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Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion

Diversity is Definitely Needed: Improving Model-Agnostic Zer...

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

作者： Jordan Shipard Arnold Wiliem Kien Nguyen Thanh Wei Xiang Clinton Fookes Signal Processing Artificial Intelligence and Vision Technologies (SAIVT) Queensland University of Technology Australia Sentient Vision Systems Australia School of Computing Engineering and Mathematical Sciences La Trobe University Australia

In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. However, the performance of this approach currently falls short of that achieved by large-scale vision-language models. One possible explanation is a potential significant domain gap between synthetic and real images. Our work offers a fresh perspective on the problem by providing initial insights that MA-ZSC performance can be improved by improving the diversity of images in the generated dataset. We propose a set of modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity, which we refer to as our bag of tricks. Our approach shows notable improvements in various classification architectures, with results comparable to state-of-the-art models such as CLIP. To validate our approach, we conduct experiments on CIFAR10, CIFAR100, and EuroSAT, which is particularly difficult for zero-shot classification due to its satellite image domain. We evaluate our approach with five classification architectures, including ResNet and ViT. Our findings provide initial insights into the problem of MA-ZSC using diffusion models. All code is available at https://***/Jordan-HS/Diversity_is_Definitely_Needed

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Tackling Domain Shifts in Person Re-Identification: A Survey and Analysis

Tackling Domain Shifts in Person Re-Identification: A Survey...

引用

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

作者： Vuong D. Nguyen Samiha Mirza Abdollah Zakeri Ayush Gupta Khadija Khaldi Rahma Aloui Pranav Mantini Shishir K. Shah Fatima Merchant University of Houston Johns Hopkins University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The necessity for a Person ReID system for rapidly evolving urban surveillance applications is severely challenged by domain shifts—variations in data distribution that occur across different environments or times. In this paper, we provide the first empirical review of domain shift in person ReID, which includes three settings namely Unsupervised Domain Adaptation ReID, Domain Generalizable ReID, and Lifelong ReID. We observe that existing approaches only tackle domain shifts caused by cross-dataset setting, while ignoring intra-dataset attribute domain shifts caused by changes in clothing, shape, or gait, which is very common in ReID. Thus, we enhance research directions in this field by redefining domain shift in ReID as the combination of attribute domain shift with cross-dataset domain shift. With a focus on Lifelong Re-ID methods, we conduct an extensive comparison on a fair experimental setup and provide an in-depth analysis of these methods under both non-cloth-changing and cloth-changing Re-ID scenarios. Insights into the strengths and limitations of these methods based on their performance are studied. This paper outlines future research directions and paves the way for the development of more adaptive, resilient, and enduring cross-domain ReID systems. Code is available here.

关键词： Surveys computer vision Codes Shape Reviews Surveillance conferences

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ELSA: Exploiting Layer-wise N:M Sparsity for vision Transformer Acceleration

ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transfor...

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

作者： Ning-Chi Huang Chi-Chih Chang Wei-Cheng Lin Endri Taka Diana Marculescu Kai-Chiang Wu National Yang Ming Chiao Tung University University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

N:M sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing N:M sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized N:M sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same number of *** this work, to address the challenge of selecting suitable sparse configuration for ViTs on N:M sparsity-supporting accelerators, we propose ELSA, Exploiting Layer-wise N:M Sparsity for ViTs. Considering not only all N:M sparsity levels supported by a given accelerator but also the expected throughput improvement, our methodology can reap the benefits of accelerators supporting mixed sparsity by trading off negligible accuracy loss with both memory usage and inference time reduction for ViT models. For instance, our approach achieves a noteworthy 2.9× reduction in FLOPs to both Swin-B and DeiT-B with only a marginal degradation of accuracy on ImageNet. Our code is publicly available at https://***/ningchihuang/ELSA.

关键词： Training Degradation computer vision Accuracy Transformers Throughput Software

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Vim4Path: Self-Supervised vision Mamba for Histopathology Images

Vim4Path: Self-Supervised Vision Mamba for Histopathology Im...

