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20172020
most citedGuided Attention Network for Object Detection and Counting on Drones

11 citations · 17 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV20202 cited

Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering

Yuanqiang Cai, Chang Liu, Weiqiang Wang +1

With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which h…

cs.CV2020

Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network

Ji Gan, Weiqiang Wang, Ke Lu

Chinese is one of the most widely used languages in the world, yet online handwritten Chinese character recognition (OLHCCR) remains challenging. To recognize Chinese characters, o…

cs.CV2020

Rethinking Object Detection in Retail Stores

Yuanqiang Cai, Longyin Wen, Libo Zhang +2

The convention standard for object detection uses a bounding box to represent each individual object instance. However, it is not practical in the industry-relevant applications in…

cs.CV201911 cited

Guided Attention Network for Object Detection and Counting on Drones

Yuanqiang Cai, Dawei Du, Libo Zhang +4

Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background. In this paper, we propose a new G…

cs.CV20191 cited

Learning Discriminative Features Via Weights-biased Softmax Loss

XiaoBin Li, WeiQiang Wang

Loss functions play a key role in training superior deep neural networks. In convolutional neural networks (CNNs), the popular cross entropy loss together with softmax does not exp…

cs.CV2019

Reinterpreting CTC training as iterative fitting

Hongzhu Li, Weiqiang Wang

The connectionist temporal classification (CTC) enables end-to-end sequence learning by maximizing the probability of correctly recognizing sequences during training. The outputs o…