26 citations · 75 across the 25 of their papers we have counts for
9 papers · 1 filter
H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions
Changlin Li, Guangyang Wu, Yanan Sun +3
Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real…
Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers
Tao Tang, Changlin Li, Guangrun Wang +3
Automatic data augmentation (AutoAugment) strategies are indispensable in supervised data-efficient training protocols of vision transformers, and have led to state-of-the-art resu…
DeViT: Deformed Vision Transformers in Video Inpainting
Jiayin Cai, Changlin Li, Xin Tao +2
This paper proposes a novel video inpainting method. We make three main contributions: First, we extended previous Transformers with patch alignment by introducing Deformed Patch-b…
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
Mingjie Li, Rui Liu, Guangsi Shi +5
Long-term time-series forecasting (LTSF) is fundamental to various real-world applications, where Transformer-based models have become the dominant framework due to their ability t…
Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism
Binbin Yang, Xinchi Deng, Han Shi +6
Continual learning is a challenging real-world problem for constructing a mature AI system when data are provided in a streaming fashion. Despite recent progress in continual class…
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
Takashi Isobe, Xu Jia, Xin Tao +6
Temporal modeling is crucial for video super-resolution. Most of the video super-resolution methods adopt the optical flow or deformable convolution for explicitly motion compensat…
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search
Minbin Huang, Zhijian Huang, Changlin Li +4
Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network d…
Beyond Fixation: Dynamic Window Visual Transformer
Pengzhen Ren, Changlin Li, Guangrun Wang +4
Recently, a surge of interest in visual transformers is to reduce the computational cost by limiting the calculation of self-attention to a local window. Most current work uses a f…
Automated Progressive Learning for Efficient Training of Vision Transformers
Changlin Li, Bohan Zhuang, Guangrun Wang +3
Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, high-lighting the urgent need to develop efficient training methods for ViTs.…