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arXiv 2024年

作者： Lin, Xiaotong Liang, Tianming Lai, Jianhuang Hu, Jian-Fang School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Human trajectory prediction is a practical task of predicting the future positions of pedestrians on the road, which typically covers all temporal ranges from short-term to long-term within a trajectory. However, existing works attempt to address the entire trajectory prediction with a singular, uniform training paradigm, neglecting the distinction between short-term and long-term dynamics in human trajectories. To overcome this limitation, we introduce a novel Progressive Pretext Task learning (PPT) framework, which progressively enhances the model's capacity of capturing short-term dynamics and long-term dependencies for the final entire trajectory prediction. Specifically, we elaborately design three stages of training tasks in the PPT framework. In the first stage, the model learns to comprehend the short-term dynamics through a stepwise next-position prediction task. In the second stage, the model is further enhanced to understand long-term dependencies through a destination prediction task. In the final stage, the model aims to address the entire future trajectory task by taking full advantage of the knowledge from previous stages. To alleviate the knowledge forgetting, we further apply a cross-task knowledge distillation. Additionally, we design a Transformer-based trajectory predictor, which is able to achieve highly efficient two-step reasoning by integrating a destination-driven prediction strategy and a group of learnable prompt embeddings. Extensive experiments on popular benchmarks have demonstrated that our proposed approach achieves state-of-the-art performance with high efficiency. Code is available at https://***/iSEE-laboratory/PPT. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Differentiable Gated Autoencoders for Unsupervised Feature Selection

SSRN

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SSRN 2024年

作者： Chen, Zebin Bian, Jintang Qiao, Bo Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Unsupervised feature selection (UFS) aims to identify a subset of the most informative features from high-dimensional data without labels. However, most existing UFS methods cannot adequately capture the intricate nonlinear relationships present in high-dimensional data, limiting their ability to obtain meaningful features. In this letter, we propose a novel UFS model that leverages the autoencoder with a differentiable gating function to address this limitation. Our model optimizes a bi-level loss function to automatically select a subset of informative and stable features by capturing the nonlinear interactions between features. Furthermore, we leverage the continuous relaxation of the Bernoulli distribution to parameterize stochastic gates, thereby enabling the learning of a relaxed model through a low-variance gradient estimator. The proposed model does not necessitate prior knowledge or domain-specific tuning, making it a versatile and widely applicable method. Extensive experiments on 13 benchmark datasets demonstrate the effectiveness and superiority of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding

arXiv

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arXiv 2024年

作者： Tan, Chaolei Lai, Jianhuang Zheng, Wei-Shi Hu, Jian-Fang School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Video Paragraph Grounding (VPG) is an emerging task in video-language understanding, which aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. However, existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. In this work, we introduce and explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to eliminate the need of temporal annotations. Different from previous weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking, we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG. Specifically, we devise a Siamese Grounding TRansformer (SiamGTR) consisting of two weight-sharing branches for learning complementary supervision. An Augmentation Branch is utilized for directly regressing the temporal boundaries of a complete paragraph within a pseudo video, and an Inference Branch is designed to capture the order-guided feature correspondence for localizing multiple sentences in a normal video. We demonstrate by extensive experiments that our paradigm has superior practicability and flexibility to achieve efficient weakly-supervised or semi-supervised learning, outperforming state-of-the-art methods trained with the same or stronger supervision. © 2024, CC BY.

关键词： Semantics

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Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding

Siamese Learning with Joint Alignment and Regression for Wea...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chaolei Tan Jianhuang Lai Wei-Shi Zheng Jian-Fang Hu School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Video Paragraph Grounding (VPG) is an emerging task in video-language understanding, which aims at localizing multiple sentences with semantic relations and temporal or-der from an untrimmed video. However, existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. In this work, we introduce and explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to elim-inate the need of temporal annotations. Different from pre-vious weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking, we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG. Specifically, we devise a Siamese Grounding TRansformer (SiamGTR) consisting of two weight-sharing branches for learning complementary supervision. An Aug-mentation Branch is utilized for directly regressing the tem-poral boundaries of a complete paragraph within a pseudo video, and an Inference Branch is designed to capture the order-guided feature correspondence for localizing multi-ple sentences in a normal video. We demonstrate by exten-sive experiments that our paradigm has superior practica-bility and flexibility to achieve efficient weakly-supervised or semi-supervised learning, outperforming state-of-the-art methods trained with the same or stronger supervision.

