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Deep Back-Projection Networks for Super-Resolution
TLDR
It is shown that extending the idea to allow concatenation of features across up- and downsampling stages (Dense DBPN) allows us to reconstruct further improve super-resolution, yielding superior results and in particular establishing new state of the art results for large scaling factors such as 8× across multiple data sets.
FractalNet: Ultra-Deep Neural Networks without Residuals
TLDR
In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks.
Learning Representations for Automatic Colorization
TLDR
A fully automatic image colorization system that leverages recent advances in deep networks, exploiting both low-level and semantic representations, and explores colorization as a vehicle for self-supervised visual representation learning.
Fast pose estimation with parameter-sensitive hashing
TLDR
A new algorithm is introduced that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task, and can rapidly and accurately estimate the articulated pose of human figures from a large database of example images.
Recurrent Back-Projection Network for Video Super-Resolution
We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that
Feedforward semantic segmentation with zoom-out features
TLDR
This work introduces a purely feed-forward architecture for semantic segmentation that exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference.
A unified learning framework for real time face detection and classification
TLDR
This paper presents progress toward an integrated, robust, real-time face detection and demographic analysis system and combines estimates from many facial detections in order to reduce the error rate.
Face Recognition from Long-Term Observations
TLDR
This work addresses the problem of face recognition from a large set of images obtained over time - a task arising in many surveillance and authentication applications and proposes an information-theoretic algorithm that classifies sets of images using the relative entropy between the estimated density of the input set and that of stored collections of images for each class.
Diverse M-Best Solutions in Markov Random Fields
TLDR
This paper proposes an algorithm for the Diverse M-Best problem, which involves finding a diverse set of highly probable solutions under a discrete probabilistic model and shows that for certain families of dissimilarity functions the authors can guarantee that these solutions can be found as easily as the MAP solution.
Style Transfer by Relaxed Optimal Transport and Self-Similarity
TLDR
The results indicate that for any desired level of content preservation, the proposed Style Transfer by Relaxed Optimal Transport and Self-Similarity (STROTSS), a new optimization-based style transfer algorithm, provides higher quality stylization than prior work.
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