activity
20202026
most citedEfficient Test-Time Model Adaptation without Forgetting

66 citations · 110 across the 13 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2025

ProCache: Constraint-Aware Feature Caching with Selective Computation for Diffusion Transformer Acceleration

Fanpu Cao, Yaofo Chen, Zeng You +1

Diffusion Transformers (DiTs) have achieved state-of-the-art performance in generative modeling, yet their high computational cost hinders real-time deployment. While feature cachi…

cs.CV2025

Sensitivity-Aware Post-Training Quantization for Deep Neural Networks

Zekang Zheng, Haokun Li, Yaofo Chen +2

Model quantization reduces neural network parameter precision to achieve compression, but often compromises accuracy. Existing post-training quantization (PTQ) methods employ itera…

cs.CV2024

Towards Long Video Understanding via Fine-detailed Video Story Generation

Zeng You, Zhiquan Wen, Yaofo Chen +4

Long video understanding has become a critical task in computer vision, driving advancements across numerous applications from surveillance to content retrieval. Existing video und…

cs.CV2021

Content-Aware Convolutional Neural Networks

Yong Guo, Yaofo Chen, Mingkui Tan +3

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverse…

cs.CV20215 cited

Contrastive Neural Architecture Search with Neural Architecture Comparators

Yaofo Chen, Yong Guo, Qi Chen +4

One of the key steps in Neural Architecture Search (NAS) is to estimate the performance of candidate architectures. Existing methods either directly use the validation performance…

cs.CV202035 cited

Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search

Yong Guo, Yaofo Chen, Yin Zheng +4

Neural architecture search (NAS) has become an important approach to automatically find effective architectures. To cover all possible good architectures, we need to search in an e…