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Prototype Augmentation and Self-Supervision for Incremental Learning

Prototype Augmentation and Self-Supervision for Incremental ...

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

作者： Zhu, Fei Zhang, Xu-Yao Wang, Chuang Yin, Fei Liu, Cheng-Lin Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9781665445092

Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally. Recently, various incremental learning methods have been proposed, and some approaches achieved acceptable performance relying on stored data or complex generative models. However, storing data from previous tasks is limited by memory or privacy issues, and generative models are usually unstable and inefficient in training. In this paper, we propose a simple non-exemplar based method named PASS, to address the catastrophic forgetting problem in incremental learning. On the one hand, we propose to memorize one class-representative prototype for each old class and adopt prototype augmentation (protoAug) in the deep feature space to maintain the decision boundary of previous tasks. On the other hand, we employ self:supervised learning (SSL) to learn more generalizable and transferable features for other tasks, which demonstrates the effectiveness of SSL in incremental learning. Experimental results on benchmark datasets show that our approach significantly outperforms non-exemplar based methods, and achieves comparable performance compared to exemplar based approaches.

关键词： Training Learning systems Deep learning Data privacy computer vision Computational modeling Prototypes

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Learning Non-target Knowledge for Few-shot Semantic Segmentation

Learning Non-target Knowledge for Few-shot Semantic Segmenta...

引用

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

作者： Liu, Yuanwei Liu, Nian Cao, Qinglong Yao, Xiwen Han, Junwei Shao, Ling Northwestern Polytech Univ Xian Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Terminus Grp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in nontarget regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL-5(i) and COCO-20(i) datasets show that our approach is effective despite its simplicity. Code is available at https://***/LIUYANWEI98/NERTNet

关键词： computer vision Codes Shape Semantics Prototypes Object segmentation Filtering algorithms

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Robust Federated Learning with Noisy and Heterogeneous Clients

Robust Federated Learning with Noisy and Heterogeneous Clien...

引用

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

作者： Fang, Xiuwen Ye, Mang Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Inst Artificial IntelligenceHubei Key Lab Multim Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Model heterogeneous federated learning is a challenging task since each client independently designs its own model. Due to the annotation difficulty and free-riding participant issue, the local client usually contains unavoidable and varying noises, which cannot be effectively addressed by existing algorithms. This paper starts the first attempt to study a new and challenging robust federated learning problem with noisy and heterogeneous clients. We present a novel solution RHFL (Robust Heterogeneous Federated Learning), which simultaneously handles the label noise and performs federated learning in a single framework. It is featured in three aspects: (1) For the communication between heterogeneous models, we directly align the models feedback by utilizing public data, which does not require additional shared global models for collaboration. (2) For internal label noise, we apply a robust noise-tolerant loss function to reduce the negative effects. (3) For challenging noisy feedback from other participants, we design a novel client confidence re-weighting scheme, which adaptively assigns corresponding weights to each client in the collaborative learning stage. Extensive experiments validate the effectiveness of our approach in reducing the negative effects of different noise rates/types under both model homogeneous and heterogeneous federated learning settings, consistently outperforming existing methods.

关键词： Adaptation models Privacy computer vision Computational modeling Collaboration Collaborative work Data models

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Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension 30

Are You Smarter Than A Sixth Grader? Textbook Question Answe...

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30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kembhavi, Aniruddha Seo, Minjoon Schwenk, Dustin Choi, Jonghyun Farhadi, Ali Hajishirzi, Hannaneh Allen Inst Artificial Intelligence Seattle WA 98013 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781538604571

We introduce the task of Multi-Modal Machine Comprehension ((MC)-C-3), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question Answering (TQA) dataset that includes 1,076 lessons and 26,260 multi-modal questions, taken from middle school science curricula. Our analysis shows that a significant portion of questions require complex parsing of the text and the diagrams and reasoning, indicating that our dataset is more complex compared to previous machine comprehension and visual question answering datasets. We extend state-of-the-art methods for textual machine comprehension and visual question answering to the TQA dataset. Our experiments show that these models do not perform well on TQA. The presented dataset opens new challenges for research in question answering and reasoning across multiple modalities.

