12 research outputs found
Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment
No-reference (NR) image quality assessment (IQA) is an important tool in
enhancing the user experience in diverse visual applications. A major drawback
of state-of-the-art NR-IQA techniques is their reliance on a large number of
human annotations to train models for a target IQA application. To mitigate
this requirement, there is a need for unsupervised learning of generalizable
quality representations that capture diverse distortions. We enable the
learning of low-level quality features agnostic to distortion types by
introducing a novel quality-aware contrastive loss. Further, we leverage the
generalizability of vision-language models by fine-tuning one such model to
extract high-level image quality information through relevant text prompts. The
two sets of features are combined to effectively predict quality by training a
simple regressor with very few samples on a target dataset. Additionally, we
design zero-shot quality predictions from both pathways in a completely blind
setting. Our experiments on diverse datasets encompassing various distortions
show the generalizability of the features and their superior performance in the
data-efficient and zero-shot settings. Code will be made available at
https://github.com/suhas-srinath/GRepQ.Comment: Accepted to IEEE/CVF WACV 202
Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches
Image generation task has received increasing attention because of its wide
application in security and entertainment. Sketch-based face generation brings
more fun and better quality of image generation due to supervised interaction.
However, When a sketch poorly aligned with the true face is given as input,
existing supervised image-to-image translation methods often cannot generate
acceptable photo-realistic face images. To address this problem, in this paper
we propose Cali-Sketch, a poorly-drawn-sketch to photo-realistic-image
generation method. Cali-Sketch explicitly models stroke calibration and image
generation using two constituent networks: a Stroke Calibration Network (SCN),
which calibrates strokes of facial features and enriches facial details while
preserving the original intent features; and an Image Synthesis Network (ISN),
which translates the calibrated and enriched sketches to photo-realistic face
images. In this way, we manage to decouple a difficult cross-domain translation
problem into two easier steps. Extensive experiments verify that the face
photos generated by Cali-Sketch are both photo-realistic and faithful to the
input sketches, compared with state-of-the-art methodsComment: 10 pages, 12 figure