27 citations · 33 across the 16 of their papers we have counts for
17 papers · 1 filter
RealBirdID: Benchmarking Bird Species Identification in the Era of MLLMs
Logan Lawrence, Mustafa Chasmai, Rangel Daroya +8
Fine-grained bird species identification in the wild is frequently unanswerable from a single image: key cues may be non-visual (e.g. vocalization), or obscured due to occlusion, c…
3D Space as a Scratchpad for Editable Text-to-Image Generation
Oindrila Saha, Vojtech Krs, Radomir Mech +3
Recent progress in large language models (LLMs) has shown that reasoning improves when intermediate thoughts are externalized into explicit workspaces, such as chain-of-thought tra…
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…
VIDMP3: Video Editing by Representing Motion with Pose and Position Priors
Sandeep Mishra, Oindrila Saha, Alan C. Bovik
Motion-preserved video editing is crucial for creators, particularly in scenarios that demand flexibility in both the structure and semantics of swapped objects. Despite its potent…
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…
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…
YouDream: Generating Anatomically Controllable Consistent Text-to-3D Animals
Sandeep Mishra, Oindrila Saha, Alan C. Bovik
3D generation guided by text-to-image diffusion models enables the creation of visually compelling assets. However previous methods explore generation based on image or text. The b…
Decomposed evaluations of geographic disparities in text-to-image models
Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa +6
Recent work has identified substantial disparities in generated images of different geographic regions, including stereotypical depictions of everyday objects like houses and cars.…
C3DAG: Controlled 3D Animal Generation using 3D pose guidance
Sandeep Mishra, Oindrila Saha, Alan C. Bovik
Recent advancements in text-to-3D generation have demonstrated the ability to generate high quality 3D assets. However while generating animals these methods underperform, often po…
Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions
Oindrila Saha, Grant Van Horn, Subhransu Maji
The zero-shot performance of existing vision-language models (VLMs) such as CLIP is limited by the availability of large-scale, aligned image and text datasets in specific domains.…
PARTICLE: Part Discovery and Contrastive Learning for Fine-grained Recognition
Oindrila Saha, Subhransu Maji
We develop techniques for refining representations for fine-grained classification and segmentation tasks in a self-supervised manner. We find that fine-tuning methods based on ins…
Improving Few-Shot Part Segmentation using Coarse Supervision
Oindrila Saha, Zezhou Cheng, Subhransu Maji
A significant bottleneck in training deep networks for part segmentation is the cost of obtaining detailed annotations. We propose a framework to exploit coarse labels such as figu…
GANORCON: Are Generative Models Useful for Few-shot Segmentation?
Oindrila Saha, Zezhou Cheng, Subhransu Maji
Advances in generative modeling based on GANs has motivated the community to find their use beyond image generation and editing tasks. In particular, several recent works have show…
RecSal : Deep Recursive Supervision for Visual Saliency Prediction
Sandeep Mishra, Oindrila Saha
State-of-the-art saliency prediction methods develop upon model architectures or loss functions; while training to generate one target saliency map. However, publicly available sal…
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri +2
Standard Convolutional Neural Networks (CNNs) designed for computer vision tasks tend to have large intermediate activation maps. These require large working memory and are thus un…
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy
Oindrila Saha, Rachana Sathish, Debdoot Sheet
Diabetic retinopathy (DR) is the most common form of diabetic eye disease. Retinopathy can affect all diabetic patients and becomes particularly dangerous, increasing the risk of b…