Injecting Text Clues for Improving Anomalous Event Detection From Weakly Labeled Videos.

Video anomaly detection (VAD) aims at localizing the snippets containing anomalous events in long unconstrained videos. The weakly supervised (WS) setting, where solely video-level labels are available during training, has attracted considerable attention, owing to its satisfactory trade-off between the detection performance and annotation cost. However, due to lack of snippet-level dense labels, the existing WS-VAD methods still get easily stuck on the detection errors, caused by false alarms and incomplete localization.

Distilling Privileged Knowledge for Anomalous Event Detection From Weakly Labeled Videos.

Weakly supervised video anomaly detection (WS-VAD) aims to identify the snippets involving anomalous events in long untrimmed videos, with solely text video-level binary labels. A typical paradigm among the existing text WS-VAD methods is to employ multiple modalities as inputs, e.g.

Holistic-Guided Disentangled Learning With Cross-Video Semantics Mining for Concurrent First-Person and Third-Person Activity Recognition.

The popularity of wearable devices has increased the demands for the research on first-person activity recognition. However, most of the current first-person activity datasets are built based on the assumption that only the human-object interaction (HOI) activities, performed by the camera-wearer, are captured in the field of view. Since humans live in complicated scenarios, in addition to the first-person activities, it is likely that third-person activities performed by other people also appear.

Invertible Image Decolorization.

Invertible image decolorization is a useful color compression technique to reduce the cost in multimedia systems. Invertible decolorization aims to synthesize faithful grayscales from color images, which can be fully restored to the original color version. In this paper, we propose a novel color compression method to produce invertible grayscale images using invertible neural networks (INNs).

Incorporating multi-level CNN and attention mechanism for Chinese clinical named entity recognition.

Named entity recognition (NER) is a fundamental task in Chinese natural language processing (NLP) tasks. Recently, Chinese clinical NER has also attracted continuous research attention because it is an essential preparation for clinical data mining. The prevailing deep learning method for Chinese clinical NER is based on long short-term memory (LSTM) network.