52 citations · 74 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…