70 citations · 139 across the 33 of their papers we have counts for
7 papers · 1 filter
Weakly Supervised Video Individual CountingWeakly Supervised Video Individual Counting
Xinyan Liu, Guorong Li, Yuankai Qi +5
Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. % Existing methods learn representations based on trajectory labels for individu…
Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection
Chen Zhang, Guorong Li, Yuankai Qi +4
The goal of weakly supervised video anomaly detection is to learn a detection model using only video-level labeled data. However, prior studies typically divide videos into fixed-l…
Semantic-aware SAM for Point-Prompted Instance Segmentation
Zhaoyang Wei, Pengfei Chen, Xuehui Yu +3
Single-point annotation in visual tasks, with the goal of minimizing labelling costs, is becoming increasingly prominent in research. Recently, visual foundation models, such as Se…
Towards Subject-Oriented Video Captioning via User-Specified Targets
Chang Teng, Yunchuan Ma, Guorong Li +3
Describing video content according to users' needs is a long-held goal. Although existing video captioning methods have made significant progress, the generated captions may not fo…
P2RBox: Point Prompt Oriented Object Detection with SAM
Guangming Cao, Xuehui Yu, Wenwen Yu +5
Single-point annotation in oriented object detection of remote sensing scenarios is gaining increasing attention due to its cost-effectiveness. However, due to the granularity ambi…
Towards Unified Token Learning for Vision-Language Tracking
Yaozong Zheng, Bineng Zhong, Qihua Liang +3
In this paper, we present a simple, flexible and effective vision-language (VL) tracking pipeline, termed \textbf{MMTrack}, which casts VL tracking as a token generation task. Trad…
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Di Wu, Pengfei Chen, Xuehui Yu +3
Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low ann…