CorrelationQA

Image model flaw detection

An investigation into the relationship between misleading images and hallucinations in large language models

The official repository of the paper "The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs"

GitHub

8 stars
1 watching
0 forks
last commit: 12 months ago

Related projects:

Repository Description Stars
assafbk/mocha_code A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models 13
tianyi-lab/hallusionbench An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy 259
junyangwang0410/haelm A framework for detecting hallucinations in large language models 17
yiyangzhou/lure Analyzing and mitigating object hallucination in large vision-language models to improve their accuracy and reliability. 136
yfzhang114/llava-align Debiasing techniques to minimize hallucinations in large visual language models 75
zhengpeng7/birefnet An open-source implementation of an image segmentation model that combines background removal and object detection capabilities. 1,484
x-plug/mplug-halowl Evaluates and mitigates hallucinations in multimodal large language models 82
bradyfu/woodpecker A method to correct hallucinations in multimodal large language models without requiring retraining 617
openmoss/halluqa An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection 111
prof-lu-cewu/visual-relationship-detection Detects relationships and predicts predicates in images using language priors 214
zcyang/imageqa-san This project provides code for training image question answering models using stacked attention networks and convolutional neural networks. 108
1zhou-wang/memvr An implementation of a method to mitigate hallucinations in large language models using visual re-tracing 28
damo-nlp-sg/vcd An approach to reduce object hallucinations in large vision-language models by contrasting output distributions derived from original and distorted visual inputs 222
zhunzhong07/random-erasing Provides data augmentation techniques to enhance image classification models 723
amazon-science/refchecker Automates fine-grained hallucination detection in large language model outputs 325