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20182021
most citedBeyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss

19 citations · 39 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CV20218 cited

Goal-Oriented Gaze Estimation for Zero-Shot Learning

Yang Liu, Lei Zhou, Xiao Bai +4

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Since semantic knowledge is built on attributes sha…

cs.CV20202 cited

Multi-layer Feature Aggregation for Deep Scene Parsing Models

Litao Yu, Yongsheng Gao, Jun Zhou +2

Scene parsing from images is a fundamental yet challenging problem in visual content understanding. In this dense prediction task, the parsing model assigns every pixel to a catego…

cs.CV20203 cited

Parameter Efficient Deep Neural Networks with Bilinear Projections

Litao Yu, Yongsheng Gao, Jun Zhou +1

Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy. Given a proper deep learning framework, it is generally possible to increase t…

cs.CV202019 cited

Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss

Cheng Yan, Guansong Pang, Xiao Bai +2

Person Re-IDentification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key…

cs.CV2020

Information Bottleneck Constrained Latent Bidirectional Embedding for Zero-Shot Learning

Yang Liu, Lei Zhou, Xiao Bai +3

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Though many ZSL methods rely on a direct mapping be…

cs.CV2020

A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Zewen Li, Wenjie Yang, Shouheng Peng +1

Convolutional Neural Network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limit…