120 citations · 152 across the 13 of their papers we have counts for
6 papers · 1 filter
Rendering-Aware Reinforcement Learning for Vector Graphics Generation
Juan A. Rodriguez, Haotian Zhang, Abhay Puri +12
Scalable Vector Graphics (SVG) offer a powerful format for representing visual designs as interpretable code. Recent advances in vision-language models (VLMs) have enabled high-qua…
Spectral State Space Model for Rotation-Invariant Visual Representation Learning
Sahar Dastani, Ali Bahri, Moslem Yazdanpanah +8
State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling global relationships with linear complexity.…
HumMUSS: Human Motion Understanding using State Space Models
Arnab Kumar Mondal, Stefano Alletto, Denis Tome
Understanding human motion from video is essential for a range of applications, including pose estimation, mesh recovery and action recognition. While state-of-the-art methods pred…
Mini-batch graphs for robust image classification
Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi
Current deep learning models for classification tasks in computer vision are trained using mini-batches. In the present article, we take advantage of the relationships between samp…
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
Arnab Kumar Mondal, Jose Dolz, Christian Desrosiers
We address the problem of segmenting 3D multi-modal medical images in scenarios where very few labeled examples are available for training. Leveraging the recent success of adversa…
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
Avisek Lahiri, Vineet Jain, Arnab Mondal +1
Contemporary deep learning based medical image segmentation algorithms require hours of annotation labor by domain experts. These data hungry deep models perform sub-optimally in t…