52 citations · 74 across the 9 of their papers we have counts for
11 papers
CoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty Estimation
Sanghyuk Chun, William Yang, Amaya Dharmasiri +1
Uncertainty estimation has been a long-standing challenge in AI models; it amounts to "knowing what you don't know," and metacognition is notoriously difficult even for humans (cf.…
Bias at the End of the Score
Salma Abdel Magid, Grace Guo, Esin Tureci +4
Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-image alignment. RMs have becom…
Presenting Large Language Models as Companions Affects What Mental Capacities People Attribute to Them
Allison Chen, Sunnie S. Y. Kim, Angel Franyutti +4
How might messages about large language models (LLMs) found in public discourse influence the way people think about and interact with these models? To explore this question, we ra…
The Impact of Coreset Selection on Spurious Correlations and Group Robustness
Amaya Dharmasiri, William Yang, Polina Kirichenko +2
Coreset selection methods have shown promise in reducing the training data size while maintaining model performance for data-efficient machine learning. However, as many datasets s…
Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao +2
Large language models (LLMs) can produce erroneous responses that sound fluent and convincing, raising the risk that users will rely on these responses as if they were correct. Mit…
Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels
Amaya Dharmasiri, Muzammal Naseer, Salman Khan +1
Large-scale vision 2D vision language models, such as CLIP can be aligned with a 3D encoder to learn generalizable (open-vocabulary) 3D vision models. However, current methods requ…
DETER: Detecting Edited Regions for Deterring Generative Manipulations
Sai Wang, Ye Zhu, Ruoyu Wang +3
Generative AI capabilities have grown substantially in recent years, raising renewed concerns about potential malicious use of generated data, or "deep fakes". However, deep fake d…
3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulation
Amaya Dharmasiri, Dinithi Dissanayake, Mohamed Afham +3
3D generative models have been recently successful in generating realistic 3D objects in the form of point clouds. However, most models do not offer controllability to manipulate t…
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake +3
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular struct…
User configurable 3D object regeneration for spatial privacy
Arpit Nama, Amaya Dharmasiri, Kanchana Thilakarathna +2
Environmental understanding capability of (AR) and (MR) devices are continuously improving through advances in sensing, computer visio…
Viewport-Aware Dynamic 360° Video Segment Categorization
Amaya Dharmasiri, Chamara Kattadige, Vincent Zhang +1
Unlike conventional videos, 360° videos give freedom to users to turn their heads, watch and interact with the content owing to its immersive spherical environment. Although these…