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.

YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone

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Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

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This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

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This repo is official PyTorch implementation of MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices(CVPRW 2021).

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Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

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This project is the PyTorch implementation of our CVPR 2022 paper:

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Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

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