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Official repository for the ICLR 2024 paper "Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition".
This is an offical implementation of "Sample Efficiency Matters: Training Multimodal Conversational Recommendation Systems in a Small Data Setting"
Source code for multimodal dialogue systems with semantic elements (MATE, He et al., 2020).
Code for the paper “Multimodal Dialogue Systems via Capturing Context-aware Dependencies and Ordinal Information of Semantic Elements”
[WWW 2024] Code and data for "On the Feasibility of Simple Transformer for Dynamic Graph Modeling"
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"
Pytorch implementation of NetVlad including training on Pittsburgh.
Official repository for R2Former: Unified Retrieval and Reranking Transformer for Place Recognition
Official PyTorch Implementation of Revisiting Self-Similarity: Structural Embedding for Image Retrieval, CVPR 2023
Official repository for the CVPR 2024 paper "CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition".
Optimal Transport Aggregation for Visual Place Recognition
GSV-Cities: a large-scale dataset for visual place recognition
PyTorch code and models for the DINOv2 self-supervised learning method.
手把手带你实战 Huggingface Transformers 课程视频同步更新在B站与YouTube
A curated list of awesome resources about multimodal recommender systems.
Code for "Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion", SIGIR 2024.
[TMM 2022] Code and data for "State Graph Reasoning for Multimodal Conversational Recommendation"
PyTorch implementation of "Brain Decodes Deep Nets"
Official code for CVPR 2022 paper "Rethinking Visual Geo-localization for Large-Scale Applications"
MixVPR: Feature Mixing for Visual Place Recognition (WACV 2023)
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🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…