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Found 5 papers, 4 papers with code
Early Action Recognition with Action Prototypes
For example, on early recognition observing only the first 10% of each video, our method improves the SOTA by +2. 23 Top-1 accuracy on Something-Something-v2, +3. 55 on UCF-101, +3. 68 on SSsub21, and +5. 03 on EPIC-Kitchens-55, where prior work used either multi-modal inputs (e. g. optical-flow) or batched inference.
Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network
Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view.
Disentangling Motion, Foreground and Background Features in Videos
This paper introduces an unsupervised framework to extract semantically rich features for video representation.
Decomposing Motion and Content for Natural Video Sequence Prediction
To the best of our knowledge, this is the first end-to-end trainable network architecture with motion and content separation to model the spatiotemporal dynamics for pixel-level future prediction in natural videos.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Learning to Generate Long-term Future via Hierarchical Prediction
To avoid inherent compounding errors in recursive pixel-level prediction, we propose to first estimate high-level structure in the input frames, then predict how that structure evolves in the future, and finally by observing a single frame from the past and the predicted high-level structure, we construct the future frames without having to observe any of the pixel-level predictions.
