Author name cluster

Weixin Luo

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7 papers

2 author rows

ICLR Conference 2025 Conference Paper

CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets

Feng Yan
Weixin Luo
Yujie Zhong
Yiyang Gan
Lin Ma 0002

Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not surpassed non-end-to-end tracking-by-detection methods. One possible reason lies in the training label assignment strategy that consistently binds the tracked objects with tracking queries and assigns few newborns to detection queries. Such an assignment, with one-to-one bipartite matching, yields an unbalanced training, _i.e._, scarce positive samples for detection queries, especially for an enclosed scene with the majority of the newborns at the beginning of videos. As such, e2e-MOT will incline to generate a tracking terminal without renewal or re-initialization, compared to other tracking-by-detection methods. To alleviate this problem, we propose **Co-MOT**, a simple yet effective method to facilitate e2e-MOT by a novel coopetition label assignment with a shadow concept. Specifically, we add tracked objects to the matching targets for detection queries when performing the label assignment for training the intermediate decoders. For query initialization, we expand each query by a set of shadow counterparts with limited disturbance to itself. With extensive ablation studies, Co-MOT achieves superior performances without extra costs, _e.g._, 69.4% HOTA on DanceTrack and 52.8% TETA on BDD100K. Impressively, Co-MOT only requires 38% FLOPs of MOTRv2 with comparable performances, resulting in the 1.4× faster inference speed. Source code is publicly available at [GitHub](https://github.com/BingfengYan/CO-MOT).

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AAAI Conference 2022 Conference Paper

Explore Inter-contrast between Videos via Composition for Weakly Supervised Temporal Sentence Grounding

Jiaming Chen
Weixin Luo
Wei Zhang
Lin Ma

Weakly supervised temporal sentence grounding aims to temporally localize the target segment corresponding to a given natural language query, where it provides video-query pairs without temporal annotations during training. Most existing methods use the fused visual-linguistic feature to reconstruct the query, where the least reconstruction error determines the target segment. This work introduces a novel approach that explores the inter-contrast between videos in a composed video by selecting components from two different videos and fusing them into a single video. Such a straightforward yet effective composition strategy provides the temporal annotations at multiple composed positions, resulting in numerous videos with temporal ground-truths for training the temporal sentence grounding task. A transformer framework is introduced with multi-tasks training to learn a compact but efficient visual-linguistic space. The experimental results on the public Charades-STA and ActivityNet- Caption dataset demonstrate the effectiveness of the proposed method, where our approach achieves comparable performance over the state-of-the-art weakly-supervised baselines. The code is available at https: //github. com/PPjmchen/ Composition WSTG.

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AAAI Conference 2021 Conference Paper

Amodal Segmentation Based on Visible Region Segmentation and Shape Prior

Yuting Xiao
Yanyu Xu
Ziming Zhong
Weixin Luo
Jiawei Li
Shenghua Gao

Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human’s amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mimic the behavior of the human and solve the ambiguity in the learning, we propose a framework, it firstly estimates a coarse visible mask and a coarse amodal mask. Then based on the coarse prediction, our model infers the amodal mask by concentrating on the visible region and utilizing the shape prior in the memory. In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation. Consequently, the amodal mask would not be affected by the occlusion when given the same visible regions. The leverage of shape prior makes the amodal mask estimation more robust and reasonable. Our proposed model is evaluated on three datasets. Experiments show that our proposed model outperforms existing state-of-the-art methods. The visualization of shape prior indicates that the category-specific feature in the codebook has certain interpretability. The code is available at https: //github. com/YutingXiao/Amodal-Segmentation- Based-on-Visible-Region-Segmentation-and-Shape-Prior.

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NeurIPS Conference 2020 Conference Paper

SIRI: Spatial Relation Induced Network For Spatial Description Resolution

Peiyao Wang
Weixin Luo
Yanyu Xu
Haojie Li
Shugong Xu
Jianyu Yang
Shenghua Gao

Spatial Description Resolution, as a language-guided localization task, is proposed for target location in a panoramic street view, given corresponding language descriptions. Explicitly characterizing an object-level relationship while distilling spatial relationships are currently absent but crucial to this task. Mimicking humans, who sequentially traverse spatial relationship words and objects with a first-person view to locate their target, we propose a novel spatial relationship induced (SIRI) network. Specifically, visual features are firstly correlated at an implicit object-level in a projected latent space; then they are distilled by each spatial relationship word, resulting in each differently activated feature representing each spatial relationship. Further, we introduce global position priors to fix the absence of positional information, which may result in global positional reasoning ambiguities. Both the linguistic and visual features are concatenated to finalize the target localization. Experimental results on the Touchdown show that our method is around 24\% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown. The code for this project is publicly available at https: //github. com/wong-puiyiu/siri-sdr.

