22 citations · 30 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…