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Unlocking the Full Potential of Small Data with Diverse Supervision

Unlocking the Full Potential of Small Data with Diverse Supe...

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

作者： Pang, Ziqi Hu, Zhiyuan Tokmakov, Pavel Wang, Yu-Xiong Hebert, Martial TuSimple Beijing Peoples R China Univ Calif San Diego San Diego CA USA Toyota Res Inst Ann Arbor MI USA UIUC Champaign IL USA CMU Pittsburgh PA USA

ISBN: (纸本)9781665448994

Virtually all of deep learning literature relies on the assumption of large amounts of available training data. Indeed, even the majority of few-shot learning methods rely on a large set of "base classes" for pre-training. This assumption, however, does not always hold. For some tasks, annotating a large number of classes can be infeasible, and even collecting the images themselves can be a challenge in some scenarios. In this paper, we study this problem and call it "Small Data" setting, in contrast to "Big Data." To unlock the full potential of small data, we propose to augment the models with annotations for other related tasks, thus increasing their generalization abilities. In particular, we use the richly annotated scene parsing dataset ADE20K to construct our realistic Long-tail recognition with Diverse Supervision (LRDS) benchmark, by splitting the object categories into head and tail based on their distribution. Following the standard few-shot learning protocol, we use the head classes for representation learning and the tail classes for evaluation. Moreover, we further subsample the head categories and images to generate two novel settings which we call "Scarce-Class" and "Scarce-Image," respectively corresponding to the shortage of training classes and images. Finally, we analyze the effect of applying various additional supervision sources under the proposed settings. Our experiments demonstrate that densely labeling a small set of images can indeed largely remedy the small data constraints. Our code and benchmark are available at https://***/BinahHu/ADE-FewShot.

关键词： Training Learning systems Head Protocols Training data Benchmark testing pattern recognition

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SafeSO: Interpretable and Explainable Deep Learning Approach for Seat Occupancy Classification in Vehicle Interior

SafeSO: Interpretable and Explainable Deep Learning Approach...

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

作者： Jaworek-Korjakowska, Joanna Kostuch, Aleksander Skruch, Pawel AGH Univ Sci & Technol Dept Automat Control & Robot Krakow Poland

ISBN: (纸本)9781665448994

Classification of seat occupancy in in-vehicle interior remains a significant challenge and is a promising area in the functionality of new generation cars. As majority of accidents are related to the driver errors the consequences of not wearing, or improperly wearing, a seat belt are clear. The NHTSA reports that 47% of the 22,215 passenger vehicle occupants killed in 2019 were not wearing seat belts. To address this problem we propose a deep learning based framework to classify seat occupancy into seven most important categories. In this study, we present an interpretable and explainable AI approach that takes advantage of pre-trained networks including ResNet152V2, DenseNet121 and the most recent EfficientNetB0-B5-B7 to calculate the feature vectors followed by an adjusted densely-connected classifier. Our model provides an interpretation of its results through the identification of object parts without direct supervision and their contribution towards classification. We explore and propose two new statistical metrics including HGD(score) and HGDA(score) which are based on the multivariate Gaussian distribution for assessing heatmaps without using human-annotated object parts to quantify the interpretability of our network. We demonstrate that the calculated statistical metrics lead to an interpretable model that correlates with the framework accuracy and can flexibly analyze heatmaps at any resolution for different user needs. Furthermore, extensive experiments have been performed on the SVIRO database [7] including 7,500 sceneries for BMW X5 model which confirm the ability of the developed framework based on the EfficientNetB5 architecture to classify seat occupancy into seven main categories with 79.87% overall accuracy as well as 95.92% recall and 90.32% specificity for empty seats recognition, which is a state-of-the-art result in this domain.

