36 citations · 117 across the 15 of their papers we have counts for
15 papers
Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification
Chengguo Yuan, Yu Jin, Zongzhen Wu +4
Recognizing target objects using an event-based camera draws more and more attention in recent years. Existing works usually represent the event streams into point-cloud, voxel, im…
AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+
Xiao Wang, Ying Wang, Ziwei Xuan +1
Unsupervised learning of vision transformers seeks to pretrain an encoder via pretext tasks without labels. Among them is the Masked Image Modeling (MIM) aligned with pretraining o…
Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey
Xiao Wang, Guangyao Chen, Guangwu Qian +5
With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as BERT, ViT, GPT, etc. Inspired by the success of these models in single domains…
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
Xiao Wang, Zongzhen Wu, Bo Jiang +5
The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large…
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
Chuanming Tang, Xiao Wang, Ju Huang +5
Combining the Color and Event cameras (also called Dynamic Vision Sensors, DVS) for robust object tracking is a newly emerging research topic in recent years. Existing color-event…
Learning Spatial-Frequency Transformer for Visual Object Tracking
Chuanming Tang, Xiao Wang, Yuanchao Bai +3
Recent trackers adopt the Transformer to combine or replace the widely used ResNet as their new backbone network. Although their trackers work well in regular scenarios, however, t…
Prompt-based Learning for Unpaired Image Captioning
Peipei Zhu, Xiao Wang, Lin Zhu +4
Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Existing works usually tackle this task using adversaria…
On the Importance of Asymmetry for Siamese Representation Learning
Xiao Wang, Haoqi Fan, Yuandong Tian +2
Many recent self-supervised frameworks for visual representation learning are based on certain forms of Siamese networks. Such networks are conceptually symmetric with two parallel…
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning
Xiao Wang, Yuhang Huang, Dan Zeng +1
As a representative self-supervised method, contrastive learning has achieved great successes in unsupervised training of representations. It trains an encoder by distinguishing po…
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
Lin Zhu, Xiao Wang, Yi Chang +3
Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports asynchronous, continuously per-pixel brightness changes called `events' with high temporal resolution…
CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation
Xiao Wang, Jingen Liu, Tao Mei +1
Some cognitive research has discovered that humans accomplish event segmentation as a side effect of event anticipation. Inspired by this discovery, we propose a simple yet effecti…
Contrastive Learning with Stronger Augmentations
Xiao Wang, Guo-Jun Qi
Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods have benefited from various data augmentations that…
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries
Qianjiang Hu, Xiao Wang, Wei Hu +1
Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are sel…
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
Xiao Wang, Daisuke Kihara, Jiebo Luo +1
Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obta…
Learning Generalized Transformation Equivariant Representations via Autoencoding Transformations
Guo-Jun Qi, Liheng Zhang, Xiao Wang
Transformation Equivariant Representations (TERs) aim to capture the intrinsic visual structures that equivary to various transformations by expanding the notion of {\em translatio…