query-perturbation
Query Perturbation Method
An object-aware query perturbation method for cross-modal image-text retrieval with a PyTorch implementation.
1 stars
0 watching
1 forks
Language: Python
last commit: about 2 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
 mjacar/pytorch-nec | An implementation of an episodic control agent in PyTorch | 0 |
 mostaphabahadou/postenum | Automates system information gathering after gaining access to a Linux system. | 281 |
 nearai/torchfold | A PyTorch module for dynamic batching and optimized computation on deep neural networks | 221 |
 ypxie/pytorch-neucom | An implementation of the Differentiable Neural Computer architecture in PyTorch | 94 |
 maximumentropy/seq2seq-pytorch | An implementation of Sequence to Sequence models in PyTorch with various attention mechanisms and extensions for machine translation tasks. | 738 |
 luuuyi/cbam.pytorch | PyTorch implementation of the CBAM module for refining feature maps in deep networks | 1,353 |
 mchong6/soat | This repository provides a PyTorch implementation of an image manipulation technique using a pretrained StyleGAN model. | 380 |
 machinalis/quepy | Transforms natural language questions into query templates for various database systems. | 1,255 |
 alexeyco/pig | A pgx wrapper that simplifies executing and scanning query results in PostgreSQL databases | 16 |
 nnizhang/smac | A Python implementation of a salient object detection algorithm utilizing RGB-D data | 45 |
 soumyaxyz/query-segmenter | An unsupervised method to segment queries in search results based on query logs. | 1 |
 javeywang/pyramid-attention-networks-pytorch | An implementation of a deep learning model using PyTorch for semantic segmentation tasks. | 237 |
 locuslab/optnet | A PyTorch module that adds differentiable optimization as a layer to neural networks | 517 |
 kazuto1011/pspnet-pytorch | Re-implementation of a deep learning model for semantic segmentation using PyTorch. | 52 |
 msamogh/nonechucks | Library that provides dynamic data cleaning and filtering capabilities for PyTorch datasets and samplers | 378 |