8 citations · 11 across the 6 of their papers we have counts for
7 papers
GeoNLI - A Natural Language Interpreter for Satellite Imagery
Ashutosh Gandhe, Anupam Rawat, Geet Sethi +5
Multi-modal multitasking models have shown strong performance on remote sensing datasets. However, because these models are trained on heterogeneous data and vary across tasks, des…
Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting
Panav Shah, Geet Sethi, Ashutosh Gandhe
Visual grounding aims to locate image regions that correspond to natural language descriptions and is a key component of interpretable vision systems. In remote sensing imagery, gr…
DiffuSAM: Diffusion Guided Zero-Shot Object Grounding for Remote Sensing Imagery
Geet Sethi, Panav Shah, Ashutosh Gandhe +1
Diffusion models have emerged as powerful tools for a wide range of vision tasks, including text-guided image generation and editing. In this work, we explore their potential for o…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
Geet Sethi, Pallab Bhattacharya, Dhruv Choudhary +2
Sequence-based deep learning recommendation models (DLRMs) are an emerging class of DLRMs showing great improvements over their prior sum-pooling based counterparts at capturing us…
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
Geet Sethi, Bilge Acun, Niket Agarwal +3
We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement technique for deep learning recommendation models (DLRMs). RecShard is designed based on two ke…
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search
Firas Abuzaid, Geet Sethi, Peter Bailis +1
Exact Maximum Inner Product Search (MIPS) is an important task that is widely pertinent to recommender systems and high-dimensional similarity search. The brute-force approach to s…