Author name cluster

Beihao Xia

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

TubeRMC: Tube-conditioned Reconstruction with Mutual Constraints for Weakly-supervised Spatio-Temporal Video Grounding

Jinxuan Li
Yi Zhang
Jian-Fang Hu
Chaolei Tan
Tianming Liang
Beihao Xia

Spatio-Temporal Video Grounding (STVG) aims to localize a spatio-temporal tube that corresponds to a given language query in an untrimmed video. This is a challenging task since it involves complex vision-language understanding and spatiotemporal reasoning. Recent works have explored weakly-supervised setting in STVG to eliminate reliance on fine-grained annotations like bounding boxes or temporal stamps. However, they typically follow a simple late-fusion manner, which generates tubes independent of the text description, often resulting in failed target identification and inconsistent target tracking. To address this limitation, we propose a Tube-conditioned Reconstruction with Mutual Constraints (TubeRMC) framework that generates text-conditioned candidate tubes with pre-trained visual grounding models and further refine them via tube-conditioned reconstruction with spatio-temporal constraints. Specifically, we design three reconstruction strategies from temporal, spatial, and spatio-temporal perspectives to comprehensively capture rich tube-text correspondences. Each strategy is equipped with a Tube-conditioned Reconstructor, utilizing spatio-temporal tubes as condition to reconstruct the key clues in the query. We further introduce mutual constraints between spatial and temporal proposals to enhance their quality for reconstruction. TubeRMC outperforms existing methods on two public benchmarks VidSTG and HCSTVG. Further visualization shows that TubeRMC effectively mitigates both target identification errors and inconsistent tracking.

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IROS Conference 2025 Conference Paper

A Multi-modal Hand Imitation Dataset for Dexterous Hand

Shaochen Wang
Qilin Wu
Kang Chen
Qing Huang
Zhuo Cheng
Beihao Xia

Multimodal data is indispensable for advancing imitation learning, particularly in the context of dexterous hands. However, existing datasets predominantly rely on single-modality inputs, such as RGB images, which inherently lack the capacity to capture the spatial and temporal dynamics essential for achieving human-like dexterity. To address this limitation, we introduce Multi-Modal Dex, a dataset that integrates multimodal sensory data to enable the effective learning of dexterous skills from human demonstrations. By combining visual, point cloud, and kinematic modalities, our dataset provides a richer representation of hand interactions, thereby facilitating a more nuanced understanding of dexterous imitation. Our framework leverages neural rendering and kinematic optimization to align human and robotic hand poses in a shared canonical space, enabling geometrically consistent skill transfer. Furthermore, we analyze the dataset’s potential to advance dexterous robots in perception, imitation learning, and real-world dexterous skill transfer. The data is available at https://github.com/WangShaoSUN/MutliDex.

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ICLR Conference 2024 Conference Paper

Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios

Ziqiang Li 0001
Hong Sun
Pengfei Xia
Heng Li 0008
Beihao Xia
Yi Wu 0018
Bin Li 0025

Recent deep neural networks (DNNs) have came to rely on vast amounts of training data, providing an opportunity for malicious attackers to exploit and contaminate the data to carry out backdoor attacks. However, existing backdoor attack methods make unrealistic assumptions, assuming that all training data comes from a single source and that attackers have full access to the training data. In this paper, we introduce a more realistic attack scenario where victims collect data from multiple sources, and attackers cannot access the complete training data. We refer to this scenario as $\textbf{data-constrained backdoor attacks}$. In such cases, previous attack methods suffer from severe efficiency degradation due to the $\textbf{entanglement}$ between benign and poisoning features during the backdoor injection process. To tackle this problem, we introduce three CLIP-based technologies from two distinct streams: $\textit{Clean Feature Suppression}$ and $\textit{Poisoning Feature Augmentation}$. The results demonstrate remarkable improvements, with some settings achieving over $\textbf{100}$% improvement compared to existing attacks in data-constrained scenarios.

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ICRA Conference 2023 Conference Paper

TODE-Trans: Transparent Object Depth Estimation with Transformer

Kang Chen
Shaochen Wang
Beihao Xia
Dongxu Li
Zhen Kan
Bin Li 0025

Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects due to the refraction and reflection of light. In this work, we present a transformer-based transparent object depth estimation approach from a single RGB-D input. We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas. In addition, to better enhance the fine-grained features, a feature fusion module (FFM) is designed to assist coherent prediction. Our empirical evidence demonstrates that our model delivers significant improvements in recent popular datasets, e. g. , 25% gain on RMSE and 21% gain on REL compared to previous state-of-the-art convolutional-based counterparts in ClearGrasp dataset. Extensive results show that our transformer-based model enables better aggregation of the object's RGB and inaccurate depth information to obtain a better depth representation. Our code and the pre-trained model are available at https://github.com/yuchendoudou/TODE.

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IJCAI Conference 2022 Conference Paper

Recent Advances in Concept Drift Adaptation Methods for Deep Learning

Liheng Yuan
Heng Li
Beihao Xia
Cuiying Gao
Mingyue Liu
Wei Yuan
Xinge You

In the ``Big Data'' age, the amount and distribution of data have increased wildly and changed over time in various time-series-based tasks, e. g weather prediction, network intrusion detection. However, deep learning models may become outdated facing variable input data distribution, which is called concept drift. To address this problem, large number of samples are usually required to update deep learning models, which is impractical in many realistic applications. This challenge drives researchers to explore the effective ways to adapt deep learning models to concept drift. In this paper, we first mathematically describe the categories of concept drift including abrupt drift, gradual drift, recurrent drift, incremental drift. We then divide existing studies into two categories (i. e. , model parameter updating and model structure updating), and analyze the pros and cons of representative methods in each category. Finally, we evaluate the performance of these methods, and point out the future directions of concept drift adaptation for deep learning.

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