Descriptive
325 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Descriptive
| Trend | Dataset | Best Model | Paper | Code | Compare |
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CRIPP-VQA
Most implemented papers
Conditional Generative Adversarial Nets
MaximeVandegar/Papers-in-100-Lines-of-Code
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Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
qianguih/voxelnet
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CVPR 2018
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Conditional Image Generation with PixelCNN Decoders
openai/pixel-cnn
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NeurIPS 2016
This work explores conditional image generation with a new image density model based on the PixelCNN architecture.
KPConv: Flexible and Deformable Convolution for Point Clouds
HuguesTHOMAS/KPConv
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ICCV 2019
Furthermore, these locations are continuous in space and can be learned by the network.
Contrastive Learning of Medical Visual Representations from Paired Images and Text
Existing work commonly relies on fine-tuning weights transferred from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize.
VITON: An Image-based Virtual Try-on Network
xthan/VITON
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CVPR 2018
We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy.
Fine-Tuning Language Models from Human Preferences
openai/lm-human-preferences
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Most work on reward learning has used simulated environments, but complex information about values is often expressed in natural language, and we believe reward learning for language is a key to making RL practical and safe for real-world tasks.
A Convolutional Attention Network for Extreme Summarization of Source Code
Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension.
Learning Deep Features for One-Class Classification
We propose a deep learning-based solution for the problem of feature learning in one-class classification.
Challenges in Data-to-Document Generation
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.
