Projects sharing features with DANet

This is the implementation for PyTroch 0.4.1. - The HRNet OCR version ia available here. - The PyTroch 1.1 version is available here.

Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation

Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap

A repository for the code used to create and train the model defined in 'Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification' from AAAI 2021 (See the Paper section).

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

The table prvoides the models and results of various models on CIFAR100. Learning rate =0.1 and will be divided by 10 every 70 epochs. Total 300 epochs. Using SGD optimizer, momentum=0.9, weight_decay=5e-4. Loss is CrossEntropyLoss. Batch-size=512.

Code for the paper MSAF: Multimodal Split Attention Fusion. This is our implementation of the MSAF module and the three MSAF-powered multimodal networks.

Keras implementation of Deeplab v3+ with pretrained weights

This repository is a PyTorch implementation of our coordinate attention (will appear in CVPR2021).

Pytorch Implementation -- All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation, CVPR 2019

Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions WACV 2023

1st to MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task. (MICCAI 2019) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020) https://teacher.bupt.edu.cn/zhuchuang/en/index.htm

PyTorch implementation of the paper "FcaNet: Frequency Channel Attention Networks".

Created by Charles R. Qi , Wei Liu , Chenxia Wu , Hao Su and Leonidas J. Guibas from Stanford University and Nuro Inc.

PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without voxelization. It provides a system for 3D object classification, semantic segmentation frameworks for partitioning clouds into categories, and tools for visualizing 3D shapes. The project utilizes a transform network to align point clouds into a canonical coordinate space and employs symmetric-function-based aggregation to condense point-wise features into global vectors regardless of point order. It also features a multi-scale grouping architecture to extract hierarchical geometric

PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da

Mask RCNN in TensorFlow

Official Implementation of PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation

The official PyTorch code implementation for TIP20' Submission: "Person Re-identification via Attention Pyramid"