Titlebook: Computer Vision – ECCV 2016 Workshops; Amsterdam, The Nethe Gang Hua,Hervé Jégou Conference proceedings 2016 Springer International Publish

REP

Making a Case for Learning Motion Representations with Phase use cases: (i) action recognition, (ii) motion prediction in static images, (iii) motion transfer in static images and, (iv) motion transfer in video. For each task we motivate the phase-based direction and provide a possible approach.

為現(xiàn)場(chǎng)

Improving Constrained Bundle Adjustment Through Semantic Scene Labelings on improving city-scale SLAM through the use of deep learning. More precisely, we propose to use CNN-based scene labeling to geometrically constrain bundle adjustment. Our experiments indicate a considerable increase in robustness and precision.

dilute

Halfhearted

Debility

The Dynamics of Change in Higher Education significant computational cost inherent to deployment of a state-of-the-art deep network for semantic labeling does not hinder interactivity thanks to suitable scheduling of the workload on an off-the-shelf PC platform equipped with two GPUs.

coagulation

情感脆弱

aphasia

Temporal Convolutional Networks: A Unified Approach to Action Segmentationles. Our model achieves superior or competitive performance using video or sensor data on three public action segmentation datasets and can be trained in a fraction of the time it takes to train an RNN.

amenity

吹牛者