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Xiaojin Gong

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Modality-Aware Bias Mitigation and Invariance Learning for Unsupervised Visible-Infrared Person Re-Identification

Menglin Wang
Xiaojin Gong
Jiachen Li
Genlin Ji

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match individuals across visible and infrared cameras without relying on any annotation. Given the significant gap across visible and infrared modality, estimating reliable cross-modality association becomes a major challenge in USVI-ReID. Existing methods usually adopt optimal transport to associate the intra-modality clusters, which is prone to propagating the local cluster errors, and also overlooks global instance-level relations. By mining and attending to the visible-infrared modality bias, this paper focuses on addressing cross-modality learning from two aspects: bias-mitigated global association and modality-invariant representation learning. Motivated by the camera-aware distance rectification in single-modality re-ID, we propose modality-aware Jaccard distance to mitigate the distance bias caused by modality discrepancy, so that more reliable cross-modality associations can be estimated through global clustering. To further improve cross-modality representation learning, a `split-and-contrast' strategy is designed to obtain modality-specific global prototypes. By explicitly aligning these prototypes under global association guidance, modality-invariant yet ID-discriminative representation learning can be achieved. While conceptually simple, our method obtains state-of-the-art performance on benchmark VI-ReID datasets and outperforms existing methods by a significant margin, validating its effectiveness.

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AAAI Conference 2025 Conference Paper

Prior-Constrained Association Learning for Fine-Grained Generalized Category Discovery

Menglin Wang
Zhun Zhong
Xiaojin Gong

This paper addresses generalized category discovery (GCD), the task of clustering unlabeled data from potentially known or unknown categories with the help of labeled instances from each known category. Compared to traditional semi-supervised learning, GCD is more challenging because unlabeled data could be from novel categories not appearing in labeled data. Current state-of-the-art methods typically learn a parametric classifier assisted by self-distillation. While being effective, these methods do not make use of cross-instance similarity to discover class-specific semantics which are essential for representation learning and category discovery. In this paper, we revisit the association-based paradigm and propose a Prior-constrained Association Learning method to capture and learn the semantic relations within data. In particular, the labeled data from known categories provides a unique prior for the association of unlabeled data. Unlike previous methods that only adopts the prior as a pre or post-clustering refinement, we fully incorporate the prior into the association process, and let it constrain the association towards a reliable grouping outcome. The estimated semantic groups are utilized through non-parametric prototypical contrast to enhance the representation learning. A further combination of both parametric and non-parametric classification complements each other and leads to a model that outperforms existing methods by a significant margin. On multiple GCD benchmarks, we perform extensive experiments and validate the effectiveness of our proposed method.

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AAAI Conference 2021 Conference Paper

Camera-Aware Proxies for Unsupervised Person Re-Identification

Menglin Wang
Baisheng Lai
Jianqiang Huang
Xiaojin Gong
Xian-Sheng Hua

This paper tackles the purely unsupervised person reidentification (Re-ID) problem that requires no annotations. Some previous methods adopt clustering techniques to generate pseudo labels and use the produced labels to train Re- ID models progressively. These methods are relatively simple but effective. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the large intra-ID variance caused mainly by the change of camera views. To address this issue, we propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. These camera-aware proxies enable us to deal with large intra-ID variance and generate more reliable pseudo labels for learning. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model to effectively learn the ID discrimination ability within and across cameras. Meanwhile, a proxybalanced sampling strategy is also designed, which facilitates our learning further. Extensive experiments on three largescale Re-ID datasets show that our proposed approach outperforms most unsupervised methods by a significant margin. Especially, on the challenging MSMT17 dataset, we gain 14. 3 percent Rank-1 and 10. 2 percent mAP improvements when compared to the second place. Code is available at: https: //github. com/Terminator8758/CAP-master.

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ICRA Conference 2021 Conference Paper

PENet: Towards Precise and Efficient Image Guided Depth Completion

Mu Hu
Shuling Wang 0002
Bin Li 0038
Shiyu Ning
Li Fan
Xiaojin Gong

Image guided depth completion is the task of generating a dense depth map from a sparse depth map and a high quality image. In this task, how to fuse the color and depth modalities plays an important role in achieving good performance. This paper proposes a two-branch backbone that consists of a color-dominant branch and a depth-dominant branch to exploit and fuse two modalities thoroughly. More specifically, one branch inputs a color image and a sparse depth map to predict a dense depth map. The other branch takes as inputs the sparse depth map and the previously predicted depth map, and outputs a dense depth map as well. The depth maps predicted from two branches are complimentary to each other and therefore they are adaptively fused. In addition, we also propose a simple geometric convolutional layer to encode 3D geometric cues. The geometric encoded backbone conducts the fusion of different modalities at multiple stages, leading to good depth completion results. We further implement a dilated and accelerated CSPN++ to refine the fused depth map efficiently. The proposed full model ranks 1st in the KITTI depth completion online leaderboard at the time of submission. It also infers much faster than most of the top ranked methods. The code of this work is available at https://github.com/JUGGHM/PENet_ICRA2021.

