Excited to be at #WACV2025 🎉& share our work: "FAIR-TAT, an adversarial training framework to address adversarial fairness issues in DNN classifiers.
📍 Poster: March 3rd, from 11:15 AM
📄 https://t.co/T3ai8Ey8rD
Happy to connect and discuss 😃.
@margret_keuper@jung_vision
Proud to announce that our paper "Can We Talk Models Into Seeing the World Differently?" was accepted at #ICLR2025 🇸🇬. This marks my last PhD paper, and we are honored that all 4 reviewers recommended acceptance, placing us in the top 6% of all submissions.
In our paper, we examine the formation of visual biases in large vision-language models (VLMs). Through the lens of the texture/shape bias, we show that LLMs significantly interact with the representation generated by dedicated vision encoders and utilize this finding to steer the VLM toward either extreme of a bias. All of our tested VLMs allowed to adjust the bias at inference time within some range by just utilizing natural language prompts! Or, in other words, we can simply talk a model into seeing the world differently. This is a key differentiator to traditional models, that will typically require finetuning or retraining to steer biases.
If this paper sounds familiar, then that is because you saw our abstract at the #CVPR2024 workshops :)
Presenting our work on "Neural Architecture Design and Robustness: A Dataset" at #ICLR2023 with @jovita_lukasik. Check out our poster at 11:30 #132 and visit our project page: https://t.co/UGg2iXpO33
I’m excited to present our work with @MoritzBoehle at #ICLR. Check out our poster today at 16:30 “Temperature Schedules for self-supervised contrastive methods for long-tail data” https://t.co/Vu7FPy7Aey