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

作者： Ali Nasiri-Sarvi Vincent Quoc-Huy Trinh Hassan Rivaz Mahdi S. Hosseini Department of Computer Science and Software Engineering (CSSE) Concordia University Canada Institute for Research in Immunology and Cancer of the University of Montreal Canada Department of Electrical and Computer Engineering (ECE) Concordia University Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim’s enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim’s capabilities, which reveals that Vim uniquely emulates the pathologist workflow—unlike ViT. This alignment with human expert analysis highlights Vim’s potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at https://***/AtlasAnalyticsLab/Vim4Path.

关键词： Representation learning Training computer vision Adaptation models Computational modeling Self-supervised learning computer architecture

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Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution

Multi-Level Feature Fusion Network for Lightweight Stereo Im...

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

作者： Yunxiang Li Wenbin Zou Qiaomu Wei Feng Huang Jing Wu Fuzhou University South China University of Technology Chengdu University of Information Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images. Cascading feature extraction modules and cross-view feature interaction modules to make use of the information from stereo images is the focus of numerous methods. However, this adds a great deal of network parameters and structural redundancy. To facilitate the application of stereo image super-resolution in downstream tasks, we propose an efficient Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution (MFFSSR). Specifically, MFFSSR utilizes the Hybrid Attention Feature Extraction Block (HAFEB) to extract multi-level intra-view features. Using the channel separation strategy, HAFEB can efficiently interact with the embedded cross-view interaction module. This structural configuration can efficiently mine features inside the view while improving the efficiency of cross-view information sharing. Hence, reconstruct image details and textures more accurately. Abundant experiments demonstrate the effectiveness of MFFSSR. We achieve superior performance with fewer parameters. The source code is available at https://***/KarosLYX/MFFSSR.

关键词： computer vision Source coding conferences Computational modeling Superresolution Redundancy Information sharing

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HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

HyperStyle: StyleGAN Inversion with HyperNetworks for Real I...

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2022 ieee/cvf conference on computer vision and pattern recognition, CVPR 2022

作者： Alaluf, Yuval Tov, Omer Mokady, Ron Gal, Rinon Bermano, Amit Blavatnik School of Computer Science Tel Aviv University Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typically suffer from degraded semantic control. Recent work proposes to mitigate this trade-off by fine-tuning the generator to add the target image to well-behaved, editable regions of the latent space. While promising, this fine-tuning scheme is impractical for prevalent use as it requires a lengthy training phase for each new image. In this work, we introduce this approach into the realm of encoder-based inversion. We propose HyperStyle, a hypernetwork that learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space. A naive modulation approach would require training a hypernetwork with over three billion parameters. Through careful network design, we reduce this to be in line with existing encoders. HyperStyle yields reconstructions comparable to those of optimization techniques with the near real-time inference capabilities of encoders. Lastly, we demonstrate HyperStyle's effectiveness on several applications beyond the inversion task, including the editing of out-of-domain images which were never seen during training. Code is available on our project page: https://***/hyperstyle/. © 2022 ieee.

关键词： Training Semantics Aerospace electronics Generators Robustness Real-time systems pattern recognition

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Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large vision Language Model

Divide and Conquer Boosting for Enhanced Traffic Safety Desc...

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

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City University of Technology VNU-HCM Vietnam Vietnam National University Ho Chi Minh City University of Science VNU-HCM Vietnam University of Information Technology VNU-HCM Vietnam FPT Telecom Vietnam AI Lab - AI Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing complexity of traffic dynamics has underscored the necessity for advanced traffic safety description and analysis, challenging the efficacy of current methodologies in comprehensively understanding and predicting safety conditions from transportation videos. This paper addresses these challenges by analyzing specific segments crucial for precise traffic safety descriptions. Through this examination, we introduce an innovative preprocessing method named "segment extraction", facilitating the creation of a novel segment-based training dataset. Additionally, we present a practical two-stage training framework specifically tailored for this dataset. This framework is designed to produce accurate descriptions of traffic safety by incorporating the unique attributes of our segment-based training datasets. Leveraging these contributions, our method achieved a notable 2nd rank with a score of 32.8877 in the AI City Challenge Track2 test set: Traffic Safety Description and Analysis 2024. The source code for the proposed approaches is openly accessible at https://***/AIVIETNAMResearch/AI-CIty-2024-Track2

关键词： Training Pedestrians Source coding Urban areas Data preprocessing Transportation Data models

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Detecting Underwater Discrete Scatterers in Echograms with Deep Learning-Based Semantic Segmentation

Detecting Underwater Discrete Scatterers in Echograms with D...

