Projects sharing features with CosFace

This is code for the PDSN in our paper. Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of face recognition over past years. However, existing general CNN face models generalize poorly for occlusions on variable facial areas. Inspired by the fact that the human visual…

FaceNet is a facial recognition framework designed to transform facial images into high-dimensional numerical embeddings for identity verification and recognition. It provides a deep learning face embedder that maps facial features into a Euclidean space where distance corresponds to facial similarity. The system includes tools for both supervised and unsupervised identity management. It features a face identity classifier for categorizing images into known identity classes and an unsupervised clustering tool to group similar facial embeddings together without predefined labels. The framewor

NormFace: L2 HyperSphere Embedding for Face Verification

This is a demo code of training and testing Probabilistic Face Embeddings using Tensorflow. Probabilistic Face Embeddging (PFE) is a method that converts conventional CNN-based face embeddings into probabilistic embeddings by calibrating each feature value with an uncertainty value. The…

SphereFace+ is released under the MIT License (refer to the LICENSE file for details).

2K real-time detection is so easy!

A PyTorch code of paper Dual Variational Generation for Low Shot Heterogeneous Face Recognition.

PyTorch module to use OpenFace's nn4.small2.v1.t7 model

By Yichun Shi and Anil K. Jain

Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c

Download from the Websit: http://home.ustc.edu.cn/~chendong/JointBayesian/

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

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

Attention-based Deep Multiple Instance Learning

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

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)

Code for our CVPR 2018 paper: "Synthesizing Images of Humans in Unseen Poses"

Contains implementation of Guided Attention Inference Network (GAIN) presented in Tell Me Where to Look(CVPR 2018). This repository aims to apply GAIN on fcn8 architecture used for segmentation.

CVPR2018 - pixel embedding & grouping for structured prediction, e.g., instance segmentation

Maxim Berman, Amal Rannen Triki, Matthew B. Blaschko

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

This is the implementation of Hierarchical Long-term Video Prediction without Supervision, to be published in ICML 2018.

Open source release of the evaluation benchmark suite described in "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"

CVPR 2025 Highlight X-Dyna: Expressive Dynamic Human Image Animation

Multi-Content GAN for Few-Shot Font Style Transfer at CVPR 2018