4 citations · 7 across the 16 of their papers we have counts for
6 papers · 1 filter
SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused Text-Conditioning
Chen Chen, Majid Abdolshah, Violetta Shevchenko +3
Existing diffusion-based super-resolution approaches often exhibit semantic ambiguities due to inaccuracies and incompleteness in their text conditioning, coupled with the inherent…
Scalable and Realistic Virtual Try-on Application for Foundation Makeup with Kubelka-Munk Theory
Hui Pang, Sunil Hadap, Violetta Shevchenko +2
Augmented reality is revolutionizing beauty industry with virtual try-on (VTO) applications, which empowers users to try a wide variety of products using their phones without the h…
BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling
Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham +1
Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Inspired by the remarkable progress of neural radiance fields (…
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering
Violetta Shevchenko, Ehsan Abbasnejad, Anthony Dick +2
The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vi…
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge
Violetta Shevchenko, Damien Teney, Anthony Dick +1
The limits of applicability of vision-and-language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense a…
Visual Question Answering with Prior Class Semantics
Violetta Shevchenko, Damien Teney, Anthony Dick +1
We present a novel mechanism to embed prior knowledge in a model for visual question answering. The open-set nature of the task is at odds with the ubiquitous approach of training…