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

作者： Rhythm Vohra Femina Senjaliya Melissa Cote Amanda Dash Alexandra Branzan Albu Julek Chawarski Steve Pearce Kaan Ersahin Electrical and Computer Engineering University of Victoria Victoria Canada ASL Environmental Sciences Victoria Canada

This paper reports on an exploratory study of the automatic detection of discrete scatterers in the water column from underwater acoustic data with deep learning (DL) networks. Underwater acoustic surveys using moored singlebeam multi-frequency echosounders make environmental monitoring tasks possible in a non-invasive manner. Discrete scatterers, i.e., individual marine organisms, are particularly challenging to detect automatically due to their small size, sometimes overlapping tracks, and similarity with various types of noise. As our interest lies in identifying the presence and general location of discrete scatterers, we propose the use of a semantic segmentation paradigm over object detection or instance segmentation, and compare several state-of-the-art DL networks. We also study the effects of early and late fusion strategies to aggregate information contained in the multi-frequency data. Experiments on the Okisollo Channel Underwater Discrete Scatterers dataset, which also include schools of herring and juvenile salmon, air bubbles from wave and fish school activity, and significant noise bands, show that late fusion yields higher metrics, with DeepLabV3+ outperforming other networks in terms of precision and intersection over union (IoU) and Attention U-Net offering higher recall. The detection of discrete scatterers is a good example of a problem for which exact annotations cannot be reached due to various reasons; in several cases, network outputs seem visually more adequate than the annotations (which contain inherent noise). This opens up the way for utilizing actual detection results to improve the annotations iteratively.

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Knowledge Distillation for Efficient Instance Semantic Segmentation with Transformers

Knowledge Distillation for Efficient Instance Semantic Segme...

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

作者： Maohui Li Michael Halstead Chris McCool University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Instance-based semantic segmentation provides detailed per-pixel scene understanding information crucial for both computer vision and robotics applications. However, state-of-the-art approaches such as Mask2Former are computationally expensive and reducing this computational burden while maintaining high accuracy remains challenging. Knowledge distillation has been regarded as a potential way to compress neural networks, but to date limited work has explored how to apply this to distill information from the output queries of a model such as *** this paper, we match the output queries of the student and teacher models to enable a query-based knowledge distillation scheme. We independently match the teacher and the student to the groundtruth and use this to define the teacher to student relationship for knowledge distillation. Using this approach we show that it is possible to perform knowledge distillation where the student models can have a lower number of queries and the backbone can be changed from a Transformer architecture to a convolutional neural network architecture. Experiments on two challenging agricultural datasets, sweet pepper (BUP20) and sugar beet (SB20), and Cityscapes demonstrate the efficacy of our approach. Across the three datasets the student models obtain an average absolute performance improvement in AP of 1.8 and 1.9 points for ResNet-50 and Swin-Tiny backbone respectively. To the best of our knowledge, this is the first work to propose knowledge distillation schemes for instance semantic segmentation with transformer-based models.

关键词： Knowledge engineering computer vision Semantic segmentation Computational modeling Impedance matching Neural networks computer architecture

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3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data

3D Kinematics Estimation from Video with a Biomechanical Mod...

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

作者： Zhi-Yi Lin Bofan Lyu Judith Cueto Fernandez Eline van der Kruk Ajay Seth Xucong Zhang Department of Intelligent Systems Computer Vision Lab Delft University of Technology Department of BioMechanical Engineering Faculty of Mechanical Engineering Delft University of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movement. Conventional marker-based motion capture is expensive in terms of financial investment, time, and the expertise required. Moreover, due to the scarcity of datasets with accurate annotations, existing markerless motion capture methods suffer from challenges including unreliable 2D keypoint detection, limited anatomic accuracy, and low generalization capability. In this work, we propose a novel biomechanics-aware network that directly outputs 3D kinematics from two input views with consideration of biomechanical prior and spatio-temporal information. To train the model, we create synthetic dataset ODAH with accurate kinematics annotations generated by aligning the body mesh from the SMPL-X model and a full-body OpenSim skeletal model. Our extensive experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods when evaluated across multiple datasets, revealing a promising direction for enhancing video-based human motion capture.

关键词： Biomechanics Visualization computer vision Accuracy Three-dimensional displays Biological system modeling Estimation

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