关键词： Knowledge discovery Visualization Cognition Training Natural languages computer vision

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HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding

HCRF-Flow: Scene Flow from Point Clouds with Continuous High...

引用

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

作者： Li, Ruibo Li, Guosheng He, Tong Li, Fayao Shen, Chunhua Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Adelaide Adelaide SA Australia ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781665445092

Scene flow in 3D point clouds plays an important role in understanding dynamic environments. Although significant advances have been made by deep neural networks, the performance is far from satisfactory as only per-point translational motion is considered, neglecting the constraints of the rigid motion in local regions. To address the issue, we propose to introduce the motion consistency to force the smoothness among neighboring points. In addition, constraints on the rigidity of the local transformation are also added by sharing unique rigid motion parameters for all points within each local region. To this end, a high-order CRFs based relation module (Con-HCRFs) is deployed to explore both point-wise smoothness and region-wise rigidity. To empower the CRFs to have a discriminative unary term, we also introduce a position-aware flow estimation module to be incorporated into the Con-HCRFs. Comprehensive experiments on FlyingThings3D and KITTI show that our proposed framework (HCRF-Flow) achieves state-of-the-art performance and significantly outperforms previous approaches substantially.

关键词： Deep learning computer vision Three-dimensional displays Costs Force Dynamics Estimation

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Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB

Tuning IR-cut Filter for Illumination-aware Spectral Reconst...

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

作者： Sun, Bo Yan, Junchi Zhou, Xiao Zheng, Yinqiang Univ Southern Calif Los Angeles CA 90007 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Anhui Normal Univ Wuhu Peoples R China Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager. It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. To improve accuracy, data-driven algorithms have been proposed to retrieve the best response curves of existing RGB cameras, or even to design brand new three-channel response curves. Instead, this paper explores the filter-array based color imaging mechanism of existing RGB cameras, and proposes to design the IR-cut filter properly for improved spectral recovery, which stands out as an in-between solution with better trade-off between reconstruction accuracy and implementation complexity. We further propose a deep learning based spectral reconstruction method, which allows to recover the illumination spectrum as well. Experiment results with both synthetic and real images under daylight illumination have shown the benefits of our IR-cut filter tuning method and our illumination-aware spectral reconstruction method.

关键词： Reflectivity Deep learning computer vision Lighting Reconstruction algorithms Filtering algorithms Cameras

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DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency

DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-s...

引用

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

作者： Yang, Zongxin Yu, Xin Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D object poses, especially when depth images of scenes are unavailable. This paper investigates whether we can estimate the object poses effectively when only RGB images and 2D object annotations are given. To this end, we present a two-step pose estimation framework to attain 6DoF object poses from 2D object bounding-boxes. In the first step, the framework learns to segment objects from real and synthetic data in a weakly-supervised fashion, and the segmentation masks will act as a prior for pose estimation. In the second step, we design a dual-scale pose estimation network, namely DSC-PoseNet, to predict object poses by employing a differential renderer. To be specific, our DSC-PoseNet firstly predicts object poses in the original image scale by comparing the segmentation masks and the rendered visible object masks. Then, we resize object regions to a fixed scale to estimate poses once again. In this fashion, we eliminate large scale variations and focus on rotation estimation, thus facilitating pose estimation. Moreover, we exploit the initial pose estimation to generate pseudo ground-truth to train our DSC-PoseNet in a self-supervised manner. The estimation results in these two scales are ensembled as our final pose estimation. Extensive experiments on widely-used benchmarks demonstrate that our method outperforms state-of-the-art models trained on synthetic data by a large margin and even is on par with several fully-supervised methods.

