4 accepted papers
Video anomaly detection (VAD) aims to identify novel actions or events which are unseen during training. Existing mainstream VAD techniques typically focus on the global patterns with redundant details and struggle to generalize to unseen samples. In this paper, we propose a framework that identifie…
Due to the complexity in understanding human behaviors under limited observations and insufficient training data, video anomaly detection is challenging. Most of existing approaches solely rely on visual clues and suffer from noisy observations which easily lead to unreasonable predictions. In this…
Binary neural networks (BNNs) contribute a lot to the efficiency of image classification models. However, in dense predication tasks such as human pose estimation, predictions in different locations are coupled and rely on the extraction of features across entire images. As a result, more robust and…
"Existing Binary Neural Networks (BNNs) mainly operate on local convolutions with binarization function. However, such simple bit operations lack the ability of modeling contextual dependencies, which is critical for learning discriminative deep representations in vision models. In this work, we tac…