22 citations · 30 across the 14 of their papers we have counts for
17 papers
Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models
Gautam Rajendrakumar Gare, Siyi Li, Hewei Wang +5
We address few-shot object detection with vision-language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using only ten annotated images f…
Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models
Gautam Rajendrakumar Gare, Jia Shi, Zhiqiu Lin +3
A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors. We show tha…
Ask Twice, Look Twice: Prompt Echoing Resolves the Question-First Paradox in Vision-Language Models
Rakshanda Hassan Abhinandan, John Galeotti, Deva Ramanan +1
Where should the question go in a vision-language model (VLM) prompt: before the image or after it? Intuition says before: knowing what is asked should tell the model where to look…
Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Gautam Gare, John Galeotti, Michael Mozer +2
In nature, events that affect some individuals or groups but not others constitute an implicit intervention and are known as natural experiments. For example, the COVID-19 pandemic…
Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
Jana Armouti, Laura Hutchins, Jacob Duplantis +12
Hospital readmission within 30 days of discharge is a leading driver of morbidity, mortality, and avoidable healthcare expenditure in congestive heart failure (CHF). Current clinic…
DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection
Gautam Rajendrakumar Gare, Neehar Peri, Matvei Popov +3
Multi-Modal LLMs (MLLMs) demonstrate strong visual grounding capabilities on popular object detection benchmarks like OdinW-13 and RefCOCO. However, state-of-the-art models still s…
Label Uncertainty for Ultrasound Segmentation
Malini Shivaram, Gautam Rajendrakumar Gare, Laura Hutchins +11
In medical imaging, inter-observer variability among radiologists often introduces label uncertainty, particularly in modalities where visual interpretation is subjective. Lung ult…
Activation Reward Models for Few-Shot Model Alignment
Tianning Chai, Chancharik Mitra, Brandon Huang +8
Aligning Large Language Models (LLMs) and Large Multimodal Models (LMMs) to human preferences is a central challenge in improving the quality of the models' generative outputs for…
LEARNER: Contrastive Pretraining for Learning Fine-Grained Patient Progression from Coarse Inter-Patient Labels
Jana Armouti, Nikhil Madaan, Rohan Panda +8
Predicting whether a treatment leads to meaningful improvement is a central challenge in personalized medicine, particularly when disease progression manifests as subtle visual cha…
LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies
Jia Shi, Gautam Gare, Jinjin Tian +6
We tackle the challenge of predicting models' Out-of-Distribution (OOD) performance using in-distribution (ID) measurements without requiring OOD data. Existing evaluations with "E…
Improving Model's Interpretability and Reliability using Biomarkers
Gautam Rajendrakumar Gare, Tom Fox, Beam Chansangavej +5
Accurate and interpretable diagnostic models are crucial in the safety-critical field of medicine. We investigate the interpretability of our proposed biomarker-based lung ultrasou…
Learning Generic Lung Ultrasound Biomarkers for Decoupling Feature Extraction from Downstream Tasks
Gautam Rajendrakumar Gare, Tom Fox, Pete Lowery +9
Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this par…
Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection
Gautam Rajendrakumar Gare, Andrew Schoenling, Vipin Philip +4
We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse…
The Role of Pleura and Adipose in Lung Ultrasound AI
Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung +8
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency lin…
Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound
Gautam Rajendrakumar Gare, Hai V. Tran, Bennett P deBoisblanc +2
With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have b…
Exploiting Class Similarity for Machine Learning with Confidence Labels and Projective Loss Functions
Gautam Rajendrakumar Gare, John Michael Galeotti
Class labels used for machine learning are relatable to each other, with certain class labels being more similar to each other than others (e.g. images of cats and dogs are more si…
W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data
Gautam Rajendrakumar Gare, Jiayuan Li, Rohan Joshi +5
We present W-Net, a novel Convolution Neural Network (CNN) framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency (RF…