12 citations · 15 across the 9 of their papers we have counts for
10 papers
Unlocking Comics: The AI4VA Dataset for Visual Understanding
Peter Grönquist, Deblina Bhattacharjee, Bahar Aydemir +4
In the evolving landscape of deep learning, there is a pressing need for more comprehensive datasets capable of training models across multiple modalities. Concurrently, in digital…
Data Augmentation via Latent Diffusion for Saliency Prediction
Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang +2
Saliency prediction models are constrained by the limited diversity and quantity of labeled data. Standard data augmentation techniques such as rotating and cropping alter scene co…
OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation
Baran Ozaydin, Tong Zhang, Deblina Bhattacharjee +2
Unsupervised Semantic Segmentation (USS) involves segmenting images without relying on predefined labels, aiming to alleviate the burden of extensive human labeling. Existing metho…
CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
Ziyang Gong, Fuhao Li, Yupeng Deng +4
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 per…
Vision Transformer Adapters for Generalizable Multitask Learning
Deblina Bhattacharjee, Sabine Süsstrunk, Mathieu Salzmann
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…
Dense Multitask Learning to Reconfigure Comics
Deblina Bhattacharjee, Sabine Süsstrunk, Mathieu Salzmann
In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comics panels to, in turn, facilitate the transfer of comics from one publication channe…
MulT: An End-to-End Multitask Learning Transformer
Deblina Bhattacharjee, Tong Zhang, Sabine Süsstrunk +1
We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmen…
Estimating Image Depth in the Comics Domain
Deblina Bhattacharjee, Martin Everaert, Mathieu Salzmann +1
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 spa…
Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks
Xiaoyu Lin, Deblina Bhattacharjee, Majed El Helou +1
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…
Modeling Object Dissimilarity for Deep Saliency Prediction
Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang +3
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-…