xmu-xiaoma666/DTNet

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Python
License
MIT License
Created about 1 year ago
Updated 5 months ago

xmu-xiaoma666/DTNet

xmu-xiaoma666

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The official repository for “Image Captioning via Dynamic Path Customization”.

External-Attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

FightingCV-Paper-Reading

⭐⭐⭐FightingCV Paper Reading, which helps you understand the most advanced research work in an easier way 🍀 🍀 🍀

FightingCV-Course

深度学习/计算机视觉/多模态/机器学习/人工智能零基础理论/实战教程汇总分享

X-Dreamer

A pytorch implementation of “X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation”

RepMLP-pytorch

Pytorch implement ion of RepMLP

xmu-xiaoma666

X-Mesh

A pytorch implementation of “ X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance”

SDATR

Official Code for "Knowing what it is: Semantic-enhanced Dual Attention Transformer" (TMM2022)

ECCV2022-Paper-List

ECCV2022-Paper-List

LSTNet

Towards Local Visual Modeling for Image Captioning

ImageCaptionMetrics

This repository contains 2 tools: - A py3 Lib for NLP & image-caption metrics - Code for a two-tailed t-test with paired samples. It will reveals whether the difference of two results is significant. In this code, we complete evaluation code for Spice details(*i.e.*,Object, Relation, Attribute, Color, Count, and Size ).

CVAlgorithm

CV面试中的常见算法

MLP-Mixer-pytorch

Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision

MFM

An official implementation for "Knowing What to Learn: A Metric-Oriented Focal Mechanism for Image Captioning"

Leetcode_diary

Leetcode is all you need

Pytorch-Image-Classification

Pytorch-Image-Classification

Awesome-Model-Pytorch

pytorch implementation of deep learning models

Multimodal-Open-O1

Multimodal Open-O1 (MO1) is designed to enhance the accuracy of inference models by utilizing a novel prompt-based approach. This tool works locally and aims to create inference chains akin to those used by OpenAI-o1, but with localized processing power.

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