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cs.CV2025
Listener-Rewarded Thinking in VLMs for Image Preferences
Alexander Gambashidze, Li Pengyi, Matvey Skripkin +5
Training robust and generalizable reward models for human visual preferences is essential for aligning text-to-image and text-to-video generative models with human intent. However,…
cs.CV2025
Test-Time Reasoning Through Visual Human Preferences with VLMs and Soft Rewards
Alexander Gambashidze, Konstantin Sobolev, Andrey Kuznetsov +1
Can Visual Language Models (VLMs) effectively capture human visual preferences? This work addresses this question by training VLMs to think about preferences at test time, employin…
cs.CV2025
MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding
Pengyi Li, Irina Abdullaeva, Alexander Gambashidze +2
Modern Video Large Language Models (VLLMs) often rely on uniform frame sampling for video understanding, but this approach frequently fails to capture critical information due to f…