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An adaptive framework to select the coordinate systems for evolutionary algorithms

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Gao, Weifeng Dang, Qianlong Gong, Maoguo Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Xidian Univ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Perception & Image Understand Minist Educ Xian 710071 Peoples R China

Many evolutionary algorithms usually utilize the fixed original coordinate system to search and cannot effectively match different function landscapes. To solve this issue, this paper proposes an adaptive framework, named STCS, to select the coordinate systems for evolutionary algorithms. In STCS, the eigen coordinate system is constructed by an archive-based covariance matrix, which can capture the feature of the function landscape. What is more, the selection process of the original coordinate system and the eigen coordinate system is defined as a Markov decision process and is controlled by reinforcement learning algorithm. STCS is applied to three popular evolutionary algorithms, i.e., differential evolution, particle swarm optimization, and teaching-learning-based optimization. The experimental results on IEEE CEC2013, IEEE CEC2014, and IEEE CEC2017 test suites demonstrate that STCS is efficient and competitive. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Evolutionary algorithms Reinforcement learning Differential evolution Particle swarm optimization Teaching-learning-based optimization

Background Semantics Matter: Cross-Task Feature Exchange Network for Clustered Infrared Small Target Detection With Sky-Annotated Dataset

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arXiv 2024年

作者： Xiao, Mengxuan Dai, Qun Zhu, Yiming Guo, Kehua Wang, Huan Shu, Xiangbo Yang, Jian Dai, Yimian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China PCALab 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 China School of Computer Science and Engineering Central South University China

Infrared small target detection poses unique challenges due to the scarcity of intrinsic target features and the abundance of similar background distractors. We argue that background semantics play a pivotal role in distinguishing visually similar objects for this task. To address this, we introduce a new task-clustered infrared small target detection, and present DenseSIRST, a novel benchmark dataset that provides per-pixel semantic annotations for background regions, enabling the transition from sparse to dense target detection. Leveraging this dataset, we propose the Background-Aware Feature Exchange Network (BAFE-Net), which transforms the detection paradigm from a single task focused on the foreground to a multi-task architecture that jointly performs target detection and background semantic segmentation. BAFE-Net introduces a dynamic cross-task feature hard-exchange mechanism to embed target and background semantics between the two tasks. Furthermore, we propose the Background-Aware Gaussian Copy-Paste (BAG-CP) method, which selectively pastes small targets into sky regions during training, avoiding the creation of false alarm targets in complex non-sky backgrounds. Extensive experiments validate the effectiveness of BAG-CP and BAFE-Net in improving target detection accuracy while reducing false alarms. The DenseSIRST dataset, code, and trained models are available at https://***/GrokCV/BAFE-Net. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

Adaptive and Background-Aware Match for Class-Agnostic Counting

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SSRN

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

Semi-Supervised image Captioning Considering Wasserstein Graph Matching

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arXiv 2024年

作者： Yang, Yang The Nanjing University of Science and Technology Nanjing210094 China 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 Ministry of Education State Key Lab. for Novel Software Technology Nanjing University China

image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that describing images always requires a huge of manpower, we usually have limited amount of described images (i.e., image-text pairs) and a large number of undescribed images in real-world applications. Thereby, a dilemma is the "Semi-Supervised image Captioning". To solve this problem, we propose a novel Semi-Supervised image Captioning method considering Wasserstein Graph Matching (SSIC-WGM), which turns to adopt the raw image inputs to supervise the generated sentences. Different from traditional single modal semi-supervised methods, the difficulty of semi-supervised cross-modal learning lies in constructing intermediately comparable information among heterogeneous modalities. In this paper, SSIC-WGM adopts the successful scene graphs as intermediate information, and constrains the generated sentences from two aspects: 1) inter-modal consistency. SSIC-WGM constructs the scene graphs of the raw image and generated sentence respectively, then employs the wasserstein distance to better measure the similarity between region embeddings of different graphs. 2) intra-modal consistency. SSIC-WGM takes the data augmentation techniques for the raw images, then constrains the consistency among augmented images and generated sentences. Consequently, SSIC-WGM combines the cross-modal pseudo supervision and structure invariant measure for efficiently using the undescribed images, and learns more reasonable mapping function. Experiments show that our method can outperform state-of-the-art comparison methods on the MS-COCO "Karpathy" offline test split, under different complex semi-supervised scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation

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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

Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

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Clothed Human Performance Capture with a Double-layer Neural...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kangkan Wang Guofeng Zhang Suxu Cong Jian Yang 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 CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFsfor the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos.

