict RGB images for future environments, while this strategy suffers from image distortion and high computational cost. To address these issues, we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments, which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore, with the predicted future environmental representations, our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method. The code is available at https://***/MrZihan/HNR-VLN

关键词： Lookahead Exploration Neural Radiance Fields vision-and-Language Navigation

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SparseOcc: Rethinking Sparse Latent Representation for vision-Based Semantic Occupancy Prediction

SparseOcc: Rethinking Sparse Latent Representation for Visio...

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

作者： Tang, Pin Wang, Zhongdao Wang, Gum-ling Zheng, Jilai Ren, Xiangxuan Feng, Bailan Ma, Chao Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350353006

vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and space complexity, which limits scalability in terms of perception range or spatial resolution. Existing approaches compress the dense representation using projections like Bird's Eye View (BEV) or Tri-Perspective View (TPV). Although efficient, these projections result in information loss, especially for tasks like semantic occupancy prediction. To address this, we propose SparseOcc, an efficient occupancy network inspired by sparse point cloud processing. It utilizes a lossless sparse latent representation with three key innovations. Firstly, a 3D sparse diffuser performs latent completion using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature pyramid and sparse interpolation enhance scales with information from others. Finally, the transformer head is redesigned as a sparse variant. SparseOcc achieves a remarkable 74.9% reduction on FLOPs over the dense baseline. Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in part can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.

关键词： Autonomous Driving Occupancy Prediction Sparse Representation vision-Based Perception

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Multi-criteria Token Fusion with One-step-ahead Attention for Efficient vision Transformers

Multi-criteria Token Fusion with One-step-ahead Attention fo...

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

作者： Lee, Sanghyeok Choi, Joonmyung Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350353006

vision Transformer (ViT) has emerged as a prominent backbone for computer vision. For more efficient ViTs, recent works lessen the quadratic cost of the self- attention layer by pruning or fusing the redundant tokens. However, these works faced the speed-accuracy trade-off caused by the loss of information. Here, we argue that token fusion needs to consider diverse relations between tokens to minimize information loss. In this paper, we propose a Multi-criteria Token Fusion (MCTF), that gradually fuses the tokens based on multi-criteria (i.e., similarity, informativeness, and size of fused tokens). Further, we utilize the one-step-ahead attention, which is the improved approach to capture the informativeness of the tokens. By training the model equipped with MCTF using a token reduction consistency, we achieve the best speed-accuracy tradeoff in the image classification (ImageNet1K). Experimental results prove that MCTF consistently surpasses the previous reduction methods with and without training. Specifically, DeiT-T and DeiT-S with MCTF reduce FLOPs by about 44% while improving the performance (+0.5%, and +0.3%) over the base model, respectively. We also demonstrate the applicability of MCTF in various vision Transformers (e.g., T2T-ViT, LV-ViT), achieving at least 31% speedup without performance degradation. Code is available at https://***/mlvlab/MCTF.

关键词： Efficient ViTs Token Fusion Token Merging Token Reduction

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Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Surv...

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

作者： Conde, Marcos, V Vasluianu, Florin-Alexandru Timofte, Radu Zhang, Jianxing Li, Jia Wang, Fan Li, Xiaopeng Liu, Zikun Park, Hyunhee Song, Sejun Kim, Changho Huang, Zhijuan Yu, Hongyuan Wan, Cheng Xiang, Wending Lin, Jiamin Zhong, Hang Zhang, Qiaosong Sun, Yue Yin, Xuanwu Zuo, Kunlong Xu, Senyan Jiang, Siyuan Sun, Zhijing Zhu, Haying Li, Liangyan Chen, Ke Li, Yunzhe Ning, Yimo Zhao, Guanhua Chen, Jun Yu, Jinyang Xu, Kele Xu, Qisheng Dou, Yong Univ Wurzburg CAIDAS & IFI Comp Vis Lab Wurzburg Germany

ISBN: (纸本)9798350365474

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.

关键词： computational photography ISP NTIRE RAW Super-Resolution

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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...

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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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Modeling Collaborator: Enabling Subjective vision Classification With Minimal Human Effort via LLM Tool-Use

Modeling Collaborator: Enabling Subjective Vision Classifica...

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

作者： Toubal, Imad Eddine Avinash, Aditya Alldrin, Neil Gordon Dlabal, Jan Zhou, Wenlei Luo, Enming Stretcu, Otilia Xiong, Hao Lu, Chun-Ta Zhou, Howard Krishna, Ranjay Fuxman, Ariel Duerig, Tom Google Res Mountain View CA 94043 USA Univ Missouri Columbia MO 65211 USA Univ Washington Seattle WA USA

ISBN: (纸本)9798350353006

From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, developing classifiers for such concepts requires substantial manual effort measured in hours, days, or even months to identify and annotate data needed for training. Even with recently proposed Agile Modeling techniques, which enable rapid bootstrapping of image classifiers, users are still required to spend 30 minutes or more of monotonous, repetitive data labeling just to train a single classifier. Drawing on Fiske's Cognitive Miser theory, we propose a new framework that alleviates manual effort by replacing human labeling with natural language interactions, reducing the total effort required to define a concept by an order of magnitude: from labeling 2,000 images to only 100 plus some natural language interactions. Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points. Most importantly, our framework eliminates the need for crowd-sourced annotations. Moreover, our framework ultimately produces lightweight classification models that are deployable in cost-sensitive scenarios. Across 15 subjective concepts and across 2 public image classification datasets, our trained models outperform traditional Agile Modeling as well as state-of-the-art zero-shot classification models like ALIGN, CLIP, CuPL, and large visual question answering models like PaLI-X.

关键词： agile modeling large language model subjective classification tool-use vision language model

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