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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

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Making vision Transformers Efficient from A Token Sparsification View

Making Vision Transformers Efficient from A Token Sparsifica...

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

作者： Chang, Shuning Wang, Pichao Lin, Ming Wang, Fan Zhang, David Junhao Jin, Rong Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

The quadratic computational complexity to the number of tokens limits the practical applications of vision Transformers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) application difficulty in the local vision transformer, and (iii) non-general-purpose networks for downstream tasks. In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks. The semantic tokens represent cluster centers, and they are initialized by pooling image tokens in space and recovered by attention, which can adaptively represent global or local semantic information. Due to the cluster properties, a few semantic tokens can attain the same effect as vast image tokens, for both global and local vision transformers. For instance, only 16 semantic tokens on DeiT-(Tiny,Small,Base) can achieve the same accuracy with more than 100% inference speed improvement and nearly 60% FLOPs reduction;on Swin-(Tiny,Small,Base), we can employ 16 semantic tokens in each window to further speed it up by around 20% with slight accuracy increase. Besides great success in image classification, we also extend our method to video recognition. In addition, we design a STViT-R(ecovery) network to restore the detailed spatial information based on the STViT, making it work for downstream tasks, which is powerless for previous token sparsification methods. Experiments demonstrate that our method can achieve competitive results compared to the original networks in object detection and instance segmentation, with over 30% FLOPs reduction for backbone.

关键词： Deep learning architectures and techniques

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BEVHeight: A Robust Framework for vision-based Roadside 3D Object Detection

BEVHeight: A Robust Framework for Vision-based Roadside 3D O...

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

作者： Yang, Lei Yu, Kaicheng Tang, Tao Li, Jun Yuan, Kun Wang, Li Zhang, Xinyu Chen, Peng Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China Alibaba Grp Autonomous Driving Lab Hangzhou Peoples R China Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Peking Univ Ctr Machine Learning Res Beijing Peoples R China

ISBN: (纸本)9798350301298

While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight, to address this issue. In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. The code is available at https://***/ADLab-AutoDrive/BEVHeight.

关键词： Autonomous driving

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MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

MAIR: Multi-view Attention Inverse Rendering with 3D Spatial...

引用

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

作者： Choi, JunYong Lee, SeokYeong Park, Haesol Jung, Seung-Won Kim, Ig-Jae Cho, Junghyun Korea Inst Sci & Technol KIST Seoul South Korea Korea Univ Seoul South Korea Univ Sci & Technol KIST Sch AI Robot Koto City Japan Yonsei Univ Yonsei KIST Convergence Res Inst Seoul South Korea

ISBN: (纸本)9798350301298

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.

关键词： vision + graphics

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Visual Atoms: Pre-training vision Transformers with Sinusoidal Waves

Visual Atoms: Pre-training Vision Transformers with Sinusoid...

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

作者： Takashima, Sora Hayamizu, Ryo Inoue, Nakamasa Kataoka, Hirokatsu Yokota, Rio Natl Inst Adv Ind Sci & Technol Tokyo Japan Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350301298

Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViTBase, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases.

关键词： Datasets and evaluation

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ConCon-Chi: Concept-Context Chimera Benchmark for Personalized vision-Language Tasks

ConCon-Chi: Concept-Context Chimera Benchmark for Personaliz...

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2024 ieee/cvf conference on computer vision and pattern recognition, cvpr 2024

作者： Rosasco, Andrea Berti, Stefano Pasquale, Giulia Malafronte, Damiano Sato, Shogo Segawa, Hiroyuki Inada, Tetsugo Natale, Lorenzo University of Genoa Italy Istituto Italiano di Tecnologia Italy Sony Interactive Entertainment Inc. Japan

ISBN: (纸本)9798350353006

While recent vision-Language (VL) models excel at open-vocabulary tasks, it is unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image Retrieval (TIR) or Generation (TIG) are recently introduced tasks that represent this challenge, where the VL model has to learn a concept from few images and respectively discriminate or generate images of the target concept in arbitrary contexts. We identify the ability to learn new meanings and their compositionality with known ones as two key properties of a personalized system. We show that the available benchmarks offer a limited validation of personalized textual concept learning from images with respect to the above properties and introduce ConCon-Chi as a benchmark for both personalized TIR and TIG, designed to fill this gap. We modelled the new-meaning concepts by crafting chimeric objects and formulating a large, varied set of contexts where we photographed each object. To promote the compositionality assessment of the learned concepts with known contexts, we combined different contexts with the same concept, and vice-versa. We carry out a thorough evaluation of state-of-the-art methods on the resulting dataset. Our study suggests that future work on personalized TIR and TIG methods should focus on the above key properties, and we propose principles and a dataset for their performance assessment. Dataset: https://***/10.48557/QJ1166 and code: https://***/hsp-iit/concon-chi-benchmark. © 2024 ieee.

关键词： benchmark chimeric objects compositionality dataset personalization personalized text-to-image generation personalized text-to-image retrieval personalized vision-language models personalized vision-language tasks vision-language models vision-language tasks

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Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models

Recurrence without Recurrence: Stable Video Landmark Detecti...

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

作者： Micaelli, Paul Vandat, Arash Yin, Hongxu Kautz, Jan Molchanov, Paulo Univ Edinburgh Edinburgh Scotland NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350301298

Cascaded computation, whereby predictions are recurrently refined over several stages, has been a persistent theme throughout the development of landmark detection models. In this work, we show that the recently proposed Deep Equilibrium Model (DEQ) can be naturally adapted to this form of computation. Our Landmark DEQ (LDEQ) achieves state-of-the-art performance on the challenging WFLW facial landmark dataset, reaching 3.92 NME with fewer parameters and a training memory cost of O(1) in the number of recurrent modules. Furthermore, we show that DEQs are particularly suited for landmark detection in videos. In this setting, it is typical to train on still images due to the lack of labelled videos. This can lead to a "flickering" effect at inference time on video, whereby a model can rapidly oscillate between different plausible solutions across consecutive frames. By rephrasing DEQs as a constrained optimization, we emulate recurrence at inference time, despite not having access to temporal data at training time. This Recurrence without Recurrence (RwR) paradigm helps in reducing landmark flicker, which we demonstrate by introducing a new metric, normalized mean flicker (NMF), and contributing a new facial landmark video dataset (WFLW-V) targeting landmark uncertainty. On the WFLW-V hard subset made up of 500 videos, our LDEQ with RwR improves the NME and NMF by 10 and 13% respectively, compared to the strongest previously published model using a hand-tuned conventional filter.

关键词： detection recognition: Categorization retrieval

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ResFormer: Scaling ViTs with Multi-Resolution Training

ResFormer: Scaling ViTs with Multi-Resolution Training

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

作者： Tian, Rui Wu, Zuxuan Dai, Qi Hu, Han Qiao, Yu Jiang, Yu-Gang Fudan Univ Sch CS Shanghai Key Lab Intell Info Proc Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have achieved overwhelming success, yet they suffer from vulnerable resolution scalability, i.e., the performance drops drastically when presented with input resolutions that are unseen during training. We introduce, ResFormer, a framework that is built upon the seminal idea of multi-resolution training for improved performance on a wide spectrum of, mostly unseen, testing resolutions. In particular, ResFormer operates on replicated images of different resolutions and enforces a scale consistency loss to engage interactive information across different scales. More importantly, to alternate among varying resolutions effectively, especially novel ones in testing, we propose a global-local positional embedding strategy that changes smoothly conditioned on input sizes. We conduct extensive experiments for image classification on ImageNet. The results provide strong quantitative evidence that ResFormer has promising scaling abilities towards a wide range of resolutions. For instance, ResFormer-B-MR achieves a Top-1 accuracy of 75.86% and 81.72% when evaluated on relatively low and high resolutions respectively (i.e., 96 and 640), which are 48% and 7.49% better than DeiT-B. We also demonstrate, moreover, ResFormer is flexible and can be easily extended to semantic segmentation, object detection and video action recognition.

关键词： Efficient and scalable vision

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Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement

Gated Multi-Resolution Transfer Network for Burst Restoratio...

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

作者： Mehta, Nancy Dudhane, Akshay Murala, Subrahmanyam Zamir, Syed Waqas Khan, Salman Khan, Fahad Shahbaz Indian Inst Technol Ropar CVPR Lab Rupnagar India Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Incept Inst AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350301298

Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMT-Net consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validate our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. Our codes and models are available at https://***/nanmehta/GMTNet.

关键词： Low-level vision

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PROB: Probabilistic Objectness for Open World Object Detection

PROB: Probabilistic Objectness for Open World Object Detecti...

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

作者： Zohar, Orr Wang, Kuan-Chieh Yeung, Serena Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350301298

Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - which can be classified and incrementally learned. In standard OD, object proposals not overlapping with a labeled object are automatically classified as background. Therefore, simply applying OD methods to OWOD fails as unknown objects would be predicted as background. The challenge of detecting unknown objects stems from the lack of supervision in distinguishing unknown objects and background object proposals. Previous OWOD methods have attempted to overcome this issue by generating supervision using pseudo-labeling - however, unknown object detection has remained low. Probabilistic/generative models may provide a solution for this challenge. Herein, we introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood maximization of known objects in the embedded feature space - ultimately allowing us to estimate the objectness probability of different proposals. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting. Comprehensive experiments on OWOD benchmarks show that PROB outperforms all existing OWOD methods in both unknown object detection (similar to 2x unknown recall) and known object detection (similar to 10% mAP). Our code is available at https://***/orrzohar/PROB.

关键词： detection recognition: Categorization retrieval

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OCTET: Object-aware Counterfactual Explanations

OCTET: Object-aware Counterfactual Explanations

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

作者： Zemni, Mehdi Chen, Mickael Zablocki, Eloi Ben-Younes, Hedi Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9798350301298

Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern. Among explanation methods, counterfactual explanations aim to find minimal and interpretable changes to the input image that would also change the output of the model to be explained. Such explanations point end-users at the main factors that impact the decision of the model. However, previous methods struggle to explain decision models trained on images with many objects, e.g., urban scenes, which are more difficult to work with but also arguably more critical to explain. In this work, we propose to tackle this issue with an object-centric framework for counterfactual explanation generation. Our method, inspired by recent generative modeling works, encodes the query image into a latent space that is structured in a way to ease object-level manipulations. Doing so, it provides the end-user with control over which search directions (e.g., spatial displacement of objects, style modification, etc.) are to be explored during the counterfactual generation. We conduct a set of experiments on counterfactual explanation benchmarks for driving scenes, and we show that our method can be adapted beyond classification, e.g., to explain semantic segmentation models. To complete our analysis, we design and run a user study that measures the usefulness of counterfactual explanations in understanding a decision model. Code is available at https://***/valeoai/OCTET.

关键词： Explainable computer vision

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