7 papers
Shrinking the Teacher: An Adaptive Teaching Paradigm for Asymmetric EEG-Vision Alignment
Lukun Wu, Jie Li, Ziqi Ren +2
Decoding visual features from EEG signals is a central challenge in neuroscience, with cross-modal alignment as the dominant approach. We argue that the relationship between visual…
DPTrack:Directional Kernel-Guided Prompt Learning for Robust Nighttime Aerial Tracking
Zhiqiang Zhu, Xinbo Gao, Wen Lu +3
Existing nighttime aerial trackers based on prompt learning rely solely on spatial localization supervision, which fails to provide fine-grained cues that point to target features…
StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning
Huaijie Wang, De Cheng, Guozhang Li +5
Video Class-Incremental Learning (VCIL) seeks to develop models that continuously learn new action categories over time without forgetting previously acquired knowledge. Unlike tra…
EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning
Huaijie Wang, De Cheng, Lingfeng He +4
Class-Incremental Learning (CIL) aims to enable AI models to continuously learn from sequentially arriving data of different classes over time while retaining previously acquired k…
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
Zhaoyang Wang, Jie Li, Wen Lu +3
State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on…
One-Step Diffusion-based Real-World Image Super-Resolution with Visual Perception Distillation
Xue Wu, Jingwei Xin, Zhijun Tu +4
Diffusion-based models have been widely used in various visual generation tasks, showing promising results in image super-resolution (SR), while typically being limited by dozens o…