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20182023
most citedBeyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss

19 citations · 32 across the 6 of their papers we have counts for

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10 papers · 1 filter

cs.CV20235 cited

Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

Jiahe Li, Jiawei Zhang, Xiao Bai +2

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, rea…

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.CV2020

HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects

Suihanjin Yu, Youmin Zhang, Chen Wang +3

In optical flow estimation task, coarse-to-fine (C2F) warping strategy is widely used to deal with the large displacement problem and provides efficiency and speed. However, limite…

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

Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection

Guansong Pang, Cheng Yan, Chunhua Shen +2

Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of int…