Jingyuan Chen

Text-Based Person Retrieval (TBPR) aims to locate a person in an image database based on a natural language description. While effective in theory, TBPR faces substantial challenges in real-world scenarios due to noisy correspondences —misaligned or weakly related image–text pairs—that significantly degrade retrieval performance. Existing methods often overemphasize hard negative mining, which inadvertently magnifies the impact of such noise. To address this issue, we propose Dynamic Uncertainty with Noisy Correspondences (DUNC) , a novel framework that incorporates two key components: (1) Cross-modal Evidential Learning (CEL) , which models bidirectional alignment uncertainty using a Dirichlet distribution to capture the confidence in image–text similarity, and (2) Dynamic Robust Loss (DRL) , which adaptively selects and aggregates hard negative samples to reduce the influence of noisy instances and improve model robustness. Unlike conventional global-alignment approaches, DUNC exploits fine-grained local correspondences to enhance semantic alignment between modalities. By integrating uncertainty-aware modeling and adaptive contrastive supervision, our method is capable of effectively disentangling noisy from reliable training pairs. Extensive experiments conducted on three benchmark datasets—CUHK-PEDES, ICFG-PEDES, and RSTPReid—demonstrate that DUNC consistently achieves state-of-the-art performance and exhibits strong robustness across a wide range of noise conditions. Code is publicly available at https://github.com/zqxie77/DUNC

Audio-Visual Speech Recognition (AVSR) has been studied for a long time in the literature. By leveraging the complementary information from both acoustic and visual modalities, this approach offers a promising solution for robust speech transcription. While recent AVSR models have achieved impressive performance on large-scale, uniformly distributed datasets, they often overlook the challenges posed by real-world scenarios—where data is collected across multiple sessions and environments, leading to significant domain shifts and heterogeneous distributions. Such heterogeneity can result in catastrophic forgetting and hinder the generalization ability of the conventional models. To bridge this gap, we introduce the Continual Audio-Visual Speech Recognition (CL-AVSR) problem, which formulates AVSR as a continual learning task. We establish a dedicated benchmark for CL-AVSR by designing three experimental scenarios that reflect real-world challenges: introducing varying background noise for the audio stream, degrading video quality for the visual stream, and dividing tasks by speaker characteristics to jointly affect both modalities. These scenarios systematically evaluate the model’s ability to adapt and retain knowledge across dynamic and non-stationary data streams. To address the unique challenges of CL-AVSR, we propose the Interaction-enhanced Multimodal Prompt learning (IMP) framework. IMP builds upon a pre-trained AV-HuBERT backbone and integrates task-relevant soft prompts with cross-modal and cross-task interactions, enabling efficient knowledge transfer from high-quality source domains to typical low-quality target domains with minimal parameter overhead. The interactive prompts facilitate fine-grained alignment and adaptation between modalities and tasks, while contrastive regularization further mitigates catastrophic forgetting. Furthermore, we devise a multi-modal prompt selection strategy that leverages clustering-based feature analysis, empowering the model to dynamically select optimal prompts for unseen data distributions during inference. Extensive experiments on the LRS2 dataset demonstrate that IMP achieves substantial improvements over strong baselines, setting new state-of-the-art performance in all CL-AVSR scenarios. Our results highlight the effectiveness of IMP in enhancing continual learning capabilities for AVSR, paving the way for more robust and adaptable multi-modal speech recognition systems in real-world applications.

Graph domain adaptation (GDA) has achieved great attention due to its effectiveness in addressing the domain shift between train and test data. A significant bottleneck in existing graph domain adaptation methods is their reliance on source-domain data, which is often unavailable due to privacy or security concerns. This limitation has driven the development of Test-Time Graph Domain Adaptation (TT-GDA), which aims to transfer knowledge without accessing the source examples. Inspired by the generative power of large language models (LLMs), we introduce a novel framework that reframes TT-GDA as a generative graph restoration problem, "restoring the target graph to its pristine, source-domain-like state". There are two key challenges: (1) We need to construct a reasonable graph restoration process and design an effective encoding scheme that an LLM can understand, bridging the modality gap. (2) We need to devise a mechanism to ensure the restored graph acquires the intrinsic features of the source domain, even without access to the source data. To ensure the effectiveness of graph restoration, we propose GRAIL, that restores the target graph into a state that is well-aligned with the source domain. Specifically, we first compress the node representations into compact latent features and then use a graph diffusion process to model the graph restoration process. Then a quantization module encodes the restored features into discrete tokens. Building on this, an LLM is fine-tuned as a generative restorer to transform a "noisy" target graph into a "native" one. To further improve restoration quality, we introduce a reinforcement learning process guided by specialized alignment and confidence rewards. Extensive experiments demonstrate the effectiveness of our approach across various datasets.

Knowledge tracing (KT) aims to predict learners' future performance based on historical learning interactions. However, existing KT models predominantly focus on data from a single course, limiting their ability to capture a comprehensive understanding of learners' knowledge states. In this paper, we propose TransKT, a contrastive cross-course knowledge tracing method that leverages concept graph guided knowledge transfer to model the relationships between learning behaviors across different courses, thereby enhancing knowledge state estimation. Specifically, TransKT constructs a cross-course concept graph by leveraging zero-shot Large Language Model (LLM) prompts to establish implicit links between related concepts across different courses. This graph serves as the foundation for knowledge transfer, enabling the model to integrate and enhance the semantic features of learners' interactions across courses. Furthermore, TransKT includes an LLM-to-LM pipeline for incorporating summarized semantic features, which significantly improves the performance of Graph Convolutional Networks (GCNs) used for knowledge transfer. Additionally, TransKT employs a contrastive objective that aligns single-course and cross-course knowledge states, thereby refining the model's ability to provide a more robust and accurate representation of learners' overall knowledge states. Our code and datasets are available at https://github.com/DQYZHWK/TransKT/.