5 accepted papers
Large Vision Language Models (LVLMs) have achieved remarkable success in a wide range of downstream tasks that require multimodal interaction, but their powerful capabilities come with substantial computational and memory overhead, which hinders practical deployment. Among numerous acceleration tech
Video anomaly detection (VAD) aims at detecting anomalous events in videos. Most existing VAD methods distinguish anomalies by learning visual features of the video, which usually face several challenges in real-world applications. First, these methods are mostly scene-dependent, whose performances…
Semi-supervised video anomaly detection (SVAD) is a challenging computer vision task due to the diversity, randomness, and rarity of abnormal events in videos. A series of important SVAD methods follow the frame prediction strategy, where the model is trained only on normal videos and thus frames wi…
For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simult…
Markov Random Field (MRF) is an important tool and has been widely used in many vision tasks. Thus, the optimization of MRFs is a problem of fundamental importance. Recently, Veskler and Kumar et. al propose the range move algorithms, which are one of the most successful solvers to this problem. How…