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Found 8 papers, 5 papers with code
DUT: Learning Video Stabilization by Simply Watching Unstable Videos
In this paper, we attempt to tackle the video stabilization problem in a deep unsupervised learning manner, which borrows the divide-and-conquer idea from traditional stabilizers while leveraging the representation power of DNNs to handle the challenges in real-world scenarios.
Bridging Composite and Real: Towards End-to-end Deep Image Matting
Furthermore, we provide a benchmark containing 2, 000 high-resolution real-world animal images and 10, 000 portrait images along with their manually labeled alpha mattes to serve as a test bed for evaluating matting model's generalization ability on real-world images.
Ranked #2 on
Image Matting
on AM-2K
Wide-angle Image Rectification: A Survey
In this paper, we comprehensively survey progress in wide-angle image rectification from transformation models to rectification methods.
Exposure Trajectory Recovery from Motion Blur
However, the motion information contained in a blurry image has yet to be fully explored and accurately formulated because: (i) the ground truth of dynamic motion is difficult to obtain; (ii) the temporal ordering is destroyed during the exposure; and (iii) the motion estimation from a blurry image is highly ill-posed.
Spatiotemporal Attacks for Embodied Agents
Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness.
Feedback Graph Convolutional Network for Skeleton-based Action Recognition
It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions.
Ranked #34 on
Skeleton Based Action Recognition
on NTU RGB+D 120
CDPM: Convolutional Deformable Part Models for Semantically Aligned Person Re-identification
Part-level representations are essential for robust person re-identification.
Saliency Propagation From Simple to Difficult
In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner.
