6 papers
Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection
Jiawei Gu, Qilin Zhao, Tengkuo Guo +6
Video anomaly detection (VAD) aims to localize anomalous events in untrimmed videos. Vision-language models (VLMs) provide rich visual understanding for training-free VAD, but exis…
Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation
Hongmei Yin, Tingliang Feng, Fan Lyu +4
In this work, we focus on continual semantic segmentation (CSS), where segmentation networks are required to continuously learn new classes without erasing knowledge of previously…
Controllable Continual Test-Time Adaptation
Ziqi Shi, Fan Lyu, Ye Liu +5
Continual Test-Time Adaptation (CTTA) is an emerging and challenging task where a model trained in a source domain must adapt to continuously changing conditions during testing, wi…
Overcoming Domain Drift in Online Continual Learning
Fan Lyu, Daofeng Liu, Linglan Zhao +5
Online Continual Learning (OCL) empowers machine learning models to acquire new knowledge online across a sequence of tasks. However, OCL faces a significant challenge: catastrophi…
Elastic Multi-Gradient Descent for Parallel Continual Learning
Fan Lyu, Wei Feng, Yuepan Li +4
The goal of Continual Learning (CL) is to continuously learn from new data streams and accomplish the corresponding tasks. Previously studied CL assumes that data are given in sequ…
Long-Tailed Learning as Multi-Objective Optimization
Weiqi Li, Fan Lyu, Fanhua Shang +2
Real-world data is extremely imbalanced and presents a long-tailed distribution, resulting in models that are biased towards classes with sufficient samples and perform poorly on r…