Fast Video Object Segmentation with Temporal Aggregation Network and Dynamic Template Matching
+ Co-author of this work.
+ Formulation of zero-shot part discovery on a large-scale fine-grained shape segmentation benchmark.
+ A learning-based agglomerative clustering framework that learns to do part proposal and grouping from training categories and generalizes to unseen novel categories
+ Re-interpretation and connection of two top popular techniques proposed in CVPR 2018 (Non-local neural networks and SE-Net).
+ Unification of these the two techniques into a general framework.
+ Better instantiation of the general framework, which is about 50x faster than the non-local neural block, while achieving better accuracy than both techniques (non-local and SE-Net) on several recognition tasks such as ImageNet classification, COCO object detection and Kinetics action recognition.
+ The first coherent and end-to-end framework for similarity measure which combines all of the appearance, motion and interaction cues.
+ Properly redesign of feature representation for the tracklet-object pair.
+ Achieve the state-of-the-art multi-object tracking (MOT) results on all of the MOT15-17 leaderboards using few bells and whistles.
This paper proposes the first end-to-end deep framework for high dynamic range (HDR) imaging of dynamic scenes with _**large-scale foreground motions**_.
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