25 citations · 28 across the 5 of their papers we have counts for
7 papers
Cycle Diffusion Model for Counterfactual Image Generation
Fangrui Huang, Alan Wang, Binxu Li +5
Deep generative models have demonstrated remarkable success in medical image synthesis. However, ensuring conditioning faithfulness and high-quality synthetic images for direct or…
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
Li-Heng Lin, Yuchen Cui, Amber Xie +2
Few-shot imitation learning relies on only a small amount of task-specific demonstrations to efficiently adapt a policy for a given downstream tasks. Retrieval-based methods come w…
Co-advise: Cross Inductive Bias Distillation
Sucheng Ren, Zhengqi Gao, Tianyu Hua +4
Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks. Howeve…
Improving Multi-Modal Learning with Uni-Modal Teachers
Chenzhuang Du, Tingle Li, Yichen Liu +4
Learning multi-modal representations is an essential step towards real-world robotic applications, and various multi-modal fusion models have been developed for this purpose. Howev…
On Feature Decorrelation in Self-Supervised Learning
Tianyu Hua, Wenxiao Wang, Zihui Xue +3
In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentat…
Exploiting Relationship for Complex-scene Image Generation
Tianyu Hua, Hongdong Zheng, Yalong Bai +3
The significant progress on Generative Adversarial Networks (GANs) has facilitated realistic single-object image generation based on language input. However, complex-scene generati…