Reza Salehi
CS PhD student at UW

About me

I am a fourth year PhD student in computer science at the The University of Washington, where I work with Hannaneh Hajishirzi and Ali Farhadi. My research spans pretraining, post-training, and benchmarking multi-modal large language models, specifically video language models. Prior to UW, I received my B.Sc. in computer engineering from Sharif University of Technology. I publish under my full name Mohammadreza, and go by Reza among my friends.

News

Publications

For the full list of publications please visit the publications page.

ActionAtlas: A VideoQA Benchmark for Domain-specialized Action Recognition

Mohammadreza Salehi, Jae Sung Park, Tanush Yadav, Aditya Kusupati, Ranjay Krishna, Yejin choi, Hanna Hajishirzi, Ali Farhadi

NeurIPS 2024 D&B

arXiv / Website / Code

TL;DR A benchmark for evaluating foundation models on complex actions which requires large enough frame sampling rate to understand fast movements, , tracking, and understanding sequence of fine movements.

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models

M. Deitke, C. Clark, S. Lee, R. Tripathi, Y. Yang, J.S. Park, M. Salehi et al.

arXiv / Demo / Blog

TL;DR We present Molmo, a new family of VLMs that are state-of-the-art in their class of openness. The best-in-class 72B model within the Molmo family not only outperforms others in the class of open weight and data models but also compares favorably against proprietary systems like GPT-4o, Claude 3.5, and Gemini 1.5 on both academic benchmarks and human evaluation..

CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement

Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri, Hadi Pouransari, Raviteja Vemulapalli, Oncel Tuzel, Ali Farhadi, Mohammad Rastegari, Sachin Mehta

TMLR'24

arXiv

TL;DR We showed how one can merge any task-specific expert model from open-source model zoos into foundation models (FMs) such as CLIP. This enhances the visual features of FM for dense prediction and localization tasks without collecting any supervised data.

SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks

Mohammadreza Salehi, Sachin Mehta, Aditya Kusupati, Ali Farhadi, Hannaneh Hajishirzi.

EMNLP'23 Findings

arXiv / Code

TL;DR We introduced a new sample adaptive inference method called SHARCS🦈. It routes samples to different sub-networks with varying widths within any transformer network based on the hardness of input sample.

Attentional Mixtures of Soft Prompt Tuning for Parameter-efficient Multi-task Knowledge Sharing

Akari Asai, Mohammadreza Salehi, Matthew E. Peters, Hannaneh Hajishirzi.

EMNLP'22

arXiv / Code

TL;DR We introduced a new parameter-efficient fine-tuning method based on prompt tuning. In our method, prompts for some source tasks are learnt and for each sample in a new target task an attentional mixture of source prompts is used as the target prompt.

MERLOT Reserve: Multimodal Neural Script Knowledge through Vision and Language and Sound

Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, Yejin Choi.

CVPR'22

arXiv / Code

TL;DR We introduce MERLOT Reserve, which learns from 20 million YouTube videos through all their modalities (audio, vision, and text). Learning from audio helps broadly -- even on single-image tasks like VCR. Our model learns state-of-the-art representations, that also transfer well to video-based tasks in a zero-shot setting.

Paraphrase Generation by Learning How to Edit from Samples

Amirhossein Kazemnejad, Mohammadreza Salehi, Mahdieh Soleymani Baghshah.

ACL'2020

ACL Anthology

TL;DR Paraphrase generation by retrieving similar paraphrase pairs from a pre-existing corpus and editing them using multi-head attention mechanism.