关键词： Training Image segmentation computer vision Three-dimensional displays Annotations Pose estimation Benchmark testing

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Energy-based Latent Aligner for Incremental Learning

Energy-based Latent Aligner for Incremental Learning

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

作者： Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Anwer, Rao Muhammad Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden Aalto Univ Espoo Finland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks may not align well with the updates suitable for older tasks. The resulting latent representation mismatch causes forgetting. In this work, we propose ELI: Energy-based Latent Aligner for Incremental Learning, which first learns an energy manifold for the latent representations such that previous task latents will have low energy and the current task latents have high energy values. This learned manifold is used to counter the representational shift that happens during incremental learning. The implicit regularization that is offered by our proposed methodology can be used as a plug-and-play module in existing incremental learning methodologies. We validate this through extensive evaluation on CIFAR-100, ImageNet subset, ImageNet 1k and Pascal VOC datasets. We observe consistent improvement when ELI is added to three prominent methodologies in class-incremental learning, across multiple incremental settings. Further, when added to the state-of-the-art incremental object detector, ELI provides over 5% improvement in detection accuracy, corroborating its effectiveness and complementary advantage to the existing art. Code is available at: https://***/JosephKJ/ELI.

关键词： Manifolds Deep learning computer vision Codes Pipelines Object detection Detectors

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Track, Check, Repeat: An EM Approach to Unsupervised Tracking

Track, Check, Repeat: An EM Approach to Unsupervised Trackin...

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

作者： Harley, Adam W. Zuo, Yiming Wen, Jing Mangal, Ayush Potdar, Shubhankar Chaudhry, Ritwick Fragkiadaki, Katerina Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We propose an unsupervised method for detecting and tracking moving objects in 3D, in unlabelled RGB-D videos. The method begins with classic handcrafted techniques for segmenting objects using motion cues: we estimate optical flow and camera motion, and conservatively segment regions that appear to be moving independently of the background. Treating these initial segments as pseudo-labels, we learn an ensemble of appearance-based 2D and 3D detectors, under heavy data augmentation. We use this ensemble to detect new instances of the "moving" type, even if they are not moving, and add these as new pseudo-labels. Our method is an expectation-maximization algorithm, where in the expectation step we fire all modules and look for agreement among them, and in the maximization step we re-train the modules to improve this agreement. The constraint of ensemble agreement helps combat contamination of the generated pseudo-labels (during the E step), and data augmentation helps the modules generalize to yetunlabelled data (during the M step). We compare against existing unsupervised object discovery and tracking methods, using challenging videos from CATER and KITTI, and show strong improvements over the state-of-the-art.

关键词： Training Three-dimensional displays Motion segmentation Detectors pattern recognition Object tracking Expectation-maximization algorithms

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GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning 31

GraphBit: Bitwise Interaction Mining via Deep Reinforcement ...

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Duan, Yueqi Wang, Ziwei Lu, Jiwen Lin, Xudong Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China State Key Lab Intelligent Technol & Syst Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781538664209

In this paper, we propose a GraphBit method to learn deep binary descriptors in a directed acyclic graph unsupervisedly, representing bitwise interactions as edges between the nodes of bits. Conventional binary representation learning methods enforce each element to be binarized into zero or one. However, there are elements lying in the boundary which suffer from doubtful binarization as "ambiguous bits". Ambiguous bits fail to collect effective information for confident binarization, which are unreliable and sensitive to noise. We argue that there are implicit inner relationships between bits in binary descriptors, where the related bits can provide extra instruction as prior knowledge for ambiguity elimination. Specifically, we design a deep reinforcement learning model to learn the structure of the graph for bitwise interaction mining, reducing the uncertainty of binary codes by maximizing the mutual information with inputs and related bits, so that the ambiguous bits receive additional instruction from the graph for confident binarization. Due to the reliability of the proposed binary codes with bitwise interaction, we obtain an average improvement of 9.64%, 8.84% and 3.22% on the CIFAR-10, Brown and HPatches datasets respectively compared with the state-of-the-art unsupervised binary descriptors.

关键词： Reliability Binary codes computer vision Linear programming Training Mutual information

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