no code implementations • 3 Nov 2024 • Asra Aslam, Sachini Herath, Ziqi Huang, Estefania Talavera, Deblina Bhattacharjee, Himangi Mittal, Vanessa Staderini, Mengwei Ren, Azade Farshad
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2024, organized alongside the CVPR 2024 in Seattle, Washington, United States.
1 code implementation • 27 Oct 2024 • Peter Grönquist, Deblina Bhattacharjee, Bahar Aydemir, Baran Ozaydin, Tong Zhang, Mathieu Salzmann, Sabine Süsstrunk
This dataset is a crucial component of the AI4VA Workshop Challenges~\url{https://sites. google. com/view/ai4vaeccv2024}, where we specifically explore depth and saliency.
1 code implementation • 11 Sep 2024 • Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang, Mathieu Salzmann, Sabine Süsstrunk
We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
1 code implementation • 26 Mar 2024 • Ziyang Gong, Fuhao Li, Yupeng Deng, Deblina Bhattacharjee, Xianzheng Ma, Xiangwei Zhu, Zhenming Ji
SAVPT features a novel metric Severity that divides all adverse scene images into low-severity and high-severity images.
Ranked #1 on
Domain Adaptation
on Cityscapes-to-FoggyDriving
no code implementations • 11 Mar 2024 • Baran Ozaydin, Tong Zhang, Deblina Bhattacharjee, Sabine Süsstrunk, Mathieu Salzmann
Our OMH yields better unsupervised segmentation performance compared to existing USS methods.
no code implementations • ICCV 2023 • 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.
no code implementations • 16 Jul 2023 • 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 channel to another by assisting authors in the task of reconfiguring their narratives.
1 code implementation • CVPR 2022 • Deblina Bhattacharjee, Tong Zhang, Sabine Süsstrunk, Mathieu Salzmann
At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks.
1 code implementation • 7 Oct 2021 • Deblina Bhattacharjee, Martin Everaert, Mathieu Salzmann, Sabine Süsstrunk
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.
Ranked #1 on
Depth Estimation
on eBDtheque
1 code implementation • 1 Jun 2021 • Xiaoyu Lin, Deblina Bhattacharjee, Majed El Helou, Sabine Süsstrunk
Furthermore, as proof of concept, we show that when using our oracle fidelity map we even outperform the fully retrained methods, whether trained on noisy or restored images.
1 code implementation • 8 Apr 2021 • Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang, Seungryong Kim, Mathieu Salzmann, Sabine Süsstrunk
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.
1 code implementation • CVPR 2020 • Deblina Bhattacharjee, Seungryong Kim, Guillaume Vizier, Mathieu Salzmann
As evidenced by our experiments, this allows us to outperform the state-of-the-art unsupervised image-to-image translation methods.