关键词： Measurement Heating systems Deep learning Visualization computer architecture Belts Object recognition

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Avalanche: an End-to-End Library for Continual Learning

Avalanche: an End-to-End Library for Continual Learning

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

作者： Lomonaco, Vincenzo Pellegrini, Lorenzo Cossu, Andrea Carta, Antonio Graffieti, Gabriele Hayes, Tyler L. De Lange, Matthias Masana, Marc Pomponi, Jary Van de Ven, Gido M. Mundt, Martin She, Qi Cooper, Keiland Forest, Jeremy Belouadah, Eden Calderara, Simone Parisi, German, I Cuzzolin, Fabio Tolias, Andreas S. Scardapane, Simone Antiga, Luca Ahmad, Subutai Popescu, Adrian Kanan, Christopher Van de Weijer, Joost Tuytelaars, Tinne Bacciu, Davide Maltoni, Davide Univ Pisa Pisa Italy Univ Bologna Bologna Italy Rochester Inst Technol Rochester NY 14623 USA Katholieke Univ Leuven Leuven Belgium Univ Autonoma Barcelona Barcelona Spain Sapienza Univ Rome Rome Italy Baylor Coll Med Houston TX 77030 USA Goethe Univ Frankfurt Germany ByteDance AI Lab Beijing Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA NYU New York NY USA Univ Paris Saclay Paris France Univ Modena & Reggio Emilia Modena Italy Univ Hamburg Hamburg Germany Oxford Brookes Univ Oxford England Orobix Bergamo Italy Numenta Redwood City CA USA Scuola Normale Super Pisa Pisa Italy

ISBN: (纸本)9781665448994

Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.

关键词： Training Deep learning computer vision Machine learning algorithms conferences Collaboration Libraries

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Video-based Person Re-identification without Bells and Whistles

Video-based Person Re-identification without Bells and Whist...

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

作者： Liu, Chih-Ting Chen, Jun-Cheng Chen, Chu-Song Chien, Shao-Yi Natl Taiwan Univ Grad Inst Elect & Engn Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781665448994

Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. However, there exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. To address this issue, we present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets. Furthermore, we introduce an improved model called Coarse-to-Fine Axial-Attention Network (CF-AAN). Based on the typical Nonlocal Network, we replace the non-local module with three 1-D position-sensitive axial attentions, in addition to our proposed coarse-to-fine structure. With the developed CF-AAN, compared to the original non-local operation, we can not only significantly reduce the computation cost but also obtain the state-of-the-art performance (91:3% in rank-1 and 86:5% in mAP) on the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for data alignment, to our surprise, several baseline models can achieve better or comparable results with the current state-of-the-arts. Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS. We hope that our work can help the community for the further development of invariant representation without the hassle of the spatial and temporal alignment and dataset noise. The code, corrected labels, evaluation protocol, and the aligned data will be available at https://***/jackie840129/CF-AAN.

关键词： computer vision Protocols Computational modeling conferences Lighting Cameras Data models

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EFI-Net: Video Frame Interpolation from Fusion of Events and Frames

EFI-Net: Video Frame Interpolation from Fusion of Events and...

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

作者： Paikin, Genady Ater, Yotam Shaul, Roy Soloveichik, Evgeny Samsung Israel R&D Ctr Tel Aviv Israel

