7 papers
LongVU-TTT: Causal Test-Time Training for Visual Resampling in Long Video Understanding
Mahmoud Ahmed, Sameh Abdulah, Olatunji Ruwase +3
Long-video MLLMs must model temporal change before a limited visual-token budget removes most frame evidence. We introduce LongVU-TTT, which inserts a convolutional Test-Time Train…
Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips
Mahmoud Ahmed, Sameh Abdulah, Olatunji Ruwase +4
Multimodal deep learning models enable joint learning across heterogeneous data sources, including text, images, and video, but their rapid scaling introduces significant memory an…
3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and Materials of 3D Shapes
Mahmoud Ahmed, Xiang Li, Arpit Prajapati +1
Understanding objects in 3D at the part level is essential for humans and robots to navigate and interact with the environment. Current datasets for part-level 3D object understand…
InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows
Kirolos Ataallah, Eslam Abdelrahman, Mahmoud Ahmed +4
Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a significant challenge for multi-modal models. Existing benchmark…
Kestrel: 3D Multimodal LLM for Part-Aware Grounded Description
Mahmoud Ahmed, Junjie Fei, Jian Ding +2
In this paper, we introduce Part-Aware Point Grounded Description (PaPGD), a challenging task aimed at advancing 3D multimodal learning for fine-grained, part-aware segmentation gr…
3DCoMPaT: An improved Large-scale 3D Vision Dataset for Compositional Recognition
Habib Slim, Xiang Li, Yuchen Li +8
In this work, we present 3DCoMPaT, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-inst…