Descriptive
331 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
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering
In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models' predictions, and compare these with human performance.
Greedy Search for Descriptive Spatial Face Features
Spatial features are derived from displacements of facial landmarks, and carry geometric information.
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks
cesc-park/attend2u
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CVPR 2017
We address personalization issues of image captioning, which have not been discussed yet in previous research.
Eye In-Painting with Exemplar Generative Adversarial Networks
This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs).
Inferencing Based on Unsupervised Learning of Disentangled Representations
tohinz/Bidirectional-InfoGAN
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Combining Generative Adversarial Networks (GANs) with encoders that learn to encode data points has shown promising results in learning data representations in an unsupervised way.
Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
sacmehta/YNet
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In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis.
Data-to-Text Generation with Content Selection and Planning
ratishsp/data2text-plan-py
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Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order.
General audio tagging with ensembling convolutional neural network and statistical features
Audio tagging is challenging due to the limited size of data and noisy labels.
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
zgojcic/3DSmoothNet
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CVPR 2019
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
Understanding and Controlling Memory in Recurrent Neural Networks
Finally, we propose a novel regularization technique that is based on the relation between hidden state speeds and memory longevity.
