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Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
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 the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
Created by Charles R. Qi , Wei Liu , Chenxia Wu , Hao Su and Leonidas J. Guibas from Stanford University and Nuro Inc.
The main features of charlesq34/frustum-pointnets are: Computer Vision Research, Object Detection and Tracking, Semantic Segmentation.
Projects with overlapping indexed features include: wasidennis/adaptsegnet — Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight). zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… abhimanyudubey/confusion — Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification". ahangchen/tfusion — CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns. ai-liu/complex-yolo. agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018.