Accountable-Textual-Visual-Chat
Instruction rejection model
Develops accountability in image generation models by learning to reject human instructions
The official repository for Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation.
7 stars
1 watching
2 forks
Language: Shell
last commit: over 1 year ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 peteanderson80/bottom-up-attention | Trains a bottom-up attention model using Faster R-CNN and Visual Genome annotations for image captioning and VQA tasks | 1,433 |
 jnhwkim/nips-mrn-vqa | This project presents a neural network model designed to answer visual questions by combining question and image features in a residual learning framework. | 39 |
 rucaibox/comvint | Creating synthetic visual reasoning instructions to improve the performance of large language models on image-related tasks | 18 |
 aidc-ai/parrot | A method and toolkit for fine-tuning large language models to perform visual instruction tasks in multiple languages. | 30 |
| aidc-ai/ovis | An architecture designed to align visual and textual embeddings in multimodal learning | 517 |
 opendatalab/vigc | Autonomously generates high-quality image-text instruction fine-tuning datasets | 90 |
 vpgtrans/vpgtrans | Transfers visual prompt generators across large language models to reduce training costs and enable customization of multimodal LLMs | 269 |
 jiasenlu/adaptiveattention | Adaptive attention mechanism for image captioning using visual sentinels | 334 |
 eric-xw/arel | This codebase provides an implementation of a novel adversarial reward learning algorithm for generating human-like visual stories from image sequences. | 137 |
 ethanyanjiali/minchatgpt | This project demonstrates the effectiveness of reinforcement learning from human feedback (RLHF) in improving small language models like GPT-2. | 213 |
 gt-vision-lab/vqa_lstm_cnn | A Visual Question Answering model using a deeper LSTM and normalized CNN architecture. | 376 |
 visionlearninggroup/ask_attend_and_answer | Develops a deep learning model to answer questions about visual scenes based on spatial attention and question guidance | 25 |
 ucsc-vlaa/sight-beyond-text | This repository provides an official implementation of a research paper exploring the use of multi-modal training to enhance language models' truthfulness and ethics in various applications. | 19 |
 wisconsinaivision/vip-llava | A system designed to enable large multimodal models to understand arbitrary visual prompts | 294 |
 vision-cair/chatcaptioner | Enables automatic generation of descriptive text from images and videos based on user input. | 452 |