28 citations · 128 across the 19 of their papers we have counts for
19 papers
Knowledge Transfer-Driven Few-Shot Class-Incremental Learning
Ye Wang, Yaxiong Wang, Guoshuai Zhao +1
Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The key of this task is effective knowle…
Logic Diffusion for Knowledge Graph Reasoning
Xiaoying Xie, Biao Gong, Yiliang Lv +3
Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are li…
Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution
Zixi Tuo, Huan Yang, Jianlong Fu +2
Existing real-world video super-resolution (VSR) methods focus on designing a general degradation pipeline for open-domain videos while ignoring data intrinsic characteristics whic…
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
Yixuan Pei, Zhiwu Qing, Jun Cen +6
Recent incremental learning for action recognition usually stores representative videos to mitigate catastrophic forgetting. However, only a few bulky videos can be stored due to t…
4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement
Chengxu Liu, Huan Yang, Jianlong Fu +1
Image enhancement aims at improving the aesthetic visual quality of photos by retouching the color and tone, and is an essential technology for professional digital photography. Re…
TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation
Chengxu Liu, Huan Yang, Jianlong Fu +1
Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includ…