ISBN: (纸本)9781665448994

Event cameras are sensors with pixels that respond independently and asynchronously to changes in scene illumination. Event cameras have a number of advantages when compared to conventional cameras: low-latency, high temporal resolution, high dynamic range, low power and sparse data output. However, existing event cameras also suffer from comparatively low spatial resolution and are sensitive to noise. Recently, it has been shown that it is possible to reconstruct an intensity frame stream from an event stream. These reconstructions preserve the high temporal rate of the event stream, but tend to suffer from significant artifacts and low image quality due to the shortcomings of event cameras. In this work we demonstrate that it is possible to combine the best of both worlds, by fusing a color frame stream at low temporal resolution and high spatial resolution with an event stream at high temporal resolution and low spatial resolution to generate a video stream with both high temporal and spatial resolutions while preserving the original color information. We utilize a novel event frame interpolation network (EFI-Net), a multi-phase convolutional neural network which fuses the frame and event streams. EFI-Net is trained using only simulated data and generalizes exceptionally well to real-world experimental data. We show that our method is able to interpolate frames where traditional video interpolation approaches fail, while also outperforming event-only reconstructions. We further contribute a new dataset, containing event camera data synchronized with high speed video. This work opens the door to a new application for event cameras, enabling high fidelity fusion with frame based image streams for generation of high-quality high-speed video. The dataset is available at https://***/file/d/1UIGVBqNER_5KguYPAu5y7TVg-JlNhz3-/view?usp=sharing [GRAPHICS] .

关键词： Interpolation Image color analysis Lighting Streaming media Cameras Sensors pattern recognition

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Learned Image Compression with Mixed Transformer-CNN Architectures

Learned Image Compression with Mixed Transformer-CNN Archite...

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conference on computer vision and pattern recognition (cvpr)

作者： Jinming Liu Heming Sun Jiro Katto Department of Computer Science and Communication Engineering Waseda University Tokyo Japan Shanghai Jiao Tong University Shanghai China Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan JST PRESTO Kawaguchi Saitama Japan

Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages. Exploiting both advantages is a point worth exploring, which has two challenges: 1) how to effectively fuse the two methods? 2) how to achieve higher performance with a suitable complexity? In this paper, we propose an efficient parallel Transformer-CNN Mixture (TCM) block with a controllable complexity to incorporate the local modeling ability of CNN and the non-local modeling ability of transformers to improve the overall architecture of image compression models. Besides, inspired by the recent progress of entropy estimation models and attention modules, we propose a channel-wise entropy model with parameter-efficient swin-transformer-based attention (SWAtten) modules by using channel squeezing. Experimental results demonstrate our proposed method achieves state-of-the-art rate-distortion performances on three different resolution datasets (i.e., Kodak, Tecnick, CLIC Professional Validation) compared to existing LIC methods. The code is at https://***/jmliu206/LIC_TCM.

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Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report

Fast and Accurate Quantized Camera Scene Detection on Smartp...

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

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Chen, Sheng Xia, Xin Liu, Zhaoyan Zhang, Yuwei Zhu, Feng Li, Jiashi Xiao, Xuefeng Tian, Yuan Wu, Xinglong Kyrkou, Christos Chen, Yixin Zhang, Zexin Peng, Yunbo Lin, Yue Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Kumar, Himanshu Ge, Chao Wu, Pei-Lin Du, Jin-Hua Batutin, Andrew Federico, Juan Pablo Lyda, Konrad Khojoyan, Levon Thanki, Abhishek Paul, Sayak Siddiqui, Shahid Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland AI Witchlabs Lausanne Switzerland ByteDance Inc Beijing Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence Nicosia Cyprus Netease Games AI Lab Beijing Peoples R China Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India Indian Inst Technol Jodhpur Karwar India Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China DataArt Inc New York NY USA PyImageSearch Mumbai Maharashtra India Univ Cyprus KIOS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781665448994

Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms. For this, the participants were provided with a large-scale CamSDD dataset consisting of more than 11K images belonging to the 30 most important scene categories. The runtime of all models was evaluated on the popular Apple Bionic A11 platform that can be found in many iOS devices. The proposed solutions are fully compatible with all major mobile AI accelerators and can demonstrate more than 100-200 FPS on the majority of recent smartphone platforms while achieving a top-3 accuracy of more than 98%. A detailed description of all models developed in the challenge is provided in this paper.

关键词： computer vision Runtime Image analysis Computational modeling conferences Cameras Real-time systems

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Proceedings - 2021 ieee/CVF conference on computer vision and pattern recognition, cvpr 2021

Proceedings - 2021 IEEE/CVF Conference on Computer Vision an...

