8 citations · 10 across the 11 of their papers we have counts for
11 papers
How Do Video Foundation Models Encode Intuitive Physics? Probing Across Pretraining Paradigms
Samuele Punzo, Niccolò Caselli, Ippokratis Pantelidis +3
Do video foundation models encode a usable sense of how the physical world works, and does intuitive physics emerge naturally from large-scale video pretraining? In this work, we s…
AmaraSpatial-10K: A Spatially and Semantically Aligned 3D Dataset for Spatial Computing and Embodied AI
Mohammad Sadegh Salehi, Alexander J. P. Perkins, Alex Perkins +4
Web-scale 3D asset collections are abundant but rarely ready to ingest without cleanup, suffering from arbitrary metric scaling, incorrect pivots, brittle geometry, and incomplete…
VideoNet: A Large-Scale Dataset for Domain-Specific Action Recognition
Tanush Yadav, Mohammadreza Salehi, Jae Sung Park +6
Videos are unique in their ability to capture actions which transcend multiple frames. Accordingly, for many years action recognition was the quintessential task for video understa…
MolmoPoint: Better Pointing for VLMs with Grounding Tokens
Christopher Clark, Yue Yang, Jae Sung Park +8
Grounding has become a fundamental capability of vision-language models (VLMs). Most existing VLMs point by generating coordinates as part of their text output, which requires lear…
Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
Christopher Clark, Jieyu Zhang, Zixian Ma +18
Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from…
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
Chenhao Zheng, Jieyu Zhang, Mohammadreza Salehi +5
Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time patches, leading to excessive tokens an…
REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations
Peter Sushko, Ayana Bharadwaj, Zhi Yang Lim +6
Existing image editing models struggle to meet real-world demands. Despite excelling in academic benchmarks, they have yet to be widely adopted for real user needs. Datasets that p…
ActionAtlas: A VideoQA Benchmark for Domain-specialized Action Recognition
Mohammadreza Salehi, Jae Sung Park, Tanush Yadav +5
Our world is full of varied actions and moves across specialized domains that we, as humans, strive to identify and understand. Within any single domain, actions can often appear q…
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models
Matt Deitke, Christopher Clark, Sangho Lee +47
Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good perfor…
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton +7
Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on ima…
SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks
Mohammadreza Salehi, Sachin Mehta, Aditya Kusupati +2
We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct…