activity
20162026
most citedSoftTriple Loss: Deep Metric Learning Without Triplet Sampling

92 citations · 291 across the 39 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.LG2023★ 1 cited

Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning

Hongyu Zang, Xin Li, Leiji Zhang +5

While bisimulation-based approaches hold promise for learning robust state representations for Reinforcement Learning (RL) tasks, their efficacy in offline RL tasks has not been up…

cs.CV2023

FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content

Yang Liu, Cheng Yu, Lei Shang +17

Recent advancement in personalized image generation have unveiled the intriguing capability of pre-trained text-to-image models on learning identity information from a collection o…

cs.CV2023★ 2 cited

TransFace++: Rethinking the Face Recognition Paradigm with a Focus on Accuracy, Efficiency, and Security

Jun Dan, Yang Liu, Baigui Sun +2

Face Recognition (FR) technology has made significant strides with the emergence of deep learning. Typically, most existing FR models are built upon Convolutional Neural Networks (…

cs.CV2023★ 1 cited

CostFormer:Cost Transformer for Cost Aggregation in Multi-view Stereo

Weitao Chen, Hongbin Xu, Zhipeng Zhou +4

The core of Multi-view Stereo(MVS) is the matching process among reference and source pixels. Cost aggregation plays a significant role in this process, while previous methods focu…

cs.CV2023★ 2 cited

The 3rd Anti-UAV Workshop & Challenge: Methods and Results

Jian Zhao, Jianan Li, Lei Jin +20

The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UA…

cs.CV2023★ 1 cited

PointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering

Zisheng Chen, Hongbin Xu, Weitao Chen +5

Semantic segmentation of point clouds usually requires exhausting efforts of human annotations, hence it attracts wide attention to the challenging topic of learning from unlabeled…