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20232026
most cited Improving Knowledge Distillation using Orthogonal Projections

1 citations · 1 across the 14 of their papers we have counts for

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cs.CV2026

EquiSteer: Cross-Attention Steering Towards a Fairer Text-Guided Image Generation

Tatiana Gaintseva, Akshit Achara, Gregory Slabaugh +2

Text-to-image diffusion models power everyday creative tasks, but they still reproduce the demographic biases in their training data. On common prompts such as ``a photo of a nurse…

cs.CV2026

Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answering

Yura Choi, Roy Miles, Rolandos Alexandros Potamias +3

Understanding and answering questions based on a user's pointing gesture is essential for next-generation egocentric AI assistants. However, current Multimodal Large Language Model…

cs.CV2026

DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias +4

Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit d…

cs.CV2025

SATGround: A Spatially-Aware Approach for Visual Grounding in Remote Sensing

Aysim Toker, Andreea-Maria Oncescu, Roy Miles +2

Vision-language models (VLMs) are emerging as powerful generalist tools for remote sensing, capable of integrating information across diverse tasks and enabling flexible, instructi…

cs.CV2025

RetouchLLM: Training-free Code-based Image Retouching with Vision Language Models

Moon Ye-Bin, Roy Miles, Tae-Hyun Oh +2

Image retouching not only enhances visual quality but also serves as a means of expressing personal preferences and emotions. However, existing learning-based approaches require la…

cs.CV2025

Region-based Cluster Discrimination for Visual Representation Learning

Yin Xie, Kaicheng Yang, Xiang An +9

Learning visual representations is foundational for a broad spectrum of downstream tasks. Although recent vision-language contrastive models, such as CLIP and SigLIP, have achieved…