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20232026
most citedBatchNorm-based Weakly Supervised Video Anomaly Detection

4 citations · 9 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration

Xun Jiang, Yufan Gu, Disen Hu +5

Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues…

cs.CV2024★ 1 cited

DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing

Minghang Zhou, Tianyu Li, Chaofan Qiao +5

Multispectral oriented object detection faces challenges due to both inter-modal and intra-modal discrepancies. Recent studies often rely on transformer-based models to address the…

cs.CV2024

Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate Metric

Xiruo Jiang, Yazhou Yao, Xili Dai +3

Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding space for downstream tasks like classification, clustering, and retrieval. Prior literature pre…

cs.CV2023★ 1 cited

Hierarchical Graph Pattern Understanding for Zero-Shot VOS

Gensheng Pei, Fumin Shen, Yazhou Yao +3

The optical flow guidance strategy is ideal for obtaining motion information of objects in the video. It is widely utilized in video segmentation tasks. However, existing optical f…

cs.CV2023★ 4 cited

BatchNorm-based Weakly Supervised Video Anomaly Detection

Yixuan Zhou, Yi Qu, Xing Xu +3

In weakly supervised video anomaly detection (WVAD), where only video-level labels indicating the presence or absence of abnormal events are available, the primary challenge arises…

cs.CV2023★ 3 cited

MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection

Yixuan Zhou, Xing Xu, Jingkuan Song +2

Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training. Some UAD ap…

cs.CV2023

Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models

Jiabang He, Yi Hu, Lei Wang +4

Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However,…