2 citations · 3 across the 7 of their papers we have counts for
11 papers
Spatially-Grounded Flow Matching: Structured Source Distributions for Image Generation
Arman Zarei, Mahdi M. Kalayeh
Current flow matching models learn to transport the source i.i.d. Gaussian noise into the target distribution of natural images, yet this source distribution carries no notion of s…
CultureScore: Evaluating Cultural Faithfulness in Video Generation Models
Anku Rani, Wei Dai, Shravan Nayak +3
As video generation models like Veo 3.1 and LTX-2 advance, their ability to accurately represent diverse global cultures remains a critical yet understudied frontier. Current metri…
Envisioning the Future, One Step at a Time
Stefan Andreas Baumann, Jannik Wiese, Tommaso Martorella +2
Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains, and efficiently explore many…
Seeing Sound: Assembling Sounds from Visuals for Audio-to-Image Generation
Darius Petermann, Mahdi M. Kalayeh
Training audio-to-image generative models requires an abundance of diverse audio-visual pairs that are semantically aligned. Such data is almost always curated from in-the-wild vid…
Looking Similar, Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning
Nikhil Singh, Chih-Wei Wu, Iroro Orife +1
Audiovisual representation learning typically relies on the correspondence between sight and sound. However, there are often multiple audio tracks that can correspond with a visual…
On Negative Sampling for Audio-Visual Contrastive Learning from Movies
Mahdi M. Kalayeh, Shervin Ardeshir, Lingyi Liu +2
The abundance and ease of utilizing sound, along with the fact that auditory clues reveal a plethora of information about what happens in a scene, make the audio-visual space an in…