activity
20172022
most citedMMDetection: Open MMLab Detection Toolbox and Benchmark

794 citations · 848 across the 9 of their papers we have counts for

collaborators

15 papers

cs.CL20221 cited

End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system

Zhengyi Zhang, Pan Zhou

End-to-end (E2E) speech recognition architectures assemble all components of traditional speech recognition system into a single model. Although it simplifies ASR system, it introd…

cs.IR202124 cited

Context-Aware Attention-Based Data Augmentation for POI Recommendation

Yang Li, Yadan Luo, Zheng Zhang +2

With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendati…

cs.CV20207 cited

Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

Zhao Zhang, Zemin Tang, Zheng Zhang +3

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks sti…

cs.LG20194 cited

Deep Self-representative Concept Factorization Network for Representation Learning

Yan Zhang, Zhao Zhang, Zheng Zhang +4

In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Ne…

cs.CV2019

Compressed DenseNet for Lightweight Character Recognition

Zhao Zhang, Zemin Tang, Yang Wang +3

Convolutional Recurrent Neural Network (CRNN) is a popular network for recognizing texts in images. Advances like the variant of CRNN, such as Dense Convolutional Network with Conn…

cs.CV20193 cited

Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning

Yulin Sun, Zhao Zhang, Weiming Jiang +4

In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning. To achieve powerful repr…