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

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SkipPLUS: Skip the First Few Layers to Better Explain vision Transformers

SkipPLUS: Skip the First Few Layers to Better Explain Vision...

引用

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

作者： Faridoun Mehri Mohsen Fayyaz Mahdieh Soleymani Baghshah Mohammad Taher Pilehvar Sharif University of Technology Iran University of Tehran Iran Cardiff University UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite their remarkable performance, the explainability of vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn't been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://***/NightMachinery/SkipPLUS-CVPR-2024.

关键词： Training computer vision Animals Aggregates conferences Transformers pattern recognition

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Refining Biologically Inconsistent Segmentation Masks with Masked Autoencoders

Refining Biologically Inconsistent Segmentation Masks with M...

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

作者： Alexander Sauer Yuan Tian Joerg Bewersdorf Jens Rittscher Department of Engineering Science University of Oxford Department of Cell Biology Yale School of Medicine

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Microscopy images often feature regions of low signal-to-noise ratio (SNR) which leads to a considerable amount of ambiguity in the correct corresponding segmentation. This ambiguity can introduce inconsistencies in the segmentation mask which violate known biological constraints. In this work, we present a methodology which identifies areas of low SNR and refines the segmentation masks such that they are consistent with biological structures. Low SNR regions with uncertain segmentation are detected using model ensembling and selectively restored by a masked autoencoder (MAE) which leverages information about well-imaged surrounding areas. The prior knowledge of biologically consistent segmentation masks is directly learned from the *** validate our approach in the context of analysing intracellular structures, specifically by refining segmentation masks of mitochondria in expansion microscopy images with a global staining.

关键词： Image segmentation computer vision Mitochondria Microscopy conferences Biological system modeling Refining

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A General Framework for Jersey Number recognition in Sports Video

A General Framework for Jersey Number Recognition in Sports ...

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

作者： Maria Koshkina James H. Elder York University Toronto Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Jersey number recognition is an important task in sports video analysis, partly due to its importance for long-term player tracking. It can be viewed as a variant of scene text recognition. However, there is a lack of published attempts to apply scene text recognition models on jersey number data. Here we introduce a novel public jersey number recognition dataset for hockey and study how scene text recognition methods can be adapted to this problem. We address issues of occlusions and assess the degree to which training on one sport (hockey) can be generalized to another (soccer). For the latter, we also consider how jersey number recognition at the single-image level can be aggregated across frames to yield tracklet-level jersey number labels. We demonstrate high performance on image- and tracklet-level tasks, achieving 91.4% accuracy for hockey images and 87.4% for soccer tracklets. Code, models, and data are available at https://***/mkoshkina/jerseynumber-pipeline.

关键词： Training computer vision Adaptation models Codes Accuracy Text recognition conferences

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Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

引用

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

作者： Lap Quoc Tran Huan Duc Vi Asilla

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. The integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.

关键词： computer vision Accuracy Urban areas Cameras Real-time systems Trajectory Spatiotemporal phenomena

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NTIRE 2024 Challenge on Night Photography Rendering

NTIRE 2024 Challenge on Night Photography Rendering

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

作者： Egor Ershov Artyom Panshin Oleg Karasev Sergey Korchagin Shepelev Lev Alexandr Startsev Daniil Vladimirov Ekaterina Zaychenkova Nikola Banić Dmitrii R Iarchuk Maria Efimova Radu Timofte Arseniy Terekhin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year’s competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants’ solutions effectively represent the state-of-the-art in night-time photography rendering. More results can be found at https://***.

关键词： Photography Visualization computer vision Reviews conferences Rendering (computer graphics) Cameras

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On Accuracy and Speed of Geodesic Regression: Do Geometric Priors Improve Learning on Small Datasets?

On Accuracy and Speed of Geodesic Regression: Do Geometric P...

引用

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

作者： Adele Myers Nina Miolane UC Santa Barbara

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image datasets in specialized fields of science, such as biomedicine, are typically smaller than traditional machine learning datasets. As such, they present a problem for training many models. To address this challenge, researchers often attempt to incorporate priors, i.e., external knowledge, to help the learning procedure. Geometric priors, for example, offer to restrict the learning process to the manifold to which the data belong. However, learning on manifolds is sometimes computationally intensive to the point of being prohibitive. Here, we ask a provocative question: is machine learning on manifolds really more accurate than its linear counterpart to the extent that it is worth sacrificing significant speedup in computation? We answer this question through an extensive theoretical and experimental study of one of the most common learning methods for manifold-valued data: geodesic regression.

