In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer.
Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis.
Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.
Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e. g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.
Ranked #4 on Object Detection on UAVDT
Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning.