Python Iccv21 Resources

NeuralGIF Code for Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing(ICCV21) We present Neural Generalized Implicit F

Viewpoint Invariant Dense Matching for Visual Geolocalization: PyTorch implementation This is the implementation of the ICCV21 paper: G Berton, C. Mas

Monocular, One-stage, Regression of Multiple 3D People ROMP, accepted by ICCV 2021, is a concise one-stage network for multi-person 3D mesh recovery f

CPN (ICCV2021) This is an implementation of Complementary Patch for Weakly Supervised Semantic Segmentation, which is accepted by ICCV2021 poster. Thi

ICCVW21-TradiCV-Survey-of-LiDAR-Cluster Motivation In contrast to popular end-to-end deep learning LiDAR panoptic segmentation solutions, we propose a

Differentiable SVD Introduction This repository contains: The official Pytorch implementation of ICCV21 paper Why Approximate Matrix Square Root Outpe

Social-NCE + CrowdNav Website | Paper | Video | Social NCE + Trajectron | Social NCE + STGCNN This is an official implementation for Social NCE: Contr

VAC_CSLR This repo holds codes of the paper: Visual Alignment Constraint for Continuous Sign Language Recognition.(ICCV 2021) [paper] Prerequisites Th

Implementation of ICCV 2021 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers arxiv This repository is based on detr Recently, DETR

RetrievalFuse Paper | Project Page | Video RetrievalFuse: Neural 3D Scene Reconstruction with a Database Yawar Siddiqui, Justus Thies, Fangchang Ma, Q

Table of Content Introduction Getting Started Datasets Installation Experiments Training & Testing Pretrained models Texture fine-tuning Demo Toward R

Self-Calibrating Neural Radiance Fields, ICCV, 2021 Project Page | Paper | Video Author Information Yoonwoo Jeong [Google Scholar] Seokjun Ahn [Google

Ranking Models in Unlabeled New Environments Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch 1.7.0 + torchivision 0.8.1

Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks.