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arXiv 2023年

作者： An, Xiaoqi Zhao, Lin Gong, Chen Wang, Nannan Wang, Di Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China

High-resolution representation is essential for achieving good performance in human pose estimation models. To obtain such features, existing works utilize high-resolution input images or fine-grained image tokens. However, this dense high-resolution representation brings a significant computational burden. In this paper, we address the following question: "Only sparse human keypoint locations are detected for human pose estimation, is it really necessary to describe the whole image in a dense, high-resolution manner?" Based on dynamic transformer models, we propose a framework that only uses Sparse High-resolution Representations for human Pose estimation (SHaRPose). In detail, SHaRPose consists of two stages. At the coarse stage, the relations between image regions and keypoints are dynamically mined while a coarse estimation is generated. Then, a quality predictor is applied to decide whether the coarse estimation results should be refined. At the fine stage, SHaRPose builds sparse high-resolution representations only on the regions related to the keypoints and provides refined high-precision human pose estimations. Extensive experiments demonstrate the outstanding performance of the proposed method. Specifically, compared to the state-of-the-art method ViTPose, our model SHaRPose-Base achieves 77.4 AP (+0.5 AP) on the COCO validation set and 76.7 AP (+0.5 AP) on the COCO test-dev set, and infers at a speed of 1.4× faster than ViTPose-Base. Code is available at https://***/AnxQ/sharpose. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Sample-Level Improved Cross-Source Contrastive Learning for PAN and MS Joint Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Tian, Pengyu Zhu, Hao Hou, Biao Chen, Lu Guo, Pute Chen, Kefan Jiao, Licheng Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi’an710071 China

In recent years, the number and ways of acquiring panchromatic images (PAN) and multispectral images (MS) have increased, and manual labeling costs have also increased. Processing these data efficiently has become a challenge. In this paper, we propose a sample-level improved cross-source contrastive learning method for PAN and MS joint classification (SLCL), which aims to provide a self-supervised pre-training model using unlabeled samples for downstream joint classification using a small quantity of labeled samples. First, we propose a sample weighting and screening (SWS) strategy, which enables the model to learn inter- and intra-source sample representations, while balancing the interference from false samples so that the model learns true samples. It solves the problems of homologous similar features embedded far away and false negative samples bringing the wrong learning direction, which exist in existing contrastive learning methods. In addition, we design a hard sample learning (HSL) module for the problem of mining and optimization of hard samples. The module efficiently mines hard samples and uses a new loss function to make the model more focused on hard sample optimization. It further improves the accuracy of pre-training models for downstream tasks. Our method performs best on multiple datasets, and it is experimentally validated and analyzed. © 1980-2012 IEEE.

关键词： Self-supervised learning

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

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IEEE Transactions on Cybernetics 2022年第6期52卷 5408-5418页

作者： Xu, Yesong Chen, Shuo Li, Jun Han, Zongyan Yang, Jian Nanjing University of Science and Technology PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches. © 2013 IEEE.

关键词： Decoding

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Hyperspectral image Classification With Contrastive Graph Convolutional Network

arXiv

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arXiv 2022年

作者： Yu, Wentao Wan, Sheng Li, Guangyu Yang, Jian Gong, Chen The PCA Laboratory The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education The Jiangsu Key Laboratory of Image and Video Understanding for Social Security The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Recently, Graph Convolutional Network (GCN) has been widely used in Hyperspectral image (HSI) classification due to its satisfactory performance. However, the number of labeled pixels is very limited in HSI, and thus the available supervision information is usually insufficient, which will inevitably degrade the representation ability of most existing GCN-based methods. To enhance the feature representation ability, in this paper, a GCN model with contrastive learning is proposed to explore the supervision signals contained in both spectral information and spatial relations, which is termed Contrastive Graph Convolutional Network (ConGCN), for HSI classification. First, in order to mine sufficient supervision signals from spectral information, a semi-supervised contrastive loss function is utilized to maximize the agreement between different views of the same node or the nodes from the same land cover category. Second, to extract the precious yet implicit spatial relations in HSI, a graph generative loss function is leveraged to explore supplementary supervision signals contained in the graph topology. In addition, an adaptive graph augmentation technique is designed to flexibly incorporate the spectral-spatial priors of HSI, which helps facilitate the subsequent contrastive representation learning. The extensive experimental results on four typical benchmark datasets firmly demonstrate the effectiveness of the proposed ConGCN in both qualitative and quantitative aspects. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectroscopy

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RSBNet: One-shot neural architecture search for a backbone network in remote sensing image recognition

arXiv

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arXiv 2021年

作者： Peng, Cheng Li, Yangyang Shang, Ronghua Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education School of Artificial Intelligence Xidian University Xi'an710071 China

Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks. However, most existing advances of deep learning methods in the RSI field heavily rely on the features extracted by the manually designed backbone network, which severely hinders the potential of deep learning models due the complexity of RSI and the limitation of prior knowledge. In this paper, we research a new design paradigm for the backbone architecture in RSI recognition tasks, including scene classification, land-cover classification and object detection. A novel one-shot architecture search framework based on weight-sharing strategy and evolutionary algorithm is proposed, called RSBNet, which consists of three stages: Firstly, a supernet constructed in a layer-wise search space is pretrained on a self-assembled large-scale RSI dataset based on an ensemble single-path training strategy. Next, the pre-trained supernet is equipped with different recognition heads through the switchable recognition module and respectively fine-tuned on the target dataset to obtain task-specific supernet. Finally, we search the optimal backbone architecture for different recognition tasks based on the evolutionary algorithm without any network training. Extensive experiments have been conducted on five benchmark datasets for different recognition tasks, the results show the effectiveness of the proposed search paradigm and demonstrate that the searched backbone is able to flexibly adapt different RSI recognition tasks and achieve impressive performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Dual Residual Network with Channel Attention for image Restoration 16th

A Dual Residual Network with Channel Attention for Image Res...

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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Nie, Shichao Ma, Chengconghui Chen, Dafan Yin, Shuting Wang, Haoran Jiao, LiCheng Liu, Fang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation School of Artificial Intelligence Xidian University Xi’an Shaanxi710071 China

ISBN: (纸本)9783030682378

Deep learning models have achieved significant performance on image restoration task. However, restoring the images with complicated and combined degradation types still remains a challenge. For this purpose, we proposed a dual residual network with channel attention (DRANet) to address complicated degradation in the real world. We further exploit the potential of encoder-decoder structure. To fuse feature more efficiently, we adopt the channel attention module with skip-connections. To better process low- and high-level information, we introduce the dual residual connection into the network architecture. And we explore the effect of multi-level connection to image restoration. Experimental results demonstrate the superiority of our proposed approach over state-of-the-art methods on the UDC T-OLED dataset. © 2020, Springer Nature Switzerland AG.

关键词： image reconstruction

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Adaptive and Background-Aware Match for Class-Agnostic Counting

SSRN

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SSRN 2023年

作者： Gong, Shenjian Yang, Jian Zhang, Shanshan PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Nankai University China

Class-agnostic counting (CAC) aims to count object instances in an image by simply specifying a few exemplar boxes of interest. The key challenge for CAC is how to tailor a desirable interaction between exemplar and query features. Previous CAC methods implement such interaction by solely leveraging standard global feature convolution. We find this interaction leads to under-match caused by intra-class diversity and over-match on background, which severely degrades counting performance. In this work, we propose a novel feature interaction method called Adaptive and Background-Aware Match (ABM) against high intra-class diversity and noisy background. Concretely, given exemplar and query features, we improve the original high-dimensional coupled space match to adaptive orthogonal subspace match (AOM), avoiding under-match caused by intra-class diversity. Moreover, background-specific match (BSM) employs interaction between learnable background prototype and query feature to provide global background priors, making match be aware of background regions. Additionally, we find the high scale variance among different query images leads to bad counting performance for extremely small scale objects. Object scale unify (OSU) is proposed to take the size of the exemplars as the scale prior and resize query images such that all objects are in a uniform average scale. Extensive experiments on FSC-147 show that our method outperforms state-of-the-art approaches. We also conduct extensive ablation studies to demonstrate the effectiveness of each component of our proposed method. The code is publicly available at https://***/Yankeegsj/ABMNet. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition

arXiv

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arXiv 2022年

作者： Li, Yang Chen, Ji Li, Fu Fu, Boxun Wu, Hao Ji, Youshuo Zhou, Yijin Niu, Yi Shi, Guangming Zheng, Wenming The Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education The School of Artificial Intelligence Xidian University Xi’an710071 China School of Biological Sciences and Medical Engineering Southeast University Jiangsu Nanjing210096 China

Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG emotion recognition is proposed. GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks. By learning from multiple tasks simultaneously, GMSS can find a representation that captures all of the tasks thereby decreasing the chance of overfitting on the original task, i.e., emotion recognition task. In particular, the spatial jigsaw puzzle task aims to capture the intrinsic spatial relationships of different brain regions. Considering the importance of frequency information in EEG emotional signals, the goal of the frequency jigsaw puzzle task is to explore the crucial frequency bands for EEG emotion recognition. To further regularize the learned features and encourage the network to learn inherent representations, contrastive learning task is adopted in this work by mapping the transformed data into a common feature space. The performance of the proposed GMSS is compared with several popular unsupervised and supervised methods. Experiments on SEED, SEED-IV, and MPED datasets show that the proposed model has remarkable advantages in learning more discriminative and general features for EEG emotional signals. © 2022, CC BY.

关键词： Electroencephalography

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SBPF: Sensitiveness Based Pruning Framework For Convolutional Neural Network On image Classification

arXiv

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arXiv 2022年

作者： Lu, Yiheng Gong, Maoguo Zhao, Wei Feng, Kai-Yuan Li, Hao State Key Laboratory of Integrated Services Networks School of Computer Science and Technology Xidian University Xi’an710071 China Key Laboratory of Intelligent Perception and Image Understanding School of Electronic Engineering Ministry of Education Xidian University Xi’an710071 China

Pruning techniques are used comprehensively to compress convolutional neural networks (CNNs) on image classification. However, the majority of pruning methods require a well pre-trained model to provide useful supporting parameters, such as C1-norm, BatchNorm value and gradient information, which may lead to inconsistency of filter evaluation if the parameters of the pre-trained model are not well optimized. Therefore, we propose a sensitiveness based method to evaluate the importance of each layer from the perspective of inference accuracy by adding extra damage for the original model. Because the performance of the accuracy is determined by the distribution of parameters across all layers rather than individual parameter, the sensitiveness based method will be robust to update of parameters. Namely, we can obtain similar importance evaluation of each convolutional layer between the imperfect-trained and fully trained models. For VGG-16 on CIFAR-10, even when the original model is only trained with 50 epochs, we can get same evaluation of layer importance as the results when the model is trained fully. Then we will remove filters proportional from each layer by the quantified sensitiveness. Our sensitiveness based pruning framework is verified efficiently on VGG-16, a customized Conv-4 and ResNet-18 with CIFAR-10, MNIST and CIFAR-100, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report

Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challen...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Conde, Marcos V. Kolmet, Manuel Seizinger, Tim Bishop, Tom E. Timofte, Radu Kong, Xiangyu Zhang, Dafeng Wu, Jinlong Wang, Fan Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Cao, Zhiguo Xian, Ke Liu, Chaowei Chen, Zigeng Yang, Xingyi Liu, Songhua Jing, Yongcheng Mi, Michael Bi Wang, Xinchao Yang, Zhihao Lian, Wenyi Lai, Siyuan Zhang, Haichuan Hoang, Trung Yazdani, Amirsaeed Monga, Vishal Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhao, Yuxuan Chen, Baoliang Xu, Yiqing Niu, Jixiang Computer Vision Lab CAIDAS IFI University of Würzburg Germany Glass Imaging Inc. China Huazhong University of Science and Technology China Nanyang Technological University Singapore National University of Singapore Singapore University of Sydney Australia Huawei Uppsala University Sweden Department of Electrical Engineering Pennsylvania State University United States Department of Information Technology Uppsala University Sweden Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi'an China North China University of Technology China

ISBN: (纸本)9798350302493

We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography aim to reach the visual quality of full-frame cameras. Now, a goal in computational photography is to optimize the Bokeh effect itself, which is the aesthetic quality of the blur in out-of-focus areas of an image. Photographers create this aesthetic effect by benefiting from the lens optical *** aim of this work is to design a neural network capable of converting the the Bokeh effect of one lens to the effect of another lens without harming the sharp foreground regions in the image. For a given input image, knowing the target lens type, we render or transform the Bokeh effect accordingly to the lens properties. We build the BETD using two full-frame Sony cameras, and diverse lens *** the best of our knowledge, we are the first attempt to solve this novel task, and we provide the first BETD dataset and benchmark for it. The challenge had 99 registered participants. The submitted methods gauge the state-of-the-art in Bokeh effect rendering and transformation. © 2023 IEEE.

关键词： Color photography

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