#计算机科学#An open source implementation of CLIP.
翻译 - CLIP 的开源实现。
#计算机科学#This repository offers a comprehensive collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge...
[ECCV2024] Video Foundation Models & Data for Multimodal Understanding
#计算机科学#Diffusion Classifier leverages pretrained diffusion models to perform zero-shot classification without additional training
#计算机科学#[NeurIPS 2023] This repository includes the official implementation of our paper "An Inverse Scaling Law for CLIP Training"
#自然语言处理#Cybertron: the home planet of the Transformers in Go
official code of “OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding”
#计算机科学#Reproducible scaling laws for contrastive language-image learning (https://arxiv.org/abs/2212.07143)
PyTorch code for MUST
Multi-Aspect Vision Language Pretraining - CVPR2024
#自然语言处理#Unofficial (Golang) Go bindings for the Hugging Face Inference API
Official PyTorch Implementation of MSDN (CVPR'22)
[TPAMI 2023] Generative Multi-Label Zero-Shot Learning
Evaluate custom and HuggingFace text-to-image/zero-shot-image-classification models like CLIP, SigLIP, DFN5B, and EVA-CLIP. Metrics include Zero-shot accuracy, Linear Probe, Image retrieval, and KNN ...
[ICASSP 2025] Open-source code for the paper "Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification"
#自然语言处理#Implementation of Z-BERT-A: a zero-shot pipeline for unknown intent detection.
#计算机科学#Low-latency ONNX and TensorRT based zero-shot classification and detection with contrastive language-image pre-training based prompts
Alternate Implementation for Zero Shot Text Classification: Instead of reframing NLI/XNLI, this reframes the text backbone of CLIP models to do ZSC. Hence, can be lightweight + supports more languages...
Codes for the experiments in our EMNLP 2021 paper "Open Aspect Target Sentiment Classification with Natural Language Prompts"
[CVPR 2024] The official implementation of paper "Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training"