12 citations · 38 across the 8 of their papers we have counts for
8 papers
Filter Pruning via Filters Similarity in Consecutive Layers
Xiaorui Wang, Jun Wang, Xin Tang +3
Filter pruning is widely adopted to compress and accelerate the Convolutional Neural Networks (CNNs), but most previous works ignore the relationship between filters and channels i…
HCL: Improving Graph Representation with Hierarchical Contrastive Learning
Jun Wang, Weixun Li, Changyu Hou +6
Contrastive learning has emerged as a powerful tool for graph representation learning. However, most contrastive learning methods learn features of graphs with fixed coarse-grained…
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
Yixuan Qiao, Shanshan Zhao, Jun Wang +8
This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage…
End-to-end speaker diarization with transformer
Yongquan Lai, Xin Tang, Yuanyuan Fu +1
Speaker diarization is connected to semantic segmentation in computer vision. Inspired from MaskFormer \cite{cheng2021per} which treats semantic segmentation as a set-prediction pr…
Visual-Semantic Transformer for Scene Text Recognition
Xin Tang, Yongquan Lai, Ying Liu +2
Modeling semantic information is helpful for scene text recognition. In this work, we propose to model semantic and visual information jointly with a Visual-Semantic Transformer (V…
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex
Yelin He, Xianbiao Qi, Jiaquan Ye +5
This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX. This competition has two sub-tasks: Table Structure Reconstruction (…
Learning Graph Normalization for Graph Neural Networks
Yihao Chen, Xin Tang, Xianbiao Qi +2
Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multip…
Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition
Bingcong Li, Xin Tang, Xianbiao Qi +2
Recently, inspired by Transformer, self-attention-based scene text recognition approaches have achieved outstanding performance. However, we find that the size of model expands rap…