8 citations · 21 across the 10 of their papers we have counts for
11 papers
Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models
Jiayi Guo, Xingqian Xu, Yifan Pu +6
Recently, diffusion models have made remarkable progress in text-to-image (T2I) generation, synthesizing images with high fidelity and diverse contents. Despite this advancement, l…
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation
Shenzhi Wang, Chang Liu, Zilong Zheng +7
Recent breakthroughs in large language models (LLMs) have brought remarkable success in the field of LLM-as-Agent. Nevertheless, a prevalent assumption is that the information proc…
Computation-efficient Deep Learning for Computer Vision: A Survey
Yulin Wang, Yizeng Han, Chaofei Wang +3
Over the past decade, deep learning models have exhibited considerable advancements, reaching or even exceeding human-level performance in a range of visual perception tasks. This…
Latency-aware Unified Dynamic Networks for Efficient Image Recognition
Yizeng Han, Zeyu Liu, Zhihang Yuan +4
Dynamic computation has emerged as a promising avenue to enhance the inference efficiency of deep networks. It allows selective activation of computational units, leading to a redu…
Zero-shot Generative Model Adaptation via Image-specific Prompt Learning
Jiayi Guo, Chaofei Wang, You Wu +6
Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain. It does not require any target-d…
Efficient Knowledge Distillation from Model Checkpoints
Chaofei Wang, Qisen Yang, Rui Huang +2
Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers). As empirically there exists a strong…
Learning to Weight Samples for Dynamic Early-exiting Networks
Yizeng Han, Yifan Pu, Zihang Lai +6
Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks…
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment
Jiayu Xiao, Liang Li, Chaofei Wang +2
Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domai…
Learn From the Past: Experience Ensemble Knowledge Distillation
Chaofei Wang, Shaowei Zhang, Shiji Song +1
Traditional knowledge distillation transfers "dark knowledge" of a pre-trained teacher network to a student network, and ignores the knowledge in the training process of the teache…
Fine-Grained Few Shot Learning with Foreground Object Transformation
Chaofei Wang, Shiji Song, Qisen Yang +2
Traditional fine-grained image classification generally requires abundant labeled samples to deal with the low inter-class variance but high intra-class variance problem. However,…
CAM-loss: Towards Learning Spatially Discriminative Feature Representations
Chaofei Wang, Jiayu Xiao, Yizeng Han +3
The backbone of traditional CNN classifier is generally considered as a feature extractor, followed by a linear layer which performs the classification. We propose a novel loss fun…