关键词： Manifolds Training Learning systems computer vision Accuracy conferences Computational modeling

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Density Map Distillation for Incremental Object Counting

Density Map Distillation for Incremental Object Counting

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wu, Chenshen Van De Weijer, Joost Computer Vision Center Barcelona Spain

ISBN: (纸本)9798350302493

We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. © 2023 ieee.

关键词： Distillation

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BeautyREC: Robust, Efficient, and Component-Specific Makeup Transfer

BeautyREC: Robust, Efficient, and Component-Specific Makeup ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Yan, Qixin Guo, Chunle Zhao, Jixin Dai, Yuekun Loy, Chen Change Li, Chongyi Tencent WeChat China Nankai University TMCC CS China Nanyang Technological University S-Lab Singapore

ISBN: (纸本)9798350302493

In this work, we propose a Robust, Efficient, and Component-specific makeup transfer method (abbreviated as BeautyREC). A unique departure from prior methods that leverage global attention, simply concatenate features, or implicitly manipulate features in latent space, we propose a component-specific correspondence to directly transfer the makeup style of a reference image to the corresponding components (e.g., skin, lips, eyes) of a source image, making elaborate and accurate local makeup transfer. As an auxiliary, the long-range visual dependencies of Transformer are introduced for effective global makeup transfer. Instead of the commonly used cycle structure that is complex and unstable, we employ a content consistency loss coupled with a content encoder to implement efficient single-path makeup transfer. The key insights of this study are modeling component-specific correspondence for local makeup transfer, capturing long-range dependencies for global makeup transfer, and enabling efficient makeup transfer via a single-path *** also contribute BeautyFace, a makeup transfer dataset to supplement existing datasets. This dataset contains 3,000 faces, covering more diverse makeup styles, face poses, and races. Each face has annotated parsing map. Extensive experiments demonstrate the effectiveness of our method against state-of-the-art methods. Besides, our method is appealing as it is with only 1M parameters, outperforming the state-of-the-art methods (BeautyGAN: 8.43M, PSGAN: 12.62M, SCGAN: 15.30M, CPM: 9.24M, SSAT: 10.48M). © 2023 ieee.

关键词： computer vision

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A Single Residual Network with ESA Modules and Distillation

A Single Residual Network with ESA Modules and Distillation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Yucong Cai, Minjie Hunan University College of Computer Science and Electronic Engineering China

ISBN: (纸本)9798350302493

Although there are many methods based on deep learning that have superior performance on single image super-resolution (SISR), it is difficult to run in real time on devices with limited computing power. Some recent studies have found that simply relying on reducing parameters or reducing the theoretical FLOPs of the model does not speed up the inference time of the network in a practical sense. Actual speed on the device is probably a better measure than FLOPs. In this work, we propose a new single residual network (SRN). On the one hand, we try to introduce and optimize an attention mechanism module to improve the performance of the network with a relatively small speed loss. On the other hand, we find that residuals in residual blocks do not have a positive impact on networks with adjusted ESA. Therefore, the residual of the network residual block is removed, which not only improves the speed of the network, but also improves the performance of the network. Finally, we reduced the number of channels and the number of residual blocks of the classic model EDSR, and removed the last convolution before the long residual. We set this tuned EDSR as the teacher model and our newly proposed SRN as the student model. Under the joint effect of the original loss and the distillation loss, the performance of the network can be improved without losing the inference time. Combining the above strategies, our proposed model runs much faster than similarly performing models. As an example, we built a Fast and Efficient Network (SRN) and its small version SRN-S, which run 30%-37% faster than the state-of-the-art EISR model: a paper champion RLFN. Furthermore, the shallow version of SRN-S achieves the second-shortest inference time as well as the second-smallest number of activations in the NTIRE2023 challenge. Code will be available at https://***/wnxbwyc/SRN. © 2023 ieee.

关键词： Deep learning

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MVDiff: Scalable and Flexible Multi-view Diffusion for 3D Object Reconstruction from Single-View

MVDiff: Scalable and Flexible Multi-view Diffusion for 3D Ob...

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

作者： Emmanuelle Bourigault Pauline Bourigault Department of Engineering Science Visual Geometry Group University of Oxford Department of Computing Imperial College London

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating consistent multiple views for 3D reconstruction tasks is still a challenge to existing image-to-3D diffusion models. Generally, incorporating 3D representations into diffusion model decrease the model’s speed as well as generalizability and quality. This paper proposes a general framework to generate consistent multi-view images from single image or leveraging scene representation transformer and view-conditioned diffusion model. In the model, we introduce epipolar geometry constraints and multi-view attention to enforce 3D consistency. From as few as one image input, our model is able to generate 3D meshes surpassing baselines methods in evaluation metrics, including PSNR, SSIM and LPIPS.

关键词： Geometry Measurement Solid modeling computer vision Three-dimensional displays conferences Diffusion models

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