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Found 10 papers, 3 papers with code
Taylor Swift: Taylor Driven Temporal Modeling for Swift Future Frame Prediction
While recurrent neural networks (RNNs) demonstrate outstanding capabilities in future video frame prediction, they model dynamics in a discrete time space and sequentially go through all frames until the desired future temporal step is reached.
Long Short View Feature Decomposition via Contrastive Video Representation Learning
We argue that a single representation to capture both types of features is sub-optimal, and propose to decompose the representation space into stationary and non-stationary features via contrastive learning from long and short views, i. e. long video sequences and their shorter sub-sequences.
Self-labeled Conditional GANs
This paper introduces a novel and fully unsupervised framework for conditional GAN training in which labels are automatically obtained from data.
3D CNNs with Adaptive Temporal Feature Resolutions
While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips.
Unsupervised Video Representation Learning by Bidirectional Feature Prediction
This paper introduces a novel method for self-supervised video representation learning via feature prediction.
Boosting Self-Supervised Learning via Knowledge Transfer
We use this framework to design a novel self-supervised task, which achieves state-of-the-art performance on the common benchmarks in PASCAL VOC 2007, ILSVRC12 and Places by a significant margin.
Representation Learning by Learning to Count
In this paper, we use two image transformations in the context of counting: scaling and tiling.
Reflection Separation and Deblurring of Plenoptic Images
In this paper, we address the problem of reflection removal and deblurring from a single image captured by a plenoptic camera.
Motion Deblurring in the Wild
The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown.
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
By following the principles of self-supervision, we build a convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles as a pretext task, which requires no manual labeling, and then later repurposed to solve object classification and detection.