arXiv Computer Vision
Aug 27

MObyGaze: a film dataset of multimodal objectification densely annotated by experts

The paper introduces MObyGaze, a dataset of 20 films annotated by experts for multimodal objectification, covering 6072 segments across 43 hours of video. It defines objectification through a structured thesaurus of 5 sub‑constructs and 11 concepts spanning visual, speech, and audio modalities. The authors formulate learning tasks, explore label diversity strategies, and benchmark vision, text, and audio models to demonstrate the task’s feasibility.

By Julie Tores, Elisa Ancarani, Lucile Sassatelli, Hui-Yin Wu, Clement Bergman, Lea Andolfi, Victor Ecrement, Remy Sun, Frederic Precioso, Thierry Devars, Magali Guaresi, Virginie Julliard, Sarah Lecossais
arXiv AI
Jun 9

I Was Scrolling and Then I Saw a Pregnant Strawberry

arXiv:2606. 09589v1 Announce Type: cross Abstract: AI minidramas (also known as fruit dramas) are short, algorithmically distributed generative AI video series featuring anthropomorphized characters that have recently emerged as a widespread phenomenon on social media platforms.

By Piera Riccio
arXiv Computer Vision
Sep 24

Gender Bias in Vision-Language In-Context Learning

The paper investigates how in‑context learning (ICL) in large vision‑language models (LVLMs) can amplify gender bias. Using the VL‑BICLE framework, the authors show that gendered ICL demonstrations shift model bias toward the demonstrated gender, especially in tasks involving gendered language such as image captioning and pronoun prediction. They find that similarity‑based retrieval does not mitigate this bias and that replacing real images with synthetic ones from stable diffusion reduces bias without hurting caption quality.

By Tong Xiang, Noa Garcia, Yuta Nakashima