Search Results for author:
Found 9 papers, 5 papers with code
CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning
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
OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation
Our OMH yields better unsupervised segmentation performance compared to existing USS methods.
Vision Transformer Adapters for Generalizable Multitask Learning
We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains.
Dense Multitask Learning to Reconfigure Comics
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.
MulT: An End-to-End Multitask Learning Transformer
At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks.
Estimating Image Depth in the Comics Domain
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
Fidelity Estimation Improves Noisy-Image Classification With Pretrained Networks
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.
Modeling Object Dissimilarity for Deep Saliency Prediction
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.
DUNIT: Detection-Based Unsupervised Image-to-Image Translation
As evidenced by our experiments, this allows us to outperform the state-of-the-art unsupervised image-to-image translation methods.