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2021 ieee/CVF conference on computer vision and pattern recognition, cvpr 2021

ISBN: (纸本)9781665445092

The proceedings contain 1658 papers. The topics discussed include: single-stage instance shadow detection with bidirectional relation learning;learning Delaunay surface elements for mesh reconstruction;fusing the old with the new: learning relative camera pose with geometry-guided uncertainty;uncertainty guided collaborative training for weakly supervised temporal action detection;privacy-preserving collaborative learning with automatic transformation search;rethinking and improving the robustness of image style transfer;style-aware normalized loss for improving arbitrary style transfer;faster meta update strategy for noise-robust deep learning;a hyperbolic-to-hyperbolic graph convolutional network;training networks in null space of feature covariance for continual learning;and exponential moving average normalization for self-supervised and semi-supervised learning.

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Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping

Byzantine-robust Decentralized Federated Learning via Dual-d...

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conference on computer vision and pattern recognition (cvpr)

作者： Peng Sun Xinyang Liu Zhibo Wang Bo Liu College of Computer Science and Electronic Engineering Hunan University China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University China School of Cyber Science and Technology Zhejiang University China Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Decentralized federated learning (DFL) facilitates collaborative model training across multiple connected clients without a central coordination server, thereby avoiding the single point of failure in traditional centralized federated learning (CFL). However, DFL exhibits increased susceptibility to Byzantine attacks owing to the lack of a responsible central server. Furthermore, a benign client in DFL may be dominated by Byzantine clients (more than half of its neighbors are malicious), posing significant challenges for robust model training. In this work, we propose DFL-Dual, a novel Byzantine-robust DFL method through dual-domain client clustering and trust bootstrapping. Specifically, we first propose to leverage both data-domain and model-domain distance metrics to identify client discrepancies. Then, we design a trust evaluation mechanism centered on benign clients, which enables them to evaluate their neighbors. Building upon the dual-domain distance metric and trust evaluation mechanism, we further develop a two-stage clustering and trust bootstrapping technique to exclude Byzantine clients from local model aggregation. We extensively evaluate the proposed DFL-Dual method through rigorous experimentation, demonstrating its remarkable performance superiority over existing robust CFL and DFL schemes.

关键词： Measurement Training computer vision Federated learning Buildings Collaboration Euclidean distance

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PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling

PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fideli...

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conference on computer vision and pattern recognition (cvpr)

作者： Xiaoyun Zheng Liwei Liao Xufeng Li Jianbo Jiao Rongjie Wang Feng Gao Shiqi Wang Ronggang Wang Peking University Shenzhen Graduate School Peng Cheng Laboratory City University of Hong Kong University of Birmingham Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

High-quality human reconstruction and photo-realistic rendering of a dynamic scene is a long-standing problem in computer vision and graphics. Despite considerable ef-forts invested in developing various capture systems and re-construction algorithms, recent advancements still struggle with loose or oversized clothing and overly complex poses. In part, this is due to the challenges of acquiring high-quality human datasets. To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos. It comprises 8.2 million frames captured by more than 56 synchronized cameras across diverse scenarios. These sequences comprise 32 human subjects across 45 different scenarios, each with a high-detailed appearance and realistic human motion. Inspired by recent advancements in neural radiance field (NeRF)-based scene representations, we carefully set up an off-the-shelf framework that is easy to provide those state-of-the-art NeRF-based implementations and benchmark on PKU-DyMVHumans dataset. It is paving the way for various applications like fine-grained foreground/background de-composition, high-quality human reconstruction and photo-realistic novel view synthesis of a dynamic scene. Exten-sive studies are performed on the benchmark, demonstrating new observations and challenges that emerge from using such high-fidelity dynamic data. The project page and data is available at: https://***.

关键词： computer vision Heuristic algorithms Dynamics Lighting Benchmark testing Rendering (computer graphics) Neural radiance field

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