105 citations · 217 across the 26 of their papers we have counts for
13 papers · 1 filter
Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline
Xiao Wang, Shiao Wang, Chuanming Tang +4
Tracking using bio-inspired event cameras has drawn more and more attention in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or direc…
SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
Xiao Wang, Yao Rong, Zongzhen Wu +4
Event camera-based pattern recognition is a newly arising research topic in recent years. Current researchers usually transform the event streams into images, graphs, or voxels, an…
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification
Yunpeng Zhai, Peixi Peng, Mengxi Jia +4
Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discrimina…
Album Storytelling with Iterative Story-aware Captioning and Large Language Models
Munan Ning, Yujia Xie, Dongdong Chen +5
This work studies how to transform an album to vivid and coherent stories, a task we refer to as "album storytelling". While this task can help preserve memories and facilitate exp…
Temporal Contrastive Learning for Spiking Neural Networks
Haonan Qiu, Zeyin Song, Yanqi Chen +6
Biologically inspired spiking neural networks (SNNs) have garnered considerable attention due to their low-energy consumption and spatio-temporal information processing capabilitie…
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning
Zeyin Song, Yifan Zhao, Yujun Shi +3
Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework t…
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Munan Ning, Donghuan Lu, Yujia Xie +6
Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data. Unfortunately, mapping the target-domain distribution to the source-domain unconditionally…
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Yi Chang, Yun Guo, Yuntong Ye +5
Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less ge…
Meta Architecture for Point Cloud Analysis
Haojia Lin, Xiawu Zheng, Lijiang Li +5
Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any…
Attention Spiking Neural Networks
Man Yao, Guangshe Zhao, Hengyu Zhang +5
Benefiting from the event-driven and sparse spiking characteristics of the brain, spiking neural networks (SNNs) are becoming an energy-efficient alternative to artificial neural n…
Masked Autoencoders for Point Cloud Self-supervised Learning
Yatian Pang, Wenxiao Wang, Francis E. H. Tay +3
As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a ne…
Training-free Transformer Architecture Search
Qinqin Zhou, Kekai Sheng, Xiawu Zheng +5
Recently, Vision Transformer (ViT) has achieved remarkable success in several computer vision tasks. The progresses are highly relevant to the architecture design, then it is worth…
Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity
Yuntong Ye, Changfeng Yu, Yi Chang +4
Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer.…