102 citations · 135 across the 13 of their papers we have counts for
13 papers
Unified Normalization for Accelerating and Stabilizing Transformers
Qiming Yang, Kai Zhang, Chaoxiang Lan +5
Solid results from Transformers have made them prevailing architectures in various natural language and vision tasks. As a default component in Transformers, Layer Normalization (L…
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
Mengze Li, Tianbao Wang, Haoyu Zhang +9
Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing…
UWC: Unit-wise Calibration Towards Rapid Network Compression
Chen Lin, Zheyang Li, Bo Peng +4
This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance. Previous PTQ meth…
Scene-Adaptive Attention Network for Crowd Counting
Xing Wei, Yuanrui Kang, Jihao Yang +4
In recent years, significant progress has been made on the research of crowd counting. However, as the challenging scale variations and complex scenes existed in crowds, neither tr…
SOIT: Segmenting Objects with Instance-Aware Transformers
Xiaodong Yu, Dahu Shi, Xing Wei +3
This paper presents an end-to-end instance segmentation framework, termed SOIT, that Segments Objects with Instance-aware Transformers. Inspired by DETR \cite{carion2020end}, our m…
LUAI Challenge 2021 on Learning to Understand Aerial Images
Gui-Song Xia, Jian Ding, Ming Qian +33
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in a…
InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation
Dahu Shi, Xing Wei, Xiaodong Yu +3
Multi-person pose estimation is an attractive and challenging task. Existing methods are mostly based on two-stage frameworks, which include top-down and bottom-up methods. Two-sta…
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition
Hui Jiang, Yunlu Xu, Zhanzhan Cheng +5
Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without add…
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
Liang Qiao, Zaisheng Li, Zhanzhan Cheng +6
Table structure recognition is a challenging task due to the various structures and complicated cell spanning relations. Previous methods handled the problem starting from elements…
PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images
Pengbo Zhao, Zhenshen Qu, Yingjia Bu +3
Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ra…
MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion
Zehan Zhang, Ming Zhang, Zhidong Liang +4
3D vehicle detection based on multi-modal fusion is an important task of many applications such as autonomous driving. Although significant progress has been made, we still observe…
Breast mass detection in digital mammography based on anchor-free architecture
Haichao Cao
Background and Objective: Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients survival rat…
Extreme Network Compression via Filter Group Approximation
Bo Peng, Wenming Tan, Zheyang Li +3
In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs…