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Dataset condensation with latent quantile matching

Dataset condensation with latent quantile matching

引用

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

作者： Wei Wei Tom De Schepper Kevin Mets Department of Computer Science IDLab University of Antwerp - imec Antwerp Belgium IDLab Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dataset condensation (DC) methods aim to learn a smaller, synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn a synthesized dataset by matching the mean of the latent embeddings between the synthetic and the real dataset. However, two distributions with the same mean can still be vastly different. In this work, we demonstrate the shortcomings of using Maximum Mean Discrepancy to match latent distributions, i.e., the weak matching power and lack of outlier regularization. To alleviate these shortcomings, we propose our new method: Latent Quantile Matching (LQM), which matches the quantiles of the latent embeddings to minimize the goodness of fit test statistic between two distributions. Empirical experiments on both image and graph-structured datasets show that LQM matches or outperforms previous state of the art in distribution matching based DC. Moreover, we show that LQM improves the performance in continual graph learning (CGL) setting, where memory efficiency and privacy can be important. Our work sheds light on the application of DM based DC for CGL.

关键词： Training Measurement Data privacy conferences Memory management Machine learning Current distribution

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Recursions Are All You Need: Towards Efficient Deep Unfolding Networks

Recursions Are All You Need: Towards Efficient Deep Unfoldin...

引用

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

作者： Rawwad Alhejaili Motaz Alfarraj Hamzah Luqman Ali Al-Shaikhi Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network. In this work, we propose a novel recursion-based framework to enhance the efficiency of deep unfolding models. First, recursions are used to effectively eliminate the redundancies in deep unfolding networks. Secondly, we randomize the number of recursions during training to decrease the overall training time. Finally, to effectively utilize the power of recursions, we introduce a learnable unit to modulate the features of the model based on both the total number of iterations and the current iteration index. To evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST. Extensive testing shows that our proposed framework allows the network to cut down as much as 75% of its learnable parameters while mostly maintaining its performance, and at the same time, it cuts around 21% and 42% from the training time for ISTA-Net+ and COAST respectively. Moreover, when presented with a limited training dataset, the recursive models match or even outperform their respective non-recursive baseline. Codes and pretrained models are available at https://***/Rawwad-Alhejaili/Recursions-Are-All-You-Need.

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Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation

Bipartite Graph Network with Adaptive Message Passing for Un...

引用

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

作者： Li, Rongjie Zhang, Songyang Wan, Bo He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium

ISBN: (纸本)9781665445092

Scene graph generation is an important visual understanding task with a broad range of vision applications. Despite recent tremendous progress, it remains challenging due to the intrinsic long-tailed class distribution and large intra-class variation. To address these issues, we introduce a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation. In addition, we propose an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training our graph network. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including Visual Genome, Open Images V4/V6, demonstrating its effectiveness and generality.

关键词： Training Visualization computer vision Adaptive systems Message passing Neural networks Genomics

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PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Seg...

引用

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

作者： Douillard, Arthur Chen, Yifu Dapogny, Arnaud Cord, Matthieu Sorbonne Univ Paris France Heuritech Paris France Datakalab Paris France Valeoai Paris France

ISBN: (纸本)9781665445092

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new classes. However, continual learning methods are usually prone to catastrophic forgetting. This issue is further aggravated in CSS where, at each step, old classes from previous iterations are collapsed into the background. In this paper, we propose Local POD, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level. Furthermore, we design an entropy-based pseudo-labelling of the background w.r.t. classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes. Our approach, called PLOP, significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks(1).

关键词： Learning systems computer vision Semantics Benchmark testing Predictive models Market research Stability analysis

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Deep Texture recognition via Exploiting Cross-Layer Statistical Self-Similarity

Deep Texture Recognition via Exploiting Cross-Layer Statisti...

引用

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

作者： Chen, Zhile Li, Feng Quan, Yuhui Xu, Yong Ji, Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Natl Univ Singapore Dept Math Singapore 119076 Singapore

ISBN: (纸本)9781665445092

In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition. The key of existing CNN-based approaches is aggregating the convolutional features into a robust yet discriminative description. This paper presents a novel feature aggregation module called CLASS (Cross-Layer Aggregation of Statistical Self-similarity) for texture recognition. We model the CNN feature maps across different layers, as a dynamic process which carries the statistical self-similarity (SSS), one well-known property of texture, from input image along the network depth dimension. The CLASS module characterizes the cross-layer SSS using a soft histogram of local differential box-counting dimensions of cross-layer features. The resulting descriptor encodes both cross-layer dynamics and local SSS of input image, providing additional discrimination over the often-used global average pooling. Integrating CLASS into a ResNet backbone, we develop CLASSNet, an effective deep model for texture recognition, which shows state-of-the-art performance in the experiments.

