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Ya-Li Li

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8 papers

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AAAI Conference 2026 Conference Paper

Preserving Topological and Geometric Embeddings for Point Cloud Recovery

Kaiyue Zhou
Zelong Tan
Hongxiao Wang
Ya-Li Li
Shengjin Wang

Recovering point clouds involves the sequential process of sampling and restoration, yet existing methods struggle to effectively leverage both topological and geometric attributes. To address this, we propose an end-to-end architecture named TopGeoFormer, which maintains these critical properties throughout the sampling and restoration phases. First, we revisit traditional feature extraction techniques to yield topological embedding using a continuous mapping of relative relationships between neighboring points, and integrate it in both phases for preserving the structure of the original space. Second, we propose the InterTwining Attention to fully merge topological and geometric embeddings, which queries shape with local awareness in both phases to form a learnable 3D shape context facilitated with point-wise, point-shape-wise, and intra-shape features. Third, we introduce a full geometry loss and a topological constraint loss to optimize the embeddings in both Euclidean and topological spaces. The geometry loss uses inconsistent matching between coarse-to-fine generations and targets for reconstructing better geometric details, and the constraint loss limits embedding variances for better approximation of the topological space. In experiments, we comprehensively analyze the circumstances using the conventional and learning-based sampling/upsampling/recovery algorithms. The quantitative and qualitative results demonstrate that our method significantly outperforms existing sampling and recovery methods.

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AAAI Conference 2026 Conference Paper

TCoT: Trajectory Chain-of-Thoughts for Robotic Manipulation with Failure Recovery in Vision-Language-Action Model

Xiang Li
Ya-Li Li
Yuan Wang
Huaqiang Wang
Shengjin Wang

Recent advances in vision-language-action (VLA) models have demonstrated impressive generalization for robotic manipulation. However, these models often operate by directly mapping visual and linguistic inputs to subsequent actions, lacking intermediate task planning, along with failure detection and recovery ability. These limitations prevent them from effectively decomposing complex tasks, recognizing problems, and correcting erroneous actions, ultimately resulting in complete task failure. This significantly hinders their ability to perform long-horizon tasks and generalization ability. To this end, we introduce TCoT: Trajectory Chain-of-Thought, a unified VLA framework that enhances this direct mapping with trajectory planning as well as failure detection and recovery. TCoT leverages hierarchy trajectories as a precise and compact representation of CoT reasoning for manipulation: global planning provides a high-level, goal-oriented trajectory to guide the robot toward its task objective, while local planning focuses on real-time adjustments to address dynamic changes. Moreover, we designed the Global-Local Switching Recovery algorithm that detects and effectively recovers from failures. Experimental results reveal that TCoT surpasses the state-of-the-art methods across both real and simulated scenarios and exhibits superior generalization capabilities.

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AAAI Conference 2025 Conference Paper

LIBA: Language Instructed Multi-granularity Bridge Assistant for 3D Visual Grounding

Yuan Wang
Ya-Li Li
W U Eastman Z Y
Shengjin Wang

3D Vision Grounding (3D-VG) seeks to unravel referential language and identify targets in 3D physical world. Prevailing methods align with the 2D-VG's pipeline to pinpoint the referred object in a categorical multi-modal reasoning manner. However, the geometric complexities of 3D scenes and the nuanced syntactic structures of language, exacerbates the \textbf{granularity inconsistency} of point cloud and text features, hindering the development of 3D-VG systems in complex scenarios. Towards this issue, we propose LIBA, a Language-Instructed multi-granularity Bridge Assistant tailored for 3D-VG task. LIBA tackles this issue as follows. (1) \textit{How to establish a multi-granularity 3D vision-text feature alignment in a unified model}? We advance a bilateral Dynamic Bridge Adapter (DBA) build multi-granularity interaction of 3D vision and language backnones during feature extraction. We further develop the Language-aware Cross-scale Object Modulation (LCOM) module to integrate multi-scale point cloud features modulated by language information. (2) After aligning multi-modal features, \textit{how to fully harness language model's knowledge to bolster vision concepts understanding}? A LLM-guided Hierarchical Query Selection (LLM-HQS) module incorporates world knowledge of Large Language Model~(LLM) to ground the target referral via an Attribute-then-Relation reasoning process. In this manner, our LIBA inherits reasoning prowess and world knowledge of LLM to bridge point clouds and texts at multiple granularities. Experiments on ScanRefer and Nr3D/Sr3D benchmarks substantiate the superiority of our LIBA, trumping state-of-the-arts by a considerable margin.

