output
20182025
most citedMulti-level Semantic Feature Augmentation for One-shot Learning

256 citations

5 papers

cs.CV2025★ 4 cited

Multi-modal Fusion and Query Refinement Network for Video Moment Retrieval and Highlight Detection

Yifang Xu, Yunzhuo Sun, Benxiang Zhai +3

Given a video and a linguistic query, video moment retrieval and highlight detection (MR&HD) aim to locate all the relevant spans while simultaneously predicting saliency scores. M…

cs.CV2019★ 48 cited

Fast Video Crowd Counting with a Temporal Aware Network

Xingjiao Wu, Baohan Xu, Yingbin Zheng +3

Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting. With the ubiqui…

cs.LG2019★ 9 cited

Black-box Adversarial Attacks on Video Recognition Models

Linxi Jiang, Xingjun Ma, Shaoxiang Chen +2

Deep neural networks (DNNs) are known for their vulnerability to adversarial examples. These are examples that have undergone small, carefully crafted perturbations, and which can…

cs.CV2018★ 26 cited

Attention-Aware Generalized Mean Pooling for Image Retrieval

Yinzheng Gu, Chuanpeng Li, Jinbin Xie

It has been shown that image descriptors extracted by convolutional neural networks (CNNs) achieve remarkable results for retrieval problems. In this paper, we apply attention mech…

cs.CV2018★ 256 cited

Multi-level Semantic Feature Augmentation for One-shot Learning

Zitian Chen, Yanwei Fu, Yinda Zhang +3

The ability to quickly recognize and learn new visual concepts from limited samples enables humans to swiftly adapt to new environments. This ability is enabled by semantic associa…