8 citations · 16 across the 12 of their papers we have counts for
12 papers
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…
Unified Spatio-Temporal Token Scoring for Efficient Video VLMs
Jianrui Zhang, Yue Yang, Rohun Tripathi +5
Token pruning is essential for enhancing the computational efficiency of vision-language models (VLMs), particularly for video-based tasks where temporal redundancy is prevalent. P…
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…
SAGE: Training Smart Any-Horizon Agents for Long Video Reasoning with Reinforcement Learning
Jitesh Jain, Jialuo Li, Zixian Ma +7
As humans, we are natural any-horizon reasoners, i.e., we can decide whether to iteratively skim long videos or watch short ones in full when necessary for a given task. With this…
HinTel-AlignBench: A Framework and Benchmark for Hindi-Telugu with English-Aligned Samples
Rishikant Chigrupaatii, Ponnada Sai Tulasi Kanishka, Lalit Chandra Routhu +6
With nearly 1.5 billion people and more than 120 major languages, India represents one of the most diverse regions in the world. As multilingual Vision-Language Models (VLMs) gain…
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…
ModEFormer: Modality-Preserving Embedding for Audio-Video Synchronization using Transformers
Akash Gupta, Rohun Tripathi, Wondong Jang
Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted para…
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
Rohun Tripathi, Vasu Singla, Mahyar Najibi +3
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines. Unfortunately, for the region proposal stage of…
RSO: A Gradient Free Sampling Based Approach For Training Deep Neural Networks
Rohun Tripathi, Bharat Singh
We propose RSO (random search optimization), a gradient free Markov Chain Monte Carlo search based approach for training deep neural networks. To this end, RSO adds a perturbation…
Semantic Segmentation with Scarce Data
Isay Katsman, Rohun Tripathi, Andreas Veit +1
Semantic segmentation is a challenging vision problem that usually necessitates the collection of large amounts of finely annotated data, which is often quite expensive to obtain.…
Recurrent neural networks based Indic word-wise script identification using character-wise training
Rohun Tripathi, Aman Gill, Riccha Tripati
This paper presents a novel methodology of Indic handwritten script recognition using Recurrent Neural Networks and addresses the problem of script recognition in poor data scenari…