← Search

Mausoom Sarkar

6 accepted papers

2025

AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models

CVPR 2025poster

Visual layouts are essential in graphic design fields such as advertising, posters, and web interfaces. The application of generative models for content-aware layout generation has recently gained traction. However, these models fail to understand the contextual aesthetic requirements of layout desi…

Cited by 0SourcePDFScholar
2023

Parameter Efficient Local Implicit Image Function Network for Face Segmentation

CVPR 2023poster

Face parsing is defined as the per-pixel labeling of images containing human faces. The labels are defined to identify key facial regions like eyes, lips, nose, hair, etc. In this work, we make use of the structural consistency of the human face to propose a lightweight face-parsing method using a L…

Cited by 10SourcePDFScholar
2023

UMFuse: Unified Multi View Fusion for Human Editing Applications

ICCV 2023poster

Numerous pose-guided human editing methods have been explored by the vision community due to their extensive practical applications. However, most of these methods still use an image-to-image formulation in which a single image is given as input to produce an edited image as output. This objective b…

Cited by 1PDFScholar
2023

VGFlow: Visibility Guided Flow Network for Human Reposing

CVPR 2023poster

The task of human reposing involves generating a realistic image of a model standing in an arbitrary conceivable pose. There are multiple difficulties in generating perceptually accurate images and existing methods suffers from limitations in preserving texture, maintaining pattern coherence, respec…

Cited by 7SourcePDFScholar
2020

Document Structure Extraction using Prior based High Resolution Hierarchical Semantic Segmentation

ECCV 2020poster

Structure extraction from document images has been a long-standing research topic due to its high impact on a wide range of practical applications. In this paper, we share our findings on employing a hierarchical semantic segmentation network for this task of structure extraction. We propose a prior…

Cited by 22SourcePDFScholar
2017

Introspection:Accelerating Neural Network Training By Learning Weight Evolution

ICLR 2017poster

Neural Networks are function approximators that have achieved state-of-the-art accuracy in numerous machine learning tasks. In spite of their great success in terms of accuracy, their large training time makes it difficult to use them for various tasks. In this paper, we explore the idea of learning…

Cited by 31SourceScholar