23 citations · 27 across the 26 of their papers we have counts for
14 papers · 1 filter
Not All Birds Look The Same: Identity-Preserving Generation For Birds
Aaron Sun, Oindrila Saha, Subhransu Maji
Since the advent of controllable image generation, increasingly rich modes of control have enabled greater customization and accessibility for everyday users. Zero-shot, identity-p…
SuperRivolution: Fine-Scale Rivers from Coarse Temporal Satellite Imagery
Rangel Daroya, Subhransu Maji
Satellite missions provide valuable optical data for monitoring rivers at diverse spatial and temporal scales. However, accessibility remains a challenge: high-resolution imagery i…
Merlin L48 Spectrogram Dataset
Aaron Sun, Subhransu Maji, Grant Van Horn
In the single-positive multi-label (SPML) setting, each image in a dataset is labeled with the presence of a single class, while the true presence of other classes remains unknown.…
SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation
Oindrila Saha, Vojtech Krs, Radomir Mech +3
We present SIGMA-GEN, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-GEN is the first to enable single-pass multi-subject identi…
You May Speak Freely: Improving the Fine-Grained Visual Recognition Capabilities of Multimodal Large Language Models with Answer Extraction
Logan Lawrence, Oindrila Saha, Megan Wei +3
Despite the renewed interest in zero-shot visual classification due to the rise of Multimodal Large Language Models (MLLMs), the problem of evaluating free-form responses of auto-r…
Reinforcement Learning for Quantum Network Control with Application-Driven Objectives
Guo Xian Yau, Alexandra Burushkina, Francisco Ferreira da Silva +3
Optimized control of quantum networks is essential for enabling distributed quantum applications with strict performance requirements. In near-term architectures with constrained h…
RiverScope: High-Resolution River Masking Dataset
Rangel Daroya, Taylor Rowley, Jonathan Flores +14
Surface water dynamics play a critical role in Earth's climate system, influencing ecosystems, agriculture, disaster resilience, and sustainable development. Yet monitoring rivers…
Consensus-Driven Active Model Selection
Justin Kay, Grant Van Horn, Subhransu Maji +2
The widespread availability of off-the-shelf machine learning models poses a challenge: which model, of the many available candidates, should be chosen for a given data analysis ta…
Active Measurement: Efficient Estimation at Scale
Max Hamilton, Jinlin Lai, Wenlong Zhao +2
AI has the potential to transform scientific discovery by analyzing vast datasets with little human effort. However, current workflows often do not provide the accuracy or statisti…
Moment Sampling in Video LLMs for Long-Form Video QA
Mustafa Chasmai, Gauri Jagatap, Gouthaman KV +3
Recent advancements in video large language models (Video LLMs) have significantly advanced the field of video question answering (VideoQA). While existing methods perform well on…
The iNaturalist Sounds Dataset
Mustafa Chasmai, Alexander Shepard, Subhransu Maji +1
We present the iNaturalist Sounds Dataset (iNatSounds), a collection of 230,000 audio files capturing sounds from over 5,500 species, contributed by more than 27,000 recordists wor…
Bioacoustic Geolocation: Species Sounds as Geographic Signals
Mustafa Chasmai, Wuao Liu, Subhransu Maji +1
Can we determine someone's geographic location solely from the sounds they hear? Are acoustic signals enough to localize within a country, state, or even city? In this work, we tac…
Feedforward Few-shot Species Range Estimation
Christian Lange, Max Hamilton, Elijah Cole +6
Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts. By mapping the spatial ranges of all species, we wou…
Generate, Transduct, Adapt: Iterative Transduction with VLMs
Oindrila Saha, Logan Lawrence, Grant Van Horn +1
Transductive zero-shot learning with vision-language models leverages image-image similarities within the dataset to achieve better classification accuracy compared to the inductiv…