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University of Hong Kong

Academic institutionasia · hk
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Research library2,812linked papers
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Selected work

Representative Papers

Model Adaptation: Unsupervised Domain Adaptation Without Source Data

Jun 01, 2020Computer Vision and Pattern Recognition

This paper addresses unsupervised model adaptation from a source domain to a target domain without access to any source-domain data or labels—termed *source-free* adaptation—aiming solely to improve the generalization of a pre-trained source model on unlabeled target data. To this end, we propose a Collaborative Class-Conditional Generative Adversarial Network (CC-GAN) framework: it models the semantic structure of the target domain via class-conditional generation; enforces weight constraints derived from the source model to preserve its discriminative capability; and incorporates clustering-driven feature regularization to enhance the discriminability of target-domain representations. Evaluated across multiple cross-domain vision tasks, our method achieves significant performance gains over conventional source-dependent adaptation approaches—using only unlabeled target data. It is the first to empirically validate the effectiveness, robustness, and scalability of model adaptation under the source-free setting.

464 citations53 influentialRead paper

Deep Kronecker Network

Oct 24, 2022Biometrika

Medical imaging analysis faces challenges of limited annotated samples and stringent requirements for model interpretability. Method: This paper proposes a novel deep tensor regression framework based on the Kronecker product, which implicitly enforces piecewise smoothness constraints while integrating a fully convolutional architecture with low-rank tensor priors. Model weights are explicitly generated as the Kronecker product of two low-dimensional factor matrices, enabling native support for multi-modal tensor data (e.g., MRI, fMRI, CT) and unifying classification and regression tasks. Contribution/Results: Evaluated on real-world ADNI MRI data, the method achieves significantly improved generalization stability under extreme few-shot settings (<100 subjects). Moreover, its analytically tractable weight decomposition yields both pixel-level and anatomically grounded interpretations, offering a new paradigm that jointly ensures high predictive performance and clinical trustworthiness.

29 citations4 influentialRead paper

AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning

Nov 18, 2024arXiv.org

Multimodal large language models (MLLMs) exhibit limited capability in solving complex mathematical reasoning problems due to insufficient granular, stepwise reasoning over multimodal inputs. Method: This paper introduces the “slow-thinking” paradigm, integrating long-chain, atomic-level reasoning into MLLMs via AtomThink—a novel atomic thinking framework comprising (i) an automatic Chain-of-Thought (CoT) annotation engine, (ii) atomic-step fine-tuning, and (iii) a policy-based search method guided by a four-category strategy reward model (PRM). The approach unifies vision–math joint fine-tuning, reinforcement learning–driven search, and interpretable CoT generation. Contribution/Results: We release AtomMATH, a large-scale multimodal mathematical dataset, and propose fine-grained atomic capability evaluation metrics. On MathVista and MathVerse benchmarks, our method achieves relative accuracy improvements of ~50% and ~120%, respectively, significantly enhancing MLLMs’ hierarchical, adaptive reasoning on complex mathematical problems.

18 citations3 influentialRead paper

DreamOn: Diffusion Language Models For Code Infilling Beyond Fixed-size Canvas

Feb 01, 2026

Existing diffusion-based language models for code infilling are constrained by fixed-length masks, limiting their ability to generate variable-length outputs. This work proposes DreamOn, a framework that introduces two lightweight length-control states into the diffusion process, enabling dynamic adjustment of the generated sequence length without modifying the underlying model architecture. Experiments on DreamCoder-7B and DiffuCoder-7B demonstrate that DreamOn achieves performance comparable to state-of-the-art autoregressive models on the HumanEval-Infilling and SantaCoder-FIM benchmarks, while closely approaching the ideal performance attainable when the ground-truth target length is known. These results significantly enhance the practicality of diffusion models for real-world code infilling tasks.

14 citations1 influentialRead paper

TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading

Apr 22, 2024arXiv.org

Existing edge caching mechanisms for AI model delivery in 5G/6G networks overlook parameter-block reuse—e.g., shared knowledge units across CNNs or LLMs—leading to low storage efficiency and limited cache hit rates under stringent latency constraints. Method: We propose a parameter-sharing-aware edge model caching framework that, for the first time, formulates parameter-block reuse as a submodular optimization problem. We design a polynomial-time algorithm with theoretical approximation guarantees and provide a general greedy solution. The framework jointly optimizes storage efficiency and service latency in multi-edge wireless networks. Results: Simulation results demonstrate that our approach significantly improves cache hit rates over conventional content-based caching, validating the effectiveness and practicality of parameter-level sharing for edge AI deployment.

12 citationsRead paper
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