1 citations · 1 across the 5 of their papers we have counts for
5 papers
ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers
Effrosyni Sokli, Isaac Roberts, Alexander Schulz +2
Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Despite their effectiveness, these…
Explaining Image Similarity with Automatically Extracted Concept Activation Vectors
Isaac Roberts, Petra Bevandic, Alexander Schulz +1
Image similarity underlies many computer vision applications, yet it is often unclear why two images receive a high or low similarity score. Existing explainability methods often r…
FederatedFactory: Generative One-Shot Learning for Extremely Non-IID Distributed Scenarios
Andrea Moleri, Christian Internò, Ali Raza +4
Federated Learning (FL) enables distributed optimization without compromising data sovereignty. Yet, where local label distributions are mutually exclusive, standard weight aggrega…
MGT: Extending Virtual Try-Off to Multi-Garment Scenarios
Riza Velioglu, Petra Bevandic, Robin Chan +1
Computer vision is transforming fashion industry through Virtual Try-On (VTON) and Virtual Try-Off (VTOFF). VTON generates images of a person in a specified garment using a target…
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
Riza Velioglu, Petra Bevandic, Robin Chan +1
This paper introduces Virtual Try-Off (VTOFF), a novel task generating standardized garment images from single photos of clothed individuals. Unlike Virtual Try-On (VTON), which di…