2k
84 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Video-to-Video Synthesis
NVIDIA/vid2vid
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NeurIPS 2018
We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e. g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video.
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
Since convolutional layers of the neural network only need to operate on low-resolution inputs and outputs, the cost of memory and computing power is thus well suppressed.
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
vinthony/ghost-free-shadow-removal
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With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images.
Hyena Hierarchy: Towards Larger Convolutional Language Models
Recent advances in deep learning have relied heavily on the use of large Transformers due to their ability to learn at scale.
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
UKPLab/argument-reasoning-comprehension-task
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NAACL 2018
On this basis, we present a new challenging task, the argument reasoning comprehension task.
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
Xiangtaokong/ClassSR
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CVPR 2021
On this basis, we propose a new solution pipeline -- ClassSR that combines classification and SR in a unified framework.
Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce
In this paper, we present a unified end-to-end approach to build a large scale Visual Search and Recommendation system for e-commerce.
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications.
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies.
Vision-and-Dialog Navigation
mmurray/cvdn
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To train agents that search an environment for a goal location, we define the Navigation from Dialog History task.
