activity
20152022
most citedCrowd Counting and Density Estimation by Trellis Encoder-Decoder Network

78 citations · 326 across the 34 of their papers we have counts for

collaborators

49 papers

cs.CV20222 cited

Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning

Xiaoyue Duan, Guoliang Kang, Runqi Wang +4

Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a meta-model which may fast adapt to novel classes with only a few exemplars and meanwhile remain robust to a…

cs.CV202231 cited

Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer

Yanjing Li, Sheng Xu, Baochang Zhang +3

The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems…

cs.CV20222 cited

IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors

Sheng Xu, Yanjing Li, Bohan Zeng +5

Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and stu…

cs.CV202212 cited

FNeVR: Neural Volume Rendering for Face Animation

Bohan Zeng, Boyu Liu, Hong Li +5

Face animation, one of the hottest topics in computer vision, has achieved a promising performance with the help of generative models. However, it remains a critical challenge to g…

cs.CV2022

Bi-level Doubly Variational Learning for Energy-based Latent Variable Models

Ge Kan, Jinhu Lü, Tian Wang +5

Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process b…

cs.LG2022

Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation

Runqi Wang, Linlin Yang, Baochang Zhang +3

Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention. However, due to their complex architectures and large number…