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This repository contains the code for training and evaluating the various models presented in the paper IQA: Visual Question Answering in Interactive Environments. It also provides an interface for reading the questions and generating new questions if desired.
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
CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns
Code for the paper
The main features of facebookresearch/embodiedqa are: Computer Vision Research, Embodied Question Answering.
Projects with overlapping indexed features include: danielgordon10/thor-iqa-cvpr-2018 — This repository contains the code for training and evaluating the various models presented in the paper IQA: Visual… zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018. aimerykong/recurrent-pixel-embedding-for-instance-grouping — CVPR2018 - pixel embedding & grouping for structured prediction, e.g., instance segmentation. akanazawa/cmr — Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik. ahangchen/tfusion — CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns.