2.7k citations · 3.1k across the 29 of their papers we have counts for
10 papers · 1 filter
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…
VPA: Fully Test-Time Visual Prompt Adaptation
Jiachen Sun, Mark Ibrahim, Melissa Hall +4
Textual prompt tuning has demonstrated significant performance improvements in adapting natural language processing models to a variety of downstream tasks by treating hand-enginee…
The Casual Conversations v2 Dataset
Bilal Porgali, Vítor Albiero, Jordan Ryda +2
This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regar…
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Zhiheng Li, Ivan Evtimov, Albert Gordo +5
Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability. Previous works address thi…
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu +9
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, t…
Generating High Fidelity Data from Low-density Regions using Diffusion Models
Vikash Sehwag, Caner Hazirbas, Albert Gordo +2
Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthe…
The 2021 Image Similarity Dataset and Challenge
Matthijs Douze, Giorgos Tolias, Ed Pizzi +9
This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to…
Towards Measuring Fairness in AI: the Casual Conversations Dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky +3
This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and…
Adversarial Threats to DeepFake Detection: A Practical Perspective
Paarth Neekhara, Brian Dolhansky, Joanna Bitton +1
Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the…
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Emad Barsoum, Cha Zhang, Cristian Canton Ferrer +1
Crowd sourcing has become a widely adopted scheme to collect ground truth labels. However, it is a well-known problem that these labels can be very noisy. In this paper, we demonst…