activity
20192026
most citedImproving alignment of dialogue agents via targeted human judgements

133 citations · 266 across the 30 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.CV2023

Local Conditional Controlling for Text-to-Image Diffusion Models

Yibo Zhao, Liang Peng, Yang Yang +9

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level structure controls, e.g., edge and depth maps, to manipulate the genera…

cs.CV2023

SmoothVideo: Smooth Video Synthesis with Noise Constraints on Diffusion Models for One-shot Video Tuning

Liang Peng, Haoran Cheng, Zheng Yang +6

Recent one-shot video tuning methods, which fine-tune the network on a specific video based on pre-trained text-to-image models (e.g., Stable Diffusion), are popular in the communi…

cs.CV2023★ 7 cited

NormKD: Normalized Logits for Knowledge Distillation

Zhihao Chi, Tu Zheng, Hengjia Li +4

Logit based knowledge distillation gets less attention in recent years since feature based methods perform better in most cases. Nevertheless, we find it still has untapped potenti…

cs.CV2023

Learning Occupancy for Monocular 3D Object Detection

Liang Peng, Junkai Xu, Haoran Cheng +6

Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to fac…

cs.CV2023★ 4 cited

APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding

Hengjia Li, Tu Zheng, Zhihao Chi +5

Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to dir…

cs.CV2023★ 6 cited

CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention

Wenxiao Wang, Wei Chen, Qibo Qiu +5

While features of different scales are perceptually important to visual inputs, existing vision transformers do not yet take advantage of them explicitly. To this end, we first pro…