1.7k citations · 2.5k across the 48 of their papers we have counts for
51 papers
Deep Learning for Forest Plantation Mapping in Godavari Districts of Andhra Pradesh, India
Snehal More, Anuj Karpatne, Valerie A. Thomas +1
Small-area forest plantations play a vital role in the socioeconomic well-being of farmers in Southeast Asia. Most plantations are established on former agricultural land, often on…
Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning
Kazi Sajeed Mehrab, Hani Alomari, Najibul Haque Sarker +4
Multimodal large language models (MLLMs) ground whole objects well from free-form language queries, but they struggle when the query names a part rather than the object. We trace t…
LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data
Abhilash Neog, Sepideh Fatemi, Medha Sawhney +9
Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs. While machine learning methods have been rece…
SeamCam: Quantifying Seamless Camouflage via Multi-Cue Visual Detectability
Amin Karimi Monsefi, Abolfazl Meyarian, Mridul Khurana +6
Animals are described as effectively camouflaged when they blend seamlessly with their surrounding, yet no standardized quantitative measure of this seamlessness exists. We address…
TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Life
Mridul Khurana, Amin Karimi Monsefi, Justin Lee +9
Accurately generating images across the Tree of Life is difficult: there are over 10M distinct species on Earth, many of which differ only by subtle visual traits. Despite the rema…
VILLA: Versatile Information Retrieval From Scientific Literature Using Large LAnguage Models
Blessy Antony, Amartya Dutta, Sneha Aggarwal +7
The lack of high-quality ground truth datasets to train machine learning (ML) models impedes the potential of artificial intelligence (AI) for science research. Scientific informat…
A continental-scale dataset of ground beetles with high-resolution images and validated morphological trait measurements
S M Rayeed, Mridul Khurana, Alyson East +18
Despite the ecological significance of invertebrates, global trait databases remain heavily biased toward vertebrates and plants, limiting comprehensive ecological analyses of high…
PRISMA: Improving the Accuracy-Latency Frontier of Diffusion-based PDE Solvers Using Physics-Informed Spectral Attention
Medha Sawhney, Abhilash Neog, Mridul Khurana +1
Diffusion-based solvers for partial differential equations (PDEs) are often bottle-necked by slow gradient-based test-time optimization routines that use PDE residuals for loss gui…
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
Amin Karimi Monsefi, Mridul Khurana, Rajiv Ramnath +3
We propose TaxaDiffusion, a taxonomy-informed training framework for diffusion models to generate fine-grained animal images with high morphological and identity accuracy. Unlike s…
Open World Scene Graph Generation using Vision Language Models
Amartya Dutta, Kazi Sajeed Mehrab, Medha Sawhney +8
Scene-Graph Generation (SGG) seeks to recognize objects in an image and distill their salient pairwise relationships. Most methods depend on dataset-specific supervision to learn t…
Physics-guided hierarchical neural networks for Maxwell's equations in plasmonic metamaterials
Sean Lynch, Jacob LaMountain, Bo Fan +5
While machine learning (ML) has found multiple applications in photonics, traditional "black box" ML models typically require prohibitively large training data sets. Generation of…
Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling
Abhilash Neog, Arka Daw, Sepideh Fatemi Khorasgani +10
Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps…
Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai +10
We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish vis…
Static Segmentation by Tracking: A Label-Efficient Approach for Fine-Grained Specimen Image Segmentation
Zhenyang Feng, Zihe Wang, Jianyang Gu +22
We study image segmentation in the biological domain, particularly trait segmentation from specimen images (e.g., butterfly wing stripes, beetle elytra). This fine-grained task is…
A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations
Naveen Gupta, Medha Sawhney, Arka Daw +2
In subsurface imaging, learning the mapping from velocity maps to seismic waveforms (forward problem) and waveforms to velocity (inverse problem) is important for several applicati…
What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits
Harish Babu Manogaran, M. Maruf, Arka Daw +12
A grand challenge in biology is to discover evolutionary traits - features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as…
VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images
M. Maruf, Arka Daw, Kazi Sajeed Mehrab +19
Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of orga…
Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution
Mridul Khurana, Arka Daw, M. Maruf +12
A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species acr…
Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
Kazi Sajeed Mehrab, M. Maruf, Arka Daw +16
We introduce Fish-Visual Trait Analysis (Fish-Vista), the first organismal image dataset designed for the analysis of visual traits of aquatic species directly from images using pr…
Knowledge-guided Machine Learning: Current Trends and Future Prospects
Anuj Karpatne, Xiaowei Jia, Vipin Kumar
This paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based…
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
Dipanjyoti Paul, Arpita Chowdhury, Xinqi Xiong +11
We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully connected layer to incorporate cla…
MEMTRACK: A Deep Learning-Based Approach to Microrobot Tracking in Dense and Low-Contrast Environments
Medha Sawhney, Bhas Karmarkar, Eric J. Leaman +3
Tracking microrobots is challenging, considering their minute size and high speed. As the field progresses towards developing microrobots for biomedical applications and conducting…
Neuro-Visualizer: An Auto-encoder-based Loss Landscape Visualization Method
Mohannad Elhamod, Anuj Karpatne
In recent years, there has been a growing interest in visualizing the loss landscape of neural networks. Linear landscape visualization methods, such as principal component analysi…
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation
M. Maruf, Arka Daw, Amartya Dutta +2
In recent years, several Weakly Supervised Semantic Segmentation (WS3) methods have been proposed that use class activation maps (CAMs) generated by a classifier to produce pseudo-…