23 citations · 31 across the 10 of their papers we have counts for
12 papers
SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models
Arijit Ray, Jiafei Duan, Ellis Brown +9
Reasoning about motion and space is a fundamental cognitive capability that is required by multiple real-world applications. While many studies highlight that large multimodal lang…
Lasagna: Layered Score Distillation for Disentangled Object Relighting
Dina Bashkirova, Arijit Ray, Rupayan Mallick +4
Professional artists, photographers, and other visual content creators use object relighting to establish their photo's desired effect. Unfortunately, manual tools that allow relig…
Socratis: Are large multimodal models emotionally aware?
Katherine Deng, Arijit Ray, Reuben Tan +3
Existing emotion prediction benchmarks contain coarse emotion labels which do not consider the diversity of emotions that an image and text can elicit in humans due to various reas…
COLA: A Benchmark for Compositional Text-to-image Retrieval
Arijit Ray, Filip Radenovic, Abhimanyu Dubey +3
Compositional reasoning is a hallmark of human visual intelligence. Yet, despite the size of large vision-language models, they struggle to represent simple compositions by combini…
Language-Guided Audio-Visual Source Separation via Trimodal Consistency
Reuben Tan, Arijit Ray, Andrea Burns +5
We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as tra…
Improving Users' Mental Model with Attention-directed Counterfactual Edits
Kamran Alipour, Arijit Ray, Xiao Lin +4
In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems ans…