134 citations · 895 across the 57 of their papers we have counts for
15 papers
Collaboration of Pre-trained Models Makes Better Few-shot Learner
Renrui Zhang, Bohao Li, Wei Zhang +4
Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regim…
Learning Degradation Representations for Image Deblurring
Dasong Li, Yi Zhang, Ka Chun Cheung +3
In various learning-based image restoration tasks, such as image denoising and image super-resolution, the degradation representations were widely used to model the degradation pro…
Frozen CLIP Models are Efficient Video Learners
Ziyi Lin, Shijie Geng, Renrui Zhang +6
Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conductin…
Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification
Renrui Zhang, Zhang Wei, Rongyao Fang +5
Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations using large-scale image-text pairs. It shows impressive per…
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
Jinguo Zhu, Xizhou Zhu, Wenhai Wang +4
To build an artificial neural network like the biological intelligence system, recent works have unified numerous tasks into a generalist model, which can process various tasks wit…
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
Hao Shao, Letian Wang, RuoBing Chen +2
Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of wh…