关键词： Location awareness Computer vision Grounding Annotations Semantics Semisupervised learning Transformers

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A Versatile Framework for Multi-scene Person Re-identification

arXiv

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arXiv 2024年

作者： Zheng, Wei-Shi Yan, Junkai Peng, Yi-Xing The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education 510006 China

Person Re-identification (ReID) has been extensively developed for a decade in order to learn the association of images of the same person across non-overlapping camera views. To overcome significant variations between images across camera views, mountains of variants of ReID models were developed for solving a number of challenges, such as resolution change, clothing change, occlusion, modality change, and so on. Despite the impressive performance of many ReID variants, these variants typically function distinctly and cannot be applied to other challenges. To our best knowledge, there is no versatile ReID model that can handle various ReID challenges at the same time. This work contributes to the first attempt at learning a versatile ReID model to solve such a problem. Our main idea is to form a two-stage prompt-based twin modeling framework called VersReID. Our VersReID firstly leverages the scene label to train a ReID Bank that contains abundant knowledge for handling various scenes, where several groups of scene-specific prompts are used to encode different scene-specific knowledge. In the second stage, we distill a V-Branch model with versatile prompts from the ReID Bank for adaptively solving the ReID of different scenes, eliminating the demand for scene labels during the inference stage. To facilitate training VersReID, we further introduce the multi-scene properties into self-supervised learning of ReID via a multi-scene prioris data augmentation (MPDA) strategy. Through extensive experiments, we demonstrate the success of learning an effective and versatile ReID model for handling ReID tasks under multi-scene conditions without manual assignment of scene labels in the inference stage, including general, low-resolution, clothing change, occlusion, and cross-modality scenes. Codes and models are available at https://***/iSEE-laboratory/VersReID. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Salient Foreground-Aware Network for Person Search 16th

Salient Foreground-Aware Network for Person Search

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Chen, Hongxu Zhang, Quan Lai, Jianhuang The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangzhou510006 China

ISBN: (纸本)9783031202322

Person search aims to simultaneously localize and identify a query person from realistic and uncropped images, which consists of person detection and re-identification. In existing methods, the extracted features come from the low-quality proposals generated by the structure like Region Proposal Network (RPN), and convolution is used to learn local features in the process of extracting features while the receptive field cannot grasp the global structural information. We propose an end-to-end network embedded with our Salient Foreground-Aware Module (SFAM). Self-attention mechanism in SFAM allows for better capture of global information. Our network incorporates our embedding method for person detection and person re-identification, which can effectively optimize the process of extracting features and improve feature expression capabilities. We merge the above modules into our Salient Foreground-Aware Network (SFAN). Extensive experiments have shown that our SFAN significantly improves the performance of end-to-end models with acceptable time-consuming and achieves state-of-the-art results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Embeddings

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Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding

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CAAI Transactions on intelligence Technology 2021年第3期6卷 360-375页

作者： Ejay Nsugbe Oluwarotimi William Samuel Mojisola Grace Asogbon Guanglin Li Independent Scholar Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences(CAS)Shenzhen Institutes of Advanced TechnologyShenzhenChina

In signal processing,multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels,where the optimal level within the signal minimises redundancy,uncertainties,and contains the information required for the characterisation of the sensed *** the area of physiological signal processing for prosthesis control,scenarios where a signal decomposition analysis are required:the wavelet decomposition(WD)has been seen to be the favoured time-frequency approach for the decomposition of non-stationary *** a research perspective,the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and *** this,there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational ***,for neuro-muscular(electromyography)and brainwave(electroencephalography)signals acquired from a transhumeral amputee,a computationally efficient time domain signal decom-position method based on a series of heuristics was applied to process the acquired signals before feature *** results showed an improvement in motion intent decoding prowess for the proposed time-domain-based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.

