Publications by Deblina Bhattacharjee

CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning

Unsupervised Domain Adaptation (UDA) aims to adapt models from labeled source domains to unlabeled target domains. When adapting to adverse scenes, existing UDA methods fail to perform well due to the lack of instructions, leading their models to overlook ...

Springer Science and Business Media Deutschland GmbH2024

Vision Transformer Adapters for Generalizable Multitask Learning

Mathieu Salzmann, Sabine Süsstrunk, Deblina Bhattacharjee

We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains. Integrated into an off-the-shelf vision transformer backbone, our adapters can simultaneously solve mu ...

Ieee Computer Soc2023

Dense Multitask Learning to Reconfigure Comics

Mathieu Salzmann, Sabine Süsstrunk, Deblina Bhattacharjee

In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comic panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narrativ ...

IEEE2023

Dense Image-based Predictions for Comics Analysis

Deblina Bhattacharjee

Dense image-based prediction methods have advanced tremendously in recent years. Their remarkable development has been possible due to the ample availability of real-world imagery. While these methods work well on photographs, their abilities do not genera ...

EPFL2023

Modeling Object Dissimilarity for Deep Saliency Prediction

Mathieu Salzmann, Sabine Süsstrunk, Bahar Aydemir, Deblina Bhattacharjee, Seungryong Kim, Tong Zhang

Saliency prediction has made great strides over the past two decades, with current techniques modeling low-level information, such as color, intensity and size contrasts, and high-level ones, such as attention and gaze direction for entire objects. Despite ...

2022

MulT: An End-to-End Multitask Learning Transformer

Mathieu Salzmann, Sabine Süsstrunk, Deblina Bhattacharjee, Tong Zhang

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edg ...

IEEE COMPUTER SOC2022

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Sabine Süsstrunk, Majed El Helou, Deblina Bhattacharjee, Xiaoyu Lin

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

IEEE Institute of Electrical and Electronics Engineers2021

Estimating Image Depth in the Comics Domain

Mathieu Salzmann, Sabine Süsstrunk, Deblina Bhattacharjee, Martin Nicolas Everaert

Estimating the depth of comics images is challenging as such images a) are monocular; b) lack ground-truth depth annotations; c) differ across different artistic styles; d) are sparse and noisy. We thus, use an off-the-shelf unsupervised image to image tra ...

2021

DUNIT: Detection-based Unsupervised Image-to-Image Translation

Mathieu Salzmann, Deblina Bhattacharjee, Seungryong Kim

Image-to-image translation has made great strides in recent years, with current techniques being able to handle unpaired training images and to account for the multimodality of the translation problem. Despite this, most methods treat the image as a whole, ...

IEEE2020