A paper list of our recent survey on continual learning, and other useful resources in this field.

Unpaired Image-text Matching via Multimodal Aligned Conceptual Knowledge / MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching

Code for visualizing the loss landscape of neural nets

Pytorch implementation of Maximum Manifold Capacity Representations (MMCR) loss.

A lightweight library for portable low-level GPU computation using WebGPU.

This is a summary of research on noisy correspondence. There may be omissions. If anything is missing please get in touch with us. Our emails: [email protected] [email protected] qinyang.gm…

[CVPR2024] Official implementation of the paper: Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning

EgoCom: A Multi-person Multi-modal Egocentric Communications Dataset

[CVPR 2022] Joint hand motion and interaction hotspots prediction from egocentric videos

🔍 Explore Egocentric Vision: research, data, challenges, real-world apps. Stay updated & contribute to our dynamic repository! Work-in-progress; join us!

Code for ACM MM 2023 paper - Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning

Official PyTorch implementation for the following paper: Spiking PointNet: Spiking Neural Networks for Point Clouds.

PyTorch source code for "Regularizing Visual Semantic Embedding with Contrastive Learning for Image-Text Matching"

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

[CVPR 2023] Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders

PyTorch implementation of Barlow Twins.

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

Code accompanying the paper "Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs" (Chen et al., CVPR 2020, Oral).