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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022"

共 11141 条记录，以下是41-50 订阅

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Choose What You Need: Disentangled Representation Learning for Scene Text recognition, Removal and Editing

Choose What You Need: Disentangled Representation Learning f...

引用

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

作者： Zhang, Boqiang Xie, Hongtao Gao, Zuan Wang, Yuxin Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

Scene text images contain not only style information (font, background) but also content information (character, texture). Different scene text tasks need different information, but previous representation learning methods use tightly coupled features for all tasks, resulting in sub-optimal performance. We propose a Disentangled Representation Learning framework (DARLING) aimed at disentangling these two types of features for improved adaptability in better addressing various downstream tasks (choose what you really need). Specifically, we synthesize a dataset of image pairs with identical style but different content. Based on the dataset, we decouple the two types of features by the supervision design. Clearly, we directly split the visual representation into style and content features, the content features are supervised by a text recognition loss, while an alignment loss aligns the style features in the image pairs. Then, style features are employed in reconstructing the counterpart image via an image decoder with a prompt that indicates the counterpart's content. Such an operation effectively decouples the features based on their distinctive properties. To the best of our knowledge, this is the first time in the field of scene text that disentangles the inherent properties of the text images. Our method achieves state-of-the-art performance in Scene Text recognition, Removal, and Editing.

关键词： Scene Text Scene Understanding

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Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning

Troika: Multi-Path Cross-Modal Traction for Compositional Ze...

引用

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

作者： Hu, Siteng Gong, Biao Feng, Yutong Zhang, Min Lv, Yiliang Wang, Donglin Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Westlake Univ Sch Engn AI Div Machine Intelligence Lab MiLAB Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limiting the exploitation of pre-trained knowledge and generalization to unseen compositions. With a particular focus on the universality of the solution, in this work, we propose a novel paradigm for CZSL models that establishes three identification branches (i.e., Multi-Path) to jointly model the state, object, and composition. The presented Troika is an outstanding implementation that aligns the branch-specific prompt representations with decomposed visual features. To calibrate the bias between semantically similar multi-modal representations, we further devise a Cross-Modal Traction module into Troika that shifts the prompt representation towards the current visual content. We conduct extensive experiments on three popular benchmarks, where our method significantly outperforms existing methods in both closed-world and open-world settings. The code will be available at https://***/bighuang624/Troika.

关键词： compositional zero-shot learning parameter-efficient transfer learning Troika vision-language models

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Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models

Visual Concept Connectome (VCC): Open World Concept Discover...

引用

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

作者： Kowal, Matthew Wildes, Richard P. Derpanis, Konstantinos G. York Univ Toronto ON Canada Samsung AI Ctr Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350353006

Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models, the Visual Concept Connectome (VCC), which discovers human interpretable concepts and their interlayer connections in a fully unsupervised manner. Our approach simultaneously reveals fine-grained concepts at a layer, connection weightings across all layers and is amendable to global analysis of network structure (e.g. branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract interpretable concepts from single layers and examine their impact on classification, but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also, we leverage VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks.

关键词： Concept-based interpretability Interpretability Multilayer interpretability Object recognition

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LowRankOcc: Tensor Decomposition and Low-Rank Recovery for vision-based 3D Semantic Occupancy Prediction

LowRankOcc: Tensor Decomposition and Low-Rank Recovery for V...

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

作者： Zhao, Linqing Xu, Xiuwei Wang, Ziwei Zhang, Yunpeng Zhang, Borui Zheng, Wenzhao Du, Dalong Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9798350353006

In this paper, we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods model outdoor scenes with fine-grained 3D grids, but the sparsity of non-empty voxels introduces considerable spatial redundancy, leading to potential overfitting risks. In contrast, our approach leverages the intrinsic low-rank property of 3D occupancy data, factorizing voxel representations into low-rank components to efficiently mitigate spatial redundancy without sacrificing performance. Specifically, we present the Vertical-Horizontal (VH) decomposition block factorizes 3D tensors into vertical vectors and horizontal matrices. With our "decomposition-encoding recovery" framework, we encode 3D contexts with only 1/2D convolutions and poolings, and subsequently recover the encoded compact yet informative context features back to voxel representations. Experimental results demonstrate that LowRankOcc achieves state-of-the-art performances in semantic scene completion on the SemanticKITTI dataset and 3D occupancy prediction on the nuScenes dataset.

关键词： 3D semantic occupancy tensor decomposition

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ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images

ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-vie...

