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Found 4 papers, 0 papers with code
Tracking Objects and Activities with Attention for Temporal Sentence Grounding
Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects.
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding
All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning.
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds
3D deep models consuming point clouds have achieved sound application effects in computer vision.
Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care
We developed a machine learning algorithm, based on a deep Reinforcement Learning (RL), for continuous management of oxygen flow rate for critical ill patients under intensive care, which can identify the optimal personalized oxygen flow rate with strong potentials to reduce mortality rate relative to the current clinical practice.
