An open source implementation of CLIP.
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Updated
Oct 21, 2024 - Python
An open source implementation of CLIP.
SketchZoo is a project that utilizes Siamese neural networks for efficient animal image retrieval. With the power of contrastive and triplet loss functions, SketchZoo accurately matches hand-drawn animal sketches to corresponding images. It also provides a user-friendly GUI interface for convenient image retrieval based on sketches
Code for the paper: Improving Speaker Representations Using Contrastive Losses on Multi-scale Features
A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
[ACM MM 2024] Code for the paper "Robust Variational Contrastive Learning for Partially View-unaligned Clustering"
The implement for paper : "Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval"
Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
A vision-language implementation for automated mammography reporting using CLIP (Contrastive Language-Image Pre-Training) neural network.
incremental learning experiments
This code is a custom implementation of the Supervised Contrastive Learning paper (https://arxiv.org/abs/2004.11362).
Official implementation for "Image Quality Assessment using Contrastive Learning"
Contrastive Unlearning
CLIP Like model fine tuned for the SemEval-2023 Visual-WSD task
Writer independent offline signature verification using convolutional siamese networks
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
[ECCV 2022 Oral] Official Pytorch implementation of CCPL and SCTNet
Medical Image Similarity Search Using a Siamese Network With a Contrastive Loss
Implementation of Cyclist Pressure Research Paper
PyTorch implementation of the InfoNCE loss for self-supervised learning.
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