关键词：

Self-Supervised 3D Scene Flow Estimation Guided by Superpoints

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Self-Supervised 3D Scene Flow Estimation Guided by Superpoin...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yaqi Shen Le Hui Jin Xie Jian Yang PCA Lab Nanjing University of Science and Technology Nanjing China 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

3D scene flow estimation aims to estimate point-wise motions between two consecutive frames of point clouds. Superpoints, i.e., points with similar geometric features, are usually employed to capture similar motions of local regions in 3D scenes for scene flow estimation. However, in existing methods, superpoints are generated with the offline clustering methods, which cannot characterize local regions with similar motions for complex 3D scenes well, leading to inaccurate scene flow estimation. To this end, we propose an iterative end-to-end super point based scene flow estimation framework, where the superpoints can be dynamically updated to guide the point-level flow prediction. Specifically, our framework consists of a flow guided superpoint generation module and a superpoint guided flow refinement module. In our superpoint generation module, we utilize the bidirectional flow information at the previous iteration to obtain the matching points of points and superpoint centers for soft point-to-superpoint association construction, in which the superpoints are generated for pairwise point clouds. With the generated superpoints, we first reconstruct the flow for each point by adaptively aggregating the superpoint-level flow, and then encode the consistency between the reconstructed flow of pairwise point clouds. Finally, we feed the consistency encoding along with the reconstructed flow into GRU to refine point-level flow. Extensive experiments on several different datasets show that our method can achieve promising performance. Code is available at https://***/supersyq/SPFlowNet.

关键词：

Non-aligned Supervision for Real image Dehazing

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arXiv 2023年

作者： Fan, Junkai Guo, Fei Qian, Jianjun Li, Xiang Li, Jun Yang, Jian Key Lab of Intelligent Perception and Systems for HighDimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computer Science Nankai University Tianjin China

Removing haze from real-world images is challenging due to unpredictable weather conditions, resulting in the misalignment of hazy and clear image pairs. In this paper, we propose an innovative dehazing framework that operates under non-aligned supervision. This framework is grounded in the atmospheric scattering model, and consists of three interconnected networks: dehazing, airlight, and transmission networks. In particular, we explore a non-alignment scenario that a clear reference image, unaligned with the input hazy image, is utilized to supervise the dehazing network. To implement this, we present a multi-scale reference loss that compares the feature representations between the referred image and the dehazed output. Our scenario makes it easier to collect hazy/clear image pairs in real-world environments, even under conditions of misalignment and shift views. To showcase the effectiveness of our scenario, we have collected a new hazy dataset including 415 image pairs captured by mobile Phone in both rural and urban areas, called"Phone-Hazy". Furthermore, we introduce a self-attention network based on mean and variance for modeling real infinite airlight, using the dark channel prior as positional guidance. Additionally, a channel attention network is employed to estimate the three-channel transmission. Experimental results demonstrate the superior performance of our framework over existing state-of-the-art techniques in the real-world image dehazing task. Phone-Hazy and code will be available at https://***/projectpage/NSDNet/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

Tri-Perspective view Decomposition for Geometry-Aware Depth Completion

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Tri-Perspective view Decomposition for Geometry-Aware Depth ...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiqiang Yan Yuankai Lin Kun Wang Yupeng Zheng Yufei Wang Zhenyu Zhang Jun Li Jian Yang PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Sci. & Tech. China Huazhong University of Science and Technology Chinese Academy of Sciences Northwestem Poly technical University Nanjing University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Depth completion is a vital taskfor autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. How-ever, most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation, which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this chal-lenge, we introduce Tri-Perspective View Decomposition (TPVD), a novel framework that can explicitly model 3D geometry. In particular, (1) TPVD ingeniously decomposes the original point cloud into three 2D views, one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation, where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPVaffinitive neighbors, the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result, our TPVD outperforms existing methods on KITTI, NYUv2, and SUN RGBD. Fur-thermore, we build a novel depth completion dataset named TOFDC, which is acquired by the time-of-flight (TOF) sen-sor and the color camera on smart phones. Project page.

关键词： Geometry Point cloud compression Solid modeling Three-dimensional displays Laser radar Convolution Pattern recognition