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IJCAI Conference 2019 Conference Paper

Margin Learning Embedded Prediction for Video Anomaly Detection with A Few Anomalies

Wen Liu
Weixin Luo
Zhengxin Li
Peilin Zhao
Shenghua Gao

Classical semi-supervised video anomaly detection assumes that only normal data are available in the training set because of the rare and unbounded nature of anomalies. It is obviously, however, these infrequently observed abnormal events can actually help with the detection of identical or similar abnormal events, a line of thinking that motivates us to study open-set supervised anomaly detection with only a few types of abnormal observed events and many normal events available. Under the assumption that normal events can be well predicted, we propose a Margin Learning Embedded Prediction (MLEP) framework. There are three features in MLEP- based open-set supervised video anomaly detection: i) we customize a video prediction framework that favors the prediction of normal events and distorts the prediction of abnormal events; ii) The margin learning framework learns a more compact normal data distribution and enlarges the margin between normal and abnormal events. Since abnormal events are unbounded, our framework consequently helps with the detection of abnormal events, even for anomalies that have never been previously observed. Therefore, our framework is suitable for the open-set supervised anomaly detection setting; iii) our framework can readily handle both frame-level and video-level anomaly annotations. Considering that video-level anomaly detection is more easily annotated in practice and that anomaly detection with a few anomalies is a more practical setting, our work thus pushes the application of anomaly detection towards real scenarios. Extensive experiments validate the effectiveness of our framework for anomaly detection.

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AAAI Conference 2019 Conference Paper

RGBD Based Gaze Estimation via Multi-Task CNN

Dongze Lian
Ziheng Zhang
Weixin Luo
Lina Hu
Minye Wu
Zechao Li
Jingyi Yu
Shenghua Gao

This paper tackles RGBD based gaze estimation with Convolutional Neural Networks (CNNs). Specifically, we propose to decompose gaze point estimation into eyeball pose, head pose, and 3D eye position estimation. Compared with RGB image-based gaze tracking, having depth modality helps to facilitate head pose estimation and 3D eye position estimation. The captured depth image, however, usually contains noise and black holes which noticeably hamper gaze tracking. Thus we propose a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points. We utilize a generator network for depth image generation with a Generative Neural Network (GAN), where the generator network is partially shared by both the gaze tracking network and GAN-based depth synthesizing. By optimizing the whole network simultaneously, depth image synthesis improves gaze point estimation and vice versa. Since the only existing RGBD dataset (EYEDIAP) is too small, we build a large-scale RGBD gaze tracking dataset for performance evaluation. As far as we know, it is the largest RGBD gaze dataset in terms of the number of participants. Comprehensive experiments demonstrate that our method outperforms existing methods by a large margin on both our dataset and the EYEDIAP dataset.

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AAAI Conference 2016 Conference Paper

Analysis-Synthesis Dictionary Learning for Universality-Particularity Representation Based Classification

Meng Yang
Weiyang Liu
Weixin Luo
Linlin Shen

Dictionary learning has played an important role in the success of sparse representation. Although synthesis dictionary learning for sparse representation has been well studied for universality representation (i. e. , the dictionary is universal to all classes) and particularity representation (i. e. , the dictionary is class-particular), jointly learning an analysis dictionary and a synthesis dictionary is still in its infant stage. Universality-particularity representation can well match the intrinsic characteristics of data (i. e. , different classes share commonality and distinctness), while analysis-synthesis dictionary can give a more complete view of data representation (i. e. , analysis dictionary is a dual-viewpoint of synthesis dictionary). In this paper, we proposed a novel model of analysis-synthesis dictionary learning for universalityparticularity (ASDL-UP) representation based classiﬁcation. The discrimination of universality and particularity representation is jointly exploited by simultaneously learning a pair of analysis dictionary and synthesis dictionary. More specifically, we impose a label preserving term to analysis coding coefﬁcients for universality representation. Fisher-like regularizations for analysis coding coefﬁcients and the subsequent synthesis representation are introduced to particularity representation. Compared with other state-of-the-art dictionary learning methods, ASDL-UP has shown better or competitive performance in various classiﬁcation tasks.

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