Pinned Repositories
a-PyTorch-Tutorial-to-Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
ACT
The official implementation of paper "Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?", ICLR 2023
APE
[ICCV 2023] Code for "Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement"
awesome-deep-text-detection-recognition
A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.
AWESOME-Dialogue
Paper List for Dialogue and Interactive Systems
awesome-embodied-vision
Reading list for research topics in embodied vision
awesome-InteractiveNLP-papers
Paper List for a new paradigm of NLP: Interactive NLP (https://arxiv.org/abs/2305.13246) :fire:
BCAN
ConVSE
PyTorch source code for "Regularizing Visual Semantic Embedding with Contrastive Learning for Image-Text Matching"
Point-RAE
Code for ACM MM 2023 paper - Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
liuyyy111's Repositories
liuyyy111/Point-RAE
Code for ACM MM 2023 paper - Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
liuyyy111/ConVSE
PyTorch source code for "Regularizing Visual Semantic Embedding with Contrastive Learning for Image-Text Matching"
liuyyy111/ACT
The official implementation of paper "Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?", ICLR 2023
liuyyy111/APE
[ICCV 2023] Code for "Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement"
liuyyy111/awesome-embodied-vision
Reading list for research topics in embodied vision
liuyyy111/awesome-InteractiveNLP-papers
Paper List for a new paradigm of NLP: Interactive NLP (https://arxiv.org/abs/2305.13246) :fire:
liuyyy111/awesome-point-cloud-analysis-2022
A list of papers and datasets about point cloud analysis (processing) since 2017. Update every day!
liuyyy111/awesome-point-cloud-place-recognition
A list of papers about point cloud based place recognition, also known as loop closure detection in SLAM (processing)
liuyyy111/Awesome-Sentence-Embedding
A curated list of research papers in Sentence Reprsentation Learning and a sts leaderboard of sentence embeddings.
liuyyy111/barlowtwins
PyTorch implementation of Barlow Twins.
liuyyy111/C1-Action-Recognition-TSN-TRN-TSM
EPIC-Kitchens-100 Action Recognition baselines: TSN, TRN, TSM
liuyyy111/CGMN
The code of the paper "Cross-Modal Graph Matching Network for Image-Text Retrieval" in ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) .
liuyyy111/consistency_models
Official repo for consistency models.
liuyyy111/DECL
Deep Evidential Learning with Noisy Correspondence for Cross-modal Retrieval ( ACM Multimedia 2022, Pytorch Code)
liuyyy111/DWSNets
Official implementation for Equivariant Architectures for Learning in Deep Weight Spaces [ICML 2023]
liuyyy111/EgoHMR
[ICRA2023] This is the official repository for EgoHMR
liuyyy111/FSMMDA_VideoRetrieval
liuyyy111/GaussianImage
🏠[ECCV 2024] GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
liuyyy111/Gaze-Attention
Integrating Human Gaze into Attention for Egocentric Activity Recognition (WACV 2021)
liuyyy111/MI-MM
Baseline model of EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge
liuyyy111/MRL
Learning Cross-Modal Retrieval with Noisy Labels (CVPR 2021, PyTorch Code)
liuyyy111/Partial_Distance_Correlation
This is the official GitHub for paper: On the Versatile Uses of Partial Distance Correlation in Deep Learning, in ECCV 2022
liuyyy111/Point-MAE
[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning
liuyyy111/PointCLIP
[CVPR 2022] PointCLIP: Point Cloud Understanding by CLIP
liuyyy111/ProS
liuyyy111/pykan
Kolmogorov Arnold Networks
liuyyy111/RelevanceMargin-ICMR22
liuyyy111/retrieval_demo
liuyyy111/SMPLer-X
liuyyy111/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.