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

作者： Jeong, Jinseo Koo, Junseo Zhang, Qimeng Kim, Gunhee Seoul Natl Univ Seoul South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Existing NeRF-based inverse rendering methods suppose that scenes are exclusively illuminated by distant light sources, neglecting the potential influence of emissive sources within a scene. In this work, we confront this limitation using LDR multi-view images captured with emissive sources turned on and off. Two key issues must be addressed: 1) ambiguity arising from the limited dynamic range along with unknown lighting details, and 2) the expensive computational cost in volume rendering to backtrace the paths leading to final object colors. We present a novel approach, ESR-NeRF, leveraging neural networks as learnable functions to represent ray-traced fields. By training networks to satisfy light transport segments, we regulate outgoing radiances, progressively identifying emissive sources while being aware of reflection areas. The results on scenes encompassing emissive sources with various properties demonstrate the superiority of ESR-NeRF in qualitative and quantitative ways. Our approach also extends its applicability to the scenes devoid of emissive sources, achieving lower CD metrics on the DTU dataset.

关键词： Inverse-rendering NeRF Scene-editing vision + graphics

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Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

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

作者： Noor, Nadhira Jametoni, Fabianaugie Kim, Jinbeom Hong, Hyunsu Park, In Kyu Inha Univ Dept Elect & Comp Engn Incheon 22212 South Korea Finedigital Inc Seongnam Si 13496 Gyeonggi Do South Korea

ISBN: (纸本)9798350365474

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Action recognition convolutional neural network embedded system skeleton-based

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A Generative Approach for Wikipedia-Scale Visual Entity recognition

A Generative Approach for Wikipedia-Scale Visual Entity Reco...

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

作者： Caron, Mathilde Iscen, Ahmet Fathi, Alireza Schmid, Cordelia Google Res San Francisco CA 94105 USA

ISBN: (纸本)9798350353006

In this paper, we address web-scale visual entity recognition, specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way of approaching a problem of such scale is using dual-encoder models (e.g. CLIP), where all the entity names and query images are embedded into a unified space, paving the way for an approximate kNN search. Alternatively, it is also possible to re-purpose a captioning model to directly generate the entity names for a given image. In contrast, we introduce a novel Generative Entity recognition (GER) frame-work, which given an input image learns to auto-regressively decode a semantic and discriminative "code" identifying the target entity. Our experiments demonstrate the efficacy of this GER paradigm, showcasing state-of-the-art performance on the challenging OVEN benchmark. GER surpasses strong captioning, dual-encoder, visual matching and hierarchical classification baselines, affirming its advantage in tackling the complexities of web-scale recognition.

关键词： generative auto-regressive models Image recognition vision-language models

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Learning to Count without Annotations

Learning to Count without Annotations

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

作者： Knobel, Lukas Han, Tengda Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands Univ Oxford Oxford England

ISBN: (纸本)9798350353006

While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images. We propose UnCounTR, a model that can learn this task without requiring any manual annotations. To this end, we construct "Self-Collages", images with various pasted objects as training samples, that provide a rich learning signal covering arbitrary object types and counts. Our method builds on existing unsupervised representations and segmentation techniques to successfully demonstrate for the first time the ability of reference-based counting without manual supervision. Our experiments show that our method not only outperforms simple base-lines and generic models such as FasterRCNN and DETR, but also matches the performance of supervised counting models in some domains.

关键词： computer vision self-supervision visual counting

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Continual Segmentation with Disentangled Objectness Learning and Class recognition

Continual Segmentation with Disentangled Objectness Learning...

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

作者： Gong, Yizheng Yu, Siyue Wang, Xiaoyang Xiao, Jimin Xian Jiaotong Liverpool Univ Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England Metavisioncn Istanbul Turkiye

ISBN: (纸本)9798350353013;9798350353006

Most continual segmentation methods tackle the problem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones, as objectness has strong transfer ability and forgetting resistance. Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning, a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes, we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://***/jordangong/CoMasTRe.

关键词： continual learning image segmentation Transformer

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Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing

Probabilistic Sampling of Balanced K-Means using Adiabatic Q...

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

作者： Zaech, Jan-Nico Danelljan, Martin Birdal, Tolga Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Univ Sofia INSAIT Sofia Bulgaria Imperial Coll London London England

ISBN: (纸本)9798350353006

Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for many computer vision tasks. Despite requiring multiple measurements from the noisy AQC, current approaches only utilize the best measurement, discarding information contained in the remaining ones. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.

关键词： Clustering computer vision Quantum Computing uncertainty estimation

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