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Cache and Reuse: Rethinking the Efficiency of On-device Transfer Learning

Cache and Reuse: Rethinking the Efficiency of On-device Tran...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yuedong Yang Hung-Yueh Chiang Guihong Li Diana Marculescu Radu Marculescu Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Training only the last few layers in deep neural networks has been considered an effective strategy for enhancing the efficiency of on-device training. Prior work has adopted this approach and focused on accelerating backpropagation. However, by conducting a thorough system-wide analysis, we discover that the primary bottleneck is actually the forward propagation through the frozen layers, rather than backpropagation, if only the last few layers are trained. To address this issue, we introduce the "cache and reuse" idea for on-device transfer learning and propose a two-stage training method, which consists of a cache initialization stage, where we store the output from the frozen layers, followed by a training stage. To make our approach practical, we also propose augmented feature caching and cache compression to address the challenges of non-cacheable feature maps and cache size explosion. We carry out extensive experiments on various models (e.g., convolutional neural network and vision transformers) using real edge devices to demonstrate the effectiveness of our method. As an example, on NVIDIA Jetson Orin NX with MobileNet-V2, our approach boosts the training speed by 6.6 ×, and improves the accuracy by 2.1%. For EfficientNet-b0, our method increases the training speed by 2.2 × and improves its accuracy by 1.3%. Therefore, our approach represents a significant improvement in enabling practical on-device transfer learning for edge devices with limited resources.

关键词： Training Backpropagation computer vision Accuracy conferences Transfer learning Transformers

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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...

引用

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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Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment

Cali-NCE: Boosting Cross-modal Video Representation Learning...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nanxuan Zhao Jianbo Jiao Weidi Xie Dahua Lin Department of Computer Science University of Bath School of Computer Science University of Birmingham Cooperative Medianet Innovation Center Shanghai Jiaotong University Department of Information Engineering Chinese University of Hong Kong

With the large-scale video-text datasets being collected, learning general visual-textual representation has gained increasing attention. While recent methods are designed with the assumption that the alt-text description naturally conveys the meaning and context of the video in semantics (i.e. well aligned with each other), it is unlikely to be satisfied for the Internet data, which potentially harms the quality of the learned visual-textual representation. To address this challenge, we first revisit three mainstream approaches: correspondence modeling, contrastive learning and predictive coding, demonstrating that a simple co-training strategy with these methods leads to a clear improvement in performance. To further explore the complementary nature of different training strategies, we propose a simple yet effective joint training framework that factorizes the total objective into conditional ones, termed as Cali-NCE 1 . Our method first estimates confidence scores for measuring the correspondence between video and text descriptions, and the scores are later used to calibrate the sample weightings during contrastive training. Through extensive experiments, we show that the proposed approach achieves state-of-the-art performance on multiple downstream tasks: text-to-video retrieval, video action recognition, and video retrieval.

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Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery

Beyond Appearances: Material Segmentation with Embedded Spec...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Fabian Perez Hoover Rueda-Chacón Universidad Industrial de Santander

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the practical constraints of data acquisition. Our approach leverages a training strategy that employs a paired RGBD-spectral data to incorporate spectral information directly within the neural network. This encoding process is facilitated by a Spectral Feature Mapper (SFM) layer, a novel module that embeds unique spectral characteristics into the network, thus enabling the network to infer materials from standard RGB-D images. Once trained, the model allows to conduct material segmentation on widely available devices without the need for direct spectral data input. In addition, we generate the 3D point cloud from the RGB-D image pair, to provide a richer spatial context for scene understanding. Through simulations using available datasets, and real experiments conducted with an iPad Pro, our method demonstrates superior performance in material segmentation compared to other methods. Code is available at: https://***/Factral/Spectral-material-segmentation

关键词： Deep learning Training Image segmentation computer vision Visualization Three-dimensional displays Lighting

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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： Image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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Masked vision Transformers for Hyperspectral Image Classification

Masked Vision Transformers for Hyperspectral Image Classific...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Linus Scheibenreif Michael Mommert Damian Borth AIML Lab School of Computer Science University of St. Gallen

Transformer architectures have become state-of-the-art models in computer vision and natural language processing. To a significant degree, their success can be attributed to self-supervised pre-training on large scale unlabeled datasets. This work investigates the use of self-supervised masked image reconstruction to advance transformer models for hyperspectral remote sensing imagery. To facilitate self-supervised pre-training, we build a large dataset of unlabeled hyperspectral observations from the EnMAP satellite and systematically investigate modifications of the vision transformer architecture to optimally leverage the characteristics of hyperspectral data. We find significant improvements in accuracy on different land cover classification tasks over both standard vision and sequence transformers using (i) blockwise patch embeddings, (ii) spatialspectral self-attention, (iii) spectral positional embeddings and (iv) masked self-supervised pre-training 1 . The resulting model outperforms standard transformer architectures by +5% accuracy on a labeled subset of our EnMAP data and by +15% on Houston2018 hyperspectral dataset, making it competitive with a strong 3D convolutional neural network baseline. In an ablation study on label-efficiency based on the Houston2018 dataset, self-supervised pre-training significantly improves transformer accuracy when little labeled training data is available. The self-supervised model outperforms randomly initialized transformers and the 3D convolutional neural network by +7-8% when only 0.1-10% of the training labels are available.

