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From the 1 of 79 linked papers with an AI index.

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20242026
most citedAstral: training physics-informed neural networks with error majorants

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cs.CV2026

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.CV2026

OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models

Ali Aliev, Kamil Garifullin, Nikolay Yudin +5

In a rapidly growing field of model training there is a constant practical interest in parameter-efficient fine-tuning and various techniques that use a small amount of training da…

cs.CV2026

Geological Field Restoration through the Lens of Image Inpainting

Vladislav Trifonov, Ivan Oseledets, Ekaterina Muravleva

We study an ill-posed problem of geological field reconstruction under limited observations. Engineers often have to deal with the problem of reconstructing the subsurface geologic…

cs.CV2026

Speech-to-LaTeX: New Models and Datasets for Converting Spoken Equations and Sentences

Dmitrii Korzh, Dmitrii Tarasov, Artyom Iudin +6

Conversion of spoken mathematical expressions is a challenging task that involves transcribing speech into a strictly structured symbolic representation while addressing the ambigu…

cs.CV2026

Spread them Apart: Towards Robust Watermarking of Generated Content

Mikhail Pautov, Danil Ivanov, Andrey V. Galichin +2

Generative models that can produce realistic images have improved significantly in recent years. The quality of the generated content has increased drastically, so sometimes it is…

cs.CV2026

T-MLA: A targeted multiscale log-exponential attack framework for neural image compression

Nikolay I. Kalmykov, Razan Dibo, Kaiyu Shen +4

Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of…