[EMNLP 2023 Demo] Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
A most Frontend Collection and survey of vision-language model papers, and models GitHub repository. Continuous updates.
(AAAI 2024) BLIVA: A Simple Multimodal LLM for Better Handling of Text-rich Visual Questions
[NeurIPS 2024] Official code base for NeuroClips
An offline AI-powered video analysis tool with object detection (YOLO), image captioning (BLIP), speech transcription (Whisper), audio event detection (PANNs), and AI-generated summaries (LLMs via Ollama). It ensures privacy and offline use with a user-friendly GUI.
Automate Fashion Image Captioning using BLIP-2. Automatic generating descriptions of clothes on shopping websites, which can help customers without fashion knowledge to better understand the features (attributes, style, functionality etc.) of the items and increase online sales by enticing more customers.
Implementation of Qformer from BLIP2 in Zeta Lego blocks.
A true multimodal LLaMA derivative -- on Discord!
[ACM MM 2024] Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
Official implementation and dataset for the NAACL 2024 paper "ComCLIP: Training-Free Compositional Image and Text Matching"
The Multimodal Model for Vietnamese Visual Question Answering (ViVQA)
CLIP Interrogator, fully in HuggingFace Transformers 🤗, with LongCLIP & CLIP's own words and / or *your* own words!
Modifying LAVIS' BLIP2 Q-former with models pretrained on Japanese datasets.
Caption images across your datasets with state of the art models from Hugging Face and Replicate!
This repository is for profiling, extracting, visualizing and reusing generative AI weights to hopefully build more accurate AI models and audit/scan weights at rest to identify knowledge domains for risk(s).
Finetuning Large Visual Models on Visual Question Answering
Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Cost
A Rizz bot that uses AI to help you make good opening lines.
In this we explore into visual Question Answering Using Gemini LLM and image was in URL or any other extension
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