关键词： Histograms computer vision Computational modeling Tools pattern recognition Convolutional neural networks

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Depth from Camera Motion and Object Detection

Depth from Camera Motion and Object Detection

引用

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

作者： Griffin, Brent A. Corso, Jason J. Univ Michigan Ann Arbor MI 48109 USA Stevens Inst Artificial Intelligence Hoboken NJ USA

ISBN: (纸本)9781665445092

This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by 1) designing a recurrent neural network (DBox) that estimates the depth of objects using a generalized representation of bounding boxes and uncalibrated camera movement and 2) introducing the Object Depth via Motion and Detection Dataset (ODMD). ODMD training data are extensible and configurable, and the ODMD benchmark includes 21,600 examples across four validation and test sets. These sets include mobile robot experiments using an end-effector camera to locate objects from the YCB dataset and examples with perturbations added to camera motion or bounding box data. In addition to the ODMD benchmark, we evaluate DBox in other monocular application domains, achieving state-of-the-art results on existing driving and robotics benchmarks and estimating the depth of objects using a camera phone.

关键词： Training Perturbation methods Robot kinematics Robot vision systems Training data Object detection Benchmark testing

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Continuous Face Aging via Self-estimated Residual Age Embedding

Continuous Face Aging via Self-estimated Residual Age Embedd...

引用

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

作者： Li, Zeqi Jiang, Ruowei Aarabi, Parham ModiFace Toronto ON Canada

ISBN: (纸本)9781665445092

Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial networks (GANs). Most existing face aging approaches divide the dataset into several age groups and leverage groupbased training strategies, which lacks the ability to provide fine-controlled continuous aging synthesis in nature. In this work, we propose a unified network structure that embeds a linear age estimator into a GAN-based model, where the embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image and provide a personalized target age embedding for age progression/regression. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age, where all preceding aging bases are derived from the learned weights of the linear age estimator. This formulation brings the unified perspective of estimating the age and generating personalized aged face, where self-estimated age embeddings can be learned for every single age. The qualitative and quantitative evaluations on different datasets further demonstrate the significant improvement in the continuous face aging aspect over the state-of-the-art.

关键词： Training computer vision Face recognition Estimation Aging Generative adversarial networks Generators

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Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach

Improving Object Detection to Fisheye Cameras with Open-Voca...

引用

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

作者： Long Hoang Pham Quoc Pham-Nam Ho Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Huy-Hung Nguyen Duong Khac Vu Chi Dai Tran Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Fish-eye cameras have long been employed in traffic surveillance systems to allow for wider observation of the roads. Despite their widespread use, limited computer vision research is tailored explicitly to images captured by fish-eye cameras. The AI City Challenge 2024 - Track 4 introduces a novel fish-eye camera dataset for the 2D road object detection task. This paper proposes a framework designed to detect objects in fish-eye camera images. Our approach involves several key steps: first, we generate image data to bridge the representation gap between day and night images. Next, we leverage zero-shot open vocabulary detection to produce pseudo-labels, aiding in training supervised object detection models. Additionally, we optimize the model’s hyper-parameters and inference configuration for better performance. Finally, we apply various post-processing techniques to enhance detection performance. Our solution achieves a final F1 score of 0.6194 in the AI City Challenge 2024 - Track 4, ranking third among competing teams. The source code is available at GitHub Repo.

关键词： Training computer vision Adaptation models Vocabulary Roads Urban areas Object detection

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Few-shot Open-set recognition by Transformation Consistency

Few-shot Open-set Recognition by Transformation Consistency

引用

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

作者： Jeong, Minki Choi, Seokeon Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples. Since OSR requires rich data and FSL considers closed-set classification, existing OSR and FSL methods show poor performances in solving FSOSR problems. The previous FSOSR method follows the pseudo-unseen class sample-based methods, which collect pseudo-unseen samples from the other dataset or synthesize samples to model unseen class representations. However, this approach is heavily dependent on the composition of the pseudo samples. In this paper, we propose a novel unknown class sample detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set. The modified set is composed by replacing a query feature and its predicted class prototype. SnaTCHer rejects samples with large differences to the transformed prototypes. Our method alters the unseen class distribution estimation problem to a relative feature transformation problem, independent of pseudo-unseen class samples. We investigate our SnaTCHer with various prototype transformation methods and observe that our method consistently improves unseen class sample detection performance without closed-set classification reduction.

关键词： Learning systems computer vision Adaptation models Prototypes Estimation Detectors pattern recognition

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Partial Person Re-identification with Part-Part Correspondence Learning

Partial Person Re-identification with Part-Part Corresponden...

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

作者： He, Tianyu Shen, Xu Huang, Jianqiang Chen, Zhibo Hua, Xian-Sheng Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781665445092

Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID). Nonetheless, most of approaches assume that the input is given with the fulfillment of expectations, while imperfect input remains rarely explored to date, which is a non-trivial problem since directly apply existing methods without adjustment can cause significant performance degradation. In this paper, we focus on recognizing partial (flawed) input with the assistance of proposed Part-Part Correspondence Learning (PPCL), a self-supervised learning framework that learns correspondence between image patches without any additional part-level supervision. Accordingly, we propose Part-Part Cycle (PP-Cycle) constraint and Part-Part Triplet (PP-Triplet) constraint that exploit the duality and uniqueness between corresponding image patches respectively. We verify our proposed PPCL on several partial person re-ID benchmarks. Experimental results demonstrate that our approach can surpass previous methods in terms of the standard evaluation metric.

关键词： Measurement Degradation Deep learning computer vision Image recognition Benchmark testing pattern recognition

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