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Semi-Stereo: A Universal Stereo Matching Framework for Imperfect Data via Semi-supervised Learning

Semi-Stereo: A Universal Stereo Matching Framework for Imper...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xin Yue Zongqing Lu Xiangru Lin Wenjia Ren Zhijing Shao Haonan Hu Yu Zhang Qingmin Liao Tsinghua University Prometheus Vision Technology Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data matters in deep-learning-based binocular stereo matching. Obtaining a perfect dataset for stereo matching is hard and thus imperfect data is common in existing benchmark datasets, such as KITTI, ETH3D and Middlebury. The imperfectness typically has two forms: sparse-labeled data or even unlabeled data. Current stereo matching networks ignore the supervision from these imperfect data itself, even the semi-supervised networks often suffer from confirmation bias in the predictions. Besides, current methods lack a unified solution to utilize the supervision signal from those imperfect data. To mitigate this research gap, we propose Semi-Stereo, the first unified stereo matching framework empowered by the teacher-student paradigm where the teacher and the student networks are trained in a mutual-beneficial manner. To explore the rich knowledge in imperfect data, we propose a consistency regularization module with weak-strong augmentation strategies. Further, in order for the teacher to provide more reliable pseudo labels, we design a confidence module, powered by left-right consistency (LRC) check and disparity distribution entropy (DDE). Extensive experiments demonstrate Semi-Stereo produces accurate and consistent predictions in untrained semantic regions and improves the performance of baseline networks in multiple tasks, including domain adaptation and domain generalization.

关键词： Measurement computer vision conferences computer network reliability Semantics Semisupervised learning Benchmark testing

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OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

OpenFed: A Comprehensive and Versatile Open-Source Federated...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dengsheng Chen Vince Junkai Tan Zhilin Lu Enhua Wu Jie Hu Meituan Bytedance Inc. Tsinghua University University of Macau State Key Lab of Computer Science ISCAS University of Chinese Academy of Sciences

Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plugged and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning. The source code of OpenFed is publicly available online at https://***/FederalLab/OpenFed.

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Multi-camera People Tracking With Mixture of Realistic and Synthetic Knowledge

Multi-camera People Tracking With Mixture of Realistic and S...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Quang Qui-Vinh Nguyen Huy Dinh-Anh Le Truc Thi-Thanh Chau Duc Trung Luu Nhat Minh Chung Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

This paper presents a solution for Track 1 of the AI City Challenge 2023, which involves Multi-Camera People Tracking in indoor scenarios. The proposed framework comprises four modules: Vehicle detection, ReID feature extraction, single-camera multi-target tracking (SCMT), single-camera matching, and multi-camera matching. A significant contribution of our approach is the introduction of ID switch detection and ID switch splitting using the Gaussian mixture model, which efficiently addresses the problem of tracklets with ID switches. Furthermore, our system performs well in matching both synthetic and real data. The proposed R-matching algorithm performs exceptionally well in real scenarios despite being trained on synthetic data. Experimental results on the public test set of 2023 AI City Challenge Track 1 demonstrate the efficacy of the proposed approach, achieving an IDF1 of 94.17% and securing 2nd position on the leaderboard. Codes will be available at https://***/nguyenquivinhquang/Multi-camera-People-Tracking-With-Mixture-of-Realistic-and-Synthetic-Knowledge

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XoFTR: Cross-modal Feature Matching Transformer

XoFTR: Cross-modal Feature Matching Transformer

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Önder Tuzcuoğlu Aybora Köksal Buğra Sofu Sinan Kalkan A. Aydın Alatan Dept. of Electrical and Electronics Eng. Center for Image Analysis Middle East Technical University Ankara Turkey ROKETSAN Inc. Ankara Turkey Dept. of Computer Eng. Center for Image Analysis Middle East Technical University Ankara Turkey

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images. Unlike visible images, TIR images are less susceptible to adverse lighting and weather conditions but present difficulties in matching due to significant texture and intensity differences. Current hand-crafted and learning-based methods for visible-TIR matching fall short in handling viewpoint, scale, and texture diversities. To address this, XoFTR incorporates masked image modeling pre-training and fine-tuning with pseudo-thermal image augmentation to handle the modality differences. Additionally, we introduce a refined matching pipeline that adjusts for scale discrepancies and enhances match reliability through sub-pixel level refinement. To validate our approach, we collect a comprehensive visible-thermal dataset, and show that our method outperforms existing methods on many benchmarks. Code and dataset at https://***/OnderT/XoFTR.

关键词： Learning systems Image matching Pipelines Lighting Benchmark testing Transformers Image augmentation

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