关键词： motion approach analysis

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Recursive Confidence Training for Pseudo-Labeling Calibration in Semi-Supervised Few-Shot Learning

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IEEE Transactions on Image Processing 2025年 34卷 3194-3208页

作者： Jing, Kunlei Ma, Hebo Zhang, Chen Wen, Lei Zhang, Zhaorui Xi’an Jiaotong University School of Software Engineering National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi’an China The Hong Kong Polytechnic University Department of Computing Hong Kong Xi’an Jiaotong University Institute of Science and Education Development Xi’an China The Hong Kong Polytechnic University Department of Computing School of Hotel and Tourism Management Hong Kong Guangzhou China

Semi-Supervised Few-Shot Learning (SSFSL) aims to address the data scarcity in few-shot learning by leveraging both a few labeled support data and abundant unlabeled data. In SSFSL, a classifier trained on scarce support data is often biased and thus assigns inaccurate pseudo-labels to the unlabeled data, which will mislead downstream learning tasks. To combat this issue, we introduce a novel method called Certainty-Aware Recursive Confidence Training (CARCT). CARCT hinges on the insight that selecting pseudo-labeled data based on confidence levels can yield more informative support data, which is crucial for retraining an unbiased classifier to achieve accurate pseudo-labeling—a process we term pseudo-labeling calibration. We observe that accurate pseudo-labels typically exhibit smaller certainty entropy, indicating high-confidence pseudo-labeling compared to those of inaccurate pseudo-labels. Accordingly, CARCT constructs a joint double-Gaussian model to fit the certainty entropies collected across numerous SSFSL tasks. Thereby, A semi-supervised Prior Confidence Distribution (ssPCD) is learned to aid in distinguishing between high-confidence and low-confidence pseudo-labels. During an SSFSL task, ssPCD guides the selection of both high-confidence and low-confidence pseudo-labeled data to retrain the classifier that then assigns more accurate pseudo-labels to the low-confidence pseudo-labeled data. Such recursive confidence training continues until the low-confidence ones are exhausted, terminating the pseudo-labeling calibration. The unlabeled data all receive accurate pseudo-labels to expand the few support data to generalize the downstream learning task, which in return meta-refines the classifier, named self-training, to boost the pseudo-labeling in subsequent tasks. Extensive experiments on basic and extended SSFSL setups showcase the superiority of CARCT versus state-of-the-art methods, and comprehensive ablation studies and visualizations justify our insigh

关键词： Semi-supervised learning

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Zikang Lin, Jingru Ge, Meng Jiang, Yu Wang, Xiaobao Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Singapore Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model's robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE. © 2025 IEEE.

关键词： continue speaking data augmentation short enrollment speech synthesis Target speaker extraction

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Uncertainty-Guided Dual Task Framework for Semi-Supervised Segmentation of Thyroid Nodules 27

Uncertainty-Guided Dual Task Framework for Semi-Supervised S...

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27th European Conference on Artificial intelligence, ECAI 2024

作者： Ying, Xiang Zhang, Jizhe Gao, Jie Yu, Mei Zhang, Ruixuan Zhu, Jialin Li, Xuewei Yu, Ruiguo College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China School of Future Technology Tianjin University Tianjin300350 China Department of Diagnostic and Therapeutic Ultrasonography Tianjin Medical University Cancer Institute and Hospital Tianjin300060 China

ISBN: (纸本)9781643685489

Since ultrasound imaging technique is convenient and real-time, it plays a crucial role in diagnosing thyroid nodules. With the development of deep learning, computer-aided diagnosis models have been widely applied to diagnose thyroid nodules, in which thyroid nodule segmentation is a basic yet essential task. Semi-supervised learning is a popular topic for thyroid ultrasound images segmentation under limited annotations. However, current mainstream semi-supervised methods (e.g., for nature image scenes) commonly produce poor segmentation results when facing thyroid ultrasound images. We analyze that there are two main reasons: First, since these methods lack the targeted learning for the ambiguous regions of images that may contain complementary clues for segmentation, the model will likely be over-fitting in the regions that are easy to predict, leading to fail to make full use of the unlabeled data. Second, these methods lack the shape constraint on thyroid nodules, resulting in incomplete segmentation shape on the nodule boundary. To address the above issues, we propose a novel Uncertainty-guided Dual Task Framework (UDTF). Concretely, we propose an Uncertainty Region Selection Module (URSM) to guide the segmentation task to learn from the ambiguous regions calculated by prototype under the constraint of consistency regularization. Additionally, to further keep the integrity of the nodules, we propose a Dual Task Module (DTM) to impose the shape constraint on thyroid nodules by exploring the task-level consistency between the segmentation and auxiliary reconstruction task. Extensive experiments are conducted on two thyroid ultrasound image datasets, including a private Philips Thyroid Ultrasound dataset and a public TN3K dataset. The results show that UDTF achieves superior performance compared to several other state-of-the-art methods. © 2024 The Authors.

关键词： Ultrasonic imaging

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