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NeurIPS Conference 2023 Conference Paper

Uni3DETR: Unified 3D Detection Transformer

Zhenyu Wang
Ya-Li Li
Xi Chen
Hengshuang Zhao
Shengjin Wang

Existing point cloud based 3D detectors are designed for the particular scene, either indoor or outdoor ones. Because of the substantial differences in object distribution and point density within point clouds collected from various environments, coupled with the intricate nature of 3D metrics, there is still a lack of a unified network architecture that can accommodate diverse scenes. In this paper, we propose Uni3DETR, a unified 3D detector that addresses indoor and outdoor 3D detection within the same framework. Specifically, we employ the detection transformer with point-voxel interaction for object prediction, which leverages voxel features and points for cross-attention and behaves resistant to the discrepancies from data. We then propose the mixture of query points, which sufficiently exploits global information for dense small-range indoor scenes and local information for large-range sparse outdoor ones. Furthermore, our proposed decoupled IoU provides an easy-to-optimize training target for localization by disentangling the $xy$ and $z$ space. Extensive experiments validate that Uni3DETR exhibits excellent performance consistently on both indoor and outdoor 3D detection. In contrast to previous specialized detectors, which may perform well on some particular datasets but suffer a substantial degradation on different scenes, Uni3DETR demonstrates the strong generalization ability under heterogeneous conditions (Fig. 1).

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AAAI Conference 2022 Conference Paper

Delving into Probabilistic Uncertainty for Unsupervised Domain Adaptive Person Re-identification

Jian Han
Ya-Li Li
Shengjin Wang

Clustering-based unsupervised domain adaptive (UDA) person re-identification (ReID) reduces exhaustive annotations. However, owing to unsatisfactory feature embedding and imperfect clustering, pseudo labels for target domain data inherently contain an unknown proportion of wrong ones, which would mislead feature learning. In this paper, we propose an approach named probabilistic uncertainty guided progressive label refinery (P2 LR) for domain adaptive person reidentification. First, we propose to model the labeling uncertainty with the probabilistic distance along with ideal singlepeak distributions. A quantitative criterion is established to measure the uncertainty of pseudo labels and facilitate the network training. Second, we explore a progressive strategy for refining pseudo labels. With the uncertainty-guided alternative optimization, we balance between the exploration of target domain data and the negative effects of noisy labeling. On top of a strong baseline, we obtain significant improvements and achieve the state-of-the-art performance on four UDA ReID benchmarks. Specifically, our method outperforms the baseline by 6. 5% mAP on the Duke2Market task, while surpassing the state-of-the-art method by 2. 5% mAP on the Market2MSMT task. Code is available at: https: //github. com/JeyesHan/P2LR.

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NeurIPS Conference 2022 Conference Paper

Self-Supervised Learning via Maximum Entropy Coding

Xin Liu
Zhongdao Wang
Ya-Li Li
Shengjin Wang

A mainstream type of current self-supervised learning methods pursues a general-purpose representation that can be well transferred to downstream tasks, typically by optimizing on a given pretext task such as instance discrimination. In this work, we argue that existing pretext tasks inevitably introduce biases into the learned representation, which in turn leads to biased transfer performance on various downstream tasks. To cope with this issue, we propose Maximum Entropy Coding (MEC), a more principled objective that explicitly optimizes on the structure of the representation, so that the learned representation is less biased and thus generalizes better to unseen downstream tasks. Inspired by the principle of maximum entropy in information theory, we hypothesize that a generalizable representation should be the one that admits the maximum entropy among all plausible representations. To make the objective end-to-end trainable, we propose to leverage the minimal coding length in lossy data coding as a computationally tractable surrogate for the entropy, and further derive a scalable reformulation of the objective that allows fast computation. Extensive experiments demonstrate that MEC learns a more generalizable representation than previous methods based on specific pretext tasks. It achieves state-of-the-art performance consistently on various downstream tasks, including not only ImageNet linear probe, but also semi-supervised classification, object detection, instance segmentation, and object tracking. Interestingly, we show that existing batch-wise and feature-wise self-supervised objectives could be seen equivalent to low-order approximations of MEC. Code and pre-trained models are available at https: //github. com/xinliu20/MEC.

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NeurIPS Conference 2021 Conference Paper

Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification

Zhenyu Wang
Ya-Li Li
Ye Guo
Shengjin Wang

Semi-supervised learning aims to leverage a large amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. In this paper, we delve into semi-supervised learning for object detection, where labeled data are more labor-intensive to collect. Current methods are easily distracted by noisy regions generated by pseudo labels. To combat the noisy labeling, we propose noise-resistant semi-supervised learning by quantifying the region uncertainty. We first investigate the adverse effects brought by different forms of noise associated with pseudo labels. Then we propose to quantify the uncertainty of regions by identifying the noise-resistant properties of regions over different strengths. By importing the region uncertainty quantification and promoting multi-peak probability distribution output, we introduce uncertainty into training and further achieve noise-resistant learning. Experiments on both PASCAL VOC and MS COCO demonstrate the extraordinary performance of our method.

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NeurIPS Conference 2021 Conference Paper

Do Different Tracking Tasks Require Different Appearance Models?

Zhongdao Wang
Hengshuang Zhao
Ya-Li Li
Shengjin Wang
Philip Torr
Luca Bertinetto

Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem in a multitude of different experimental setups. As a consequence, the literature has fragmented too, and now novel approaches proposed by the community are usually specialised to fit only one specific setup. To understand to what extent this specialisation is necessary, in this work we present UniTrack, a solution to address five different tasks within the same framework. UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple ``heads'' that address individual tasks and do not require training. We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered. The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus extending their evaluation and comparison to a larger variety of important problems.

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