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20162024
most citedCross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video

6 citations · 27 across the 13 of their papers we have counts for

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12 papers · 1 filter

cs.CV20241 cited

T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Models

Zhongqi Wang, Jie Zhang, Shiguang Shan +1

While text-to-image diffusion models demonstrate impressive generation capabilities, they also exhibit vulnerability to backdoor attacks, which involve the manipulation of model ou…

cs.CV20242 cited

HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention

Xiaolong Tang, Meina Kan, Shiguang Shan +3

Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm, which predicts the future trajectory…

cs.CV2024

Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications

Minyang Hu, Hong Chang, Zong Guo +3

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL…

cs.CV2024

Progressive Conservative Adaptation for Evolving Target Domains

Gangming Zhao, Chaoqi Chen, Wenhao He +5

Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge seque…

cs.CV20242 cited

Glance and Focus: Memory Prompting for Multi-Event Video Question Answering

Ziyi Bai, Ruiping Wang, Xilin Chen

Video Question Answering (VideoQA) has emerged as a vital tool to evaluate agents' ability to understand human daily behaviors. Despite the recent success of large vision language…

cs.CV2023

Dual Compensation Residual Networks for Class Imbalanced Learning

Ruibing Hou, Hong Chang, Bingpeng Ma +2

Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution,…