A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

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Deep LearningCLIP4CMR
Overview

CLIP4CMR

A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

The original data and pre-calculated CLIP features are available at here. The train.pkl and test.pkl include image pixel features and text id features, and the clip_train.pkl and clip_test.pkl include 1024-dimensional image and text features.

User-friendly bulk RNAseq deconvolution using simulated annealing

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Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

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Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

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Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

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Code for the ICML 2021 paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"

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