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IJCAI Conference 2018 Conference Paper

A Normalized Convolutional Neural Network for Guided Sparse Depth Upsampling

Jiashen Hua
Xiaojin Gong

Guided sparse depth upsampling aims to upsample an irregularly sampled sparse depth map when an aligned high-resolution color image is given as guidance. When deep convolutional neural networks (CNNs) become the optimal choice to many applications nowadays, how to deal with irregular and sparse data still remains a non-trivial problem. Inspired by the classical normalized convolution operation, this work proposes a normalized convolutional layer (NCL) implemented in CNNs. Sparse data are therefore explicitly considered in CNNs by the separation of both data and filters into a signal part and a certainty part. Based upon NCLs, we design a normalized convolutional neural network (NCNN) to perform guided sparse depth upsampling. Experiments on both indoor and outdoor datasets show that the proposed NCNN models achieve state-of-the-art upsampling performance. Moreover, the models using NCLs gain a great generalization ability to different sparsity levels.

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IJCAI Conference 2017 Conference Paper

Saliency Guided End-to-End Learning for Weakly Supervised Object Detection

Baisheng Lai
Xiaojin Gong

Weakly supervised object detection (WSOD), which is the problem of learning detectors using only image-level labels, has been attracting more and more interest. However, this problem is quite challenging due to the lack of location supervision. To address this issue, this paper integrates saliency into a deep architecture, in which the location information is explored both explicitly and implicitly. Specifically, we select highly confident object proposals under the guidance of class-specific saliency maps. The location information, together with semantic and saliency information, of the select proposals are then used to explicitly supervise the network by imposing two additional losses. Meanwhile, a saliency prediction sub-network is built in the architecture. The prediction results are used to implicitly guide the localization procedure. The entire network is trained end-to-end. Experiments on PASCAL VOC demonstrate that our approach outperforms all state-of-the-arts.

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AAAI Conference 2016 Conference Paper

An Alternating Proximal Splitting Method with Global Convergence for Nonconvex Structured Sparsity Optimization

Shubao Zhang
Hui Qian
Xiaojin Gong

In many learning tasks with structural properties, structured sparse modeling usually leads to better interpretability and higher generalization performance. While great efforts have focused on the convex regularization, recent studies show that nonconvex regularizers can outperform their convex counterparts in many situations. However, the resulting nonconvex optimization problems are still challenging, especially for the structured sparsity-inducing regularizers. In this paper, we propose a splitting method for solving nonconvex structured sparsity optimization problems. The proposed method alternates between a gradient step and an easily solvable proximal step, and thus enjoys low per-iteration computational complexity. We prove that the whole sequence generated by the proposed method converges to a critical point with at least sublinear convergence rate, relying on the Kurdyka-Łojasiewicz inequality. Experiments on both simulated and real-world data sets demonstrate the efﬁciency and efﬁcacy of the proposed method.

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ICRA Conference 2014 Conference Paper

Road scene segmentation via fusing camera and lidar data

Wenqi Huang 0002
Xiaojin Gong
Zhiyu Xiang

This paper presents an approach for pixel-wise object segmentation for road scenes based on the integration of a color image and an aligned 3D point cloud. In light of the advantage of range information in object discovery, we first produce initial object hypotheses by clustering the sparse 3D point cloud. The image pixels registered to the clustered 3D points are taken as samples to learn each object's prior knowledge. The priors are represented by Gaussian Mixture Models (GMMs) of color and 3D location information only, requiring no high-level features. We further formulate the segmentation problem within a Conditional Random Field (CRF) framework, which incorporates the learned prior models, together with hard constraints placed on the registered pixels and pairwise spatial constraints to achieve final results. Our algorithm is validated on the challenging KITTI dataset which contains diverse complicated road scenarios. Both qualitative and quantitative evaluation results show the superiority of our algorithm.

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