Projects sharing features with AdaptSegNet

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

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

This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and

Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

This repository contains the TensorFlow code for our NeurIPS 2018 paper “Unsupervised Attention-guided Image-to-Image Translation”. This code is based on the TensorFlow implementation of CycleGAN provided by Harry Yang. You may need to train several times as the quality of the results are…

Project page for End-to-end Recovery of Human Shape and Pose

Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik

Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"

PyTorch implementation of UNet++ (Nested U-Net).

3DMV jointly combines RGB color and geometric information to perform 3D semantic segmentation of RGB-D scans. This work is based on our ECCV'18 paper, 3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation.

记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文

CVPR'18 ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

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

PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

"Zero-Shot" Super-Resolution using Deep Internal Learning

Attention-based Deep Multiple Instance Learning