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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

Topology-Guided Multi-Class Cell Context Generation for Digi...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shahira Abousamra Rajarsi Gupta Tahsin Kurc Dimitris Samaras Joel Saltz Chao Chen Department of Computer Science Stony Brook University USA Department of Biomedical Informatics Stony Brook University USA

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

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vision Transformers are Parameter-Efficient Audio-Visual Learners

Vision Transformers are Parameter-Efficient Audio-Visual Lea...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yan-Bo Lin Yi-Lin Sung Jie Lei Mohit Bansal Gedas Bertasius Department of Computer Science UNC Chapel Hill

vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://***/project_page/LAVISH/

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Recognizing Rigid patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positives

Recognizing Rigid Patterns of Unlabeled Point Clouds by Comp...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Daniel Widdowson Vitaliy Kurlin Computer Science department University of Liverpool UK

Rigid structures such as cars or any other solid objects are often represented by finite clouds of unlabeled points. The most natural equivalence on these point clouds is rigid motion or isometry maintaining all inter-point distances. Rigid patterns of point clouds can be reliably compared only by complete isometry invariants that can also be called equivariant descriptors without false negatives (isometric clouds having different descriptions) and without false positives (non-isometric clouds with the same description). Noise and motion in data motivate a search for invariants that are continuous under perturbations of points in a suitable metric. We propose the first continuous and complete invariant of unlabeled clouds in any Euclidean space. For a fixed dimension, the new metric for this invariant is computable in a polynomial time in the number of points.

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NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Florin-Alexandru Vasluianu Tim Seizinger Zhuyun Zhou Zongwei Wu Cailian Chen Radu Timofte Wei Dong Han Zhou Yuqiong Tian Jun Chen Xueyang Fu Xin Lu Yurui Zhu Xi Wang Dong Li Jie Xiao Yunpeng Zhang Zheng-Jun Zha Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Yanyan Wei Zhihao Zhao Long Sun Tingting Yang Jinshan Pan Jiangxin Dong Jinhui Tang Bilel Benjdira Mohammed Nassif Anis Koubaa Ahmed Elhayek Anas M. Ali Kyotaro Tokoro Kento Kawai Kaname Yokoyama Takuya Seno Yuki Kondo Norimichi Ukita Chenghua Li Bo Yang Zhiqi Wu Gao Chen Yihan Yu Sixiang Chen Kai Zhang Tian Ye Wenbin Zou Yunlong Lin Zhaohu Xing Jinbin Bai Wenhao Chai Lei Zhu Ritik Maheshwari Rakshank Verma Rahul Tekchandani Praful Hambarde Satya Narayan Tazi Santosh Kumar Vipparthi Subrahmanyam Murala Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Fan Gao Weidan Yan Wenze Shao Dengyin Zhang Bin Chen Siqi Zhang Yanxin Qian Yuanbin Chen Yuanbo Zhou Tong Tong Rongfeng Wei Ruiqi Sun Yue Liu Nikhil Akalwadi Amogh Joshi Sampada Malagi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Adinath Dukre Vivek Deshmukh Shruti S. Phutke Ashutosh Kulkarni Anil Gonde Arun karthik K Manasa N Shri Hari Priya Wei Hao Xingzhuo Yan Minghan Fu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： computer vision Reviews Image synthesis conferences Buildings Image restoration pattern recognition

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

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

作者： Simpson, Benjamin Lubana, Ekdeep Liu, Yuchen Dick, Robert Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728193601

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Image enhancement

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Open-Set Representation Learning through Combinatorial Embedding

Open-Set Representation Learning through Combinatorial Embed...

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conference on computer vision and pattern recognition (CVPR)

作者： Geeho Kim Junoh Kang Bohyung Han Computer Vision Laboratory ECE Seoul National University IPAI Seoul National University

Visual recognition tasks are often limited to dealing with a small subset of classes simply because the labels for the remaining classes are unavailable. We are interested in identifying novel concepts in a dataset through representation learning based on both labeled and unlabeled examples, and extending the horizon of recognition to both known and novel classes. To address this challenging task, we propose a combinatorial learning approach, which naturally clusters the examples in unseen classes using the compositional knowledge given by multiple supervised meta-classifiers on heterogeneous label spaces. The representations given by the combinatorial embedding are made more robust by unsupervised pairwise relation learning. The proposed algorithm discovers novel concepts via a joint optimization for enhancing the discrimitiveness of unseen classes as well as learning the representations of known classes generalizable to novel ones. Our extensive experiments demonstrate remarkable performance gains by the proposed approach on public datasets for image retrieval and image categorization with novel class discovery.

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Repetition-aware Image Sequence Sampling for Recognizing Repetitive Human Actions

Repetition-aware Image Sequence Sampling for Recognizing Rep...

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International conference on computer vision workshops (ICCV workshops)

作者： Konstantinos Bacharidis Antonis Argyros Computer Science Department University of Crete Institute of Computer Science FORTH

In the field of video-based human action recognition (HAR), standard hand-crafted and deep learning-based approaches are constrained by the computational and memory requirements of their models and the length of the input sequence that can be processed during learning. Sampling techniques employing a windowed or a random clip cropping have been the simplest and most effective ways to cope with limitations on the maximum possible length of the input sequence. However, such designs do not guarantee that the correct ordering of the action steps is captured, or require several learning iterations. In this work we address this problem for the class of repetitive actions. Specifically, given a temporal segmentation of a repetitive action into its repetitive segments, we propose and develop novel approaches for ranking and selecting/sampling segments so as to improve learning in deep models for HAR. We show that by employing the proposed repetition-aware sampling schemes in state-of-the-art deep models for HAR, the action recognition accuracy is increased. The proposed approach is evaluated on existing datasets and on a new dataset that is tailored to the quantitative evaluation of the task at hand. The obtained results reveal how our approach performs in relation to various characteristics of the observed repetitive actions (repetition frequency, their effects on scene objects, etc) and demonstrate the performance improvements.

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Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Weakly Supervised Video Emotion Detection and Prediction via...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhicheng Zhang Lijuan Wang Jufeng Yang TMCC College of Computer Science Nankai University China

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://***/nku-zhichengzhang/WECL.

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CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability

CR-FIQA: Face Image Quality Assessment by Learning Sample Re...

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conference on computer vision and pattern recognition (CVPR)

作者： Fadi Boutros Meiling Fang Marcel Klemt Biying Fu Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. This work proposes a novel FIQA method, CR-FIQA, that estimates the face image quality of a sample by learning to predict its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property by probing internal network observations during the training process and utilizing it to predict the quality of unseen samples. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms. 1 1 https://***/fdbtrs/CR-FIQA

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NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Pri...

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conference on computer vision and pattern recognition (CVPR)

作者： Wenjing Bian Zirui Wang Kejie Li Jia-Wang Bian Victor Adrian Prisacariu Active Vision Lab University of Oxford

Training a Neural Radiance Field (NeRF) without precomputed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy. Our project page is https://***.

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