Jun Liu

Parameter-efficient fine-tuning (PEFT) is promising for RGB-D semantic segmentation, as lightweight prompters enable frozen pre-trained RGB backbones to leverage massive RGB pretraining knowledge without full fine-tuning on limited paired RGB-D data. However, existing PEFT methods have two critical limitations: static modal fusion ignores the dynamic reliability of RGB and depth across scenes, leading to suboptimal performance in complex environments; conventional prompts lack structural awareness, causing the loss of edge and texture details essential for dense prediction. To solve these problems, we propose the Accurate Prompting Network (APNet) for precise prompt injection in frozen backbones with two core modules. A Modality Effectiveness Guider (MEG) conducts input-level modal reliability assessment and dynamically generates scene-adaptive modality weights by capturing scene characteristics (e.g., illumination, texture richness). A Structural Awareness Prompter (SAP) injects directional structural priors into prompts via multi-directional gating convolution, endowing prompts with explicit edge and texture information to match semantic segmentation demands. MEG and SAP collaboratively form a precise prompting mechanism that realizes dynamic modal contribution allocation and structural detail preservation, facilitating efficient and accurate cross-modal knowledge transfer to the frozen backbone. Extensive experiments on NYUDv2 and SUN RGB-D show that APNet achieves state-of-the-art mIoU of 59.6% and 52.6% with only 6.2M trainable parameters, realizing a superior trade-off between segmentation accuracy and parameter efficiency.

Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton features with static, class-level semantics. This coarse-grained alignment fails to bridge the domain shift between seen, unseen classes, thereby impeding the effective transfer of fine-grained visual knowledge. To address these limitations, we introduce DynaPURLS, a unified framework that establishes robust, multi-scale visual-semantic correspondences, dynamically refines them at inference time to enhance generalization. Our framework leverages a large language model to generate hierarchical textual descriptions that encompass both global movements, local body-part dynamics. Concurrently, an adaptive partitioning module produces fine-grained visual representations by semantically grouping skeleton joints. To fortify this fine-grained alignment against the train-test domain shift, DynaPURLS incorporates a dynamic refinement module. During inference, this module adapts textual features to the incoming visual stream via a lightweight learnable projection. This refinement process is stabilized by a confidence-aware, class-balanced memory bank, which mitigates error propagation from noisy pseudo-labels. Extensive experiments on three large-scale benchmark datasets, including NTU RGB+D 60/120, PKU-MMD, demonstrate that DynaPURLS significantly outperforms prior art, setting new state-of-the-art records. The source code is made publicly available at https://github.com/Alchemist0754/DynaPURLS

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance.