Sanqing Qu (瞿三清)

Hi there! Welcome to my homepage. I am currently a Ph.D. student in Intelligent Sensing, Perception and Computing (ISPC) Gruop led by Prof. Guang Chen at Tongji University, Shanghai, China. Before that, I received my bachelor degree of Automotive Engineering at Tongji University in 2020.

My research interests include autonomous driving, transfer learning, and video analysis.

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Selected Publications

* indicates equal contribution

Despite the simple global and local clustering (GLC) technique achieving commendable performance in separating "known" and "unknown" data, its reliance on pseudo-labeling supervision, especially using uniform encoding for all "unknown" data limits its capacity to discriminate among different "unknown" categories. To alleviate this, we promote GLC to GLC++ by developing a new contrastive affinity learning strategy, sidestepping the need for a specialized source model structure.

In this paper, we delve into TTA in 3D point cloud segmentation and propose a novel Hierarchical Geometry Learning (HGL) framework. HGL comprises three complementary modules from local, global to temporal learning in a bottom-up manner. Technically, we first construct a local geometry learning module for pseudo-label generation. Next, we build prototypes from the global geometry perspective for pseudo-label fine-tuning. Furthermore, we introduce a temporal consistency regularization module to mitigate negative transfer.

For Universal Domain Adaptation (UniDA), in this paper, we propose a new idea of LEArning Decomposition (LEAD), which decouples features into source-known and -unknown components to identify target-private data. This solution leads to elegant views for identifying target-private unknown data without tedious tuning thresholds or relying on iterative clustering.

In this paper, we target a practical setting in IP protection, i.e., Source-free Model IP protection. To achieve this, we propose a novel MAsk Pruning (MAP) framework. MAP stems from an intuitive hypothesis, i.e., there are target-related parameters in a well-trained model, locating and pruning them is the key to IP protection.

In this paper, we explore the Source-free Universal Domain Adaptation (SF-UniDA). SF-UniDA is appealing in view that universal model adaptation can be resolved only on the basis of a standard pre-trained closed-set model, i.e., without source raw data and dedicated model architecture.

Existing single-DG techniques commonly devise various data-augmentation algorithms, and remould the multi-source domain generalization methodology to learn domain-generalized (semantic) features. In contrast, we target a versatile Modality-Agnostic Debiasing (MAD) framework for single-DG, that enables generalization for different modalities.

In this paper, we design a general prototype based pseudo-labeling strategy. It is model-agnostic and can be applied to existing self-training based SFDA methods.

In this paper, we proposed a bio-inspired event-camera based falls temporal localization framework. Specifically, we propose a event density-based action proposal generation scheme, and introduce a temporal-spatial attention mechanism for action modeling.

In this paper, we propose an action-context modeling network termed ACM-Net, which integrates a three-branch attention module to measure the likelihood of each temporal point being action instance, context, or non-action background, simultaneously.

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