Mikhail Kuznetsov — accepted AI-conference papers

2026

Visual Implicit Geometry Transformer for Autonomous Driving

Arsenii Shirokov, Mikhail Kuznetsov, Danila Stepochkin, Egor Evdokimov, Daniil Glazkov, Nikolay Patakin +2

IJCAI 2026

We introduce the Visual Implicit Geometry Transformer (ViGT), an autonomous driving geometric model that estimates continuous 3D occupancy fields from surround-view camera rigs. ViGT represents a step towards foundational geometric models for autonomous driving, prioritizing scalability, architectur

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2024

HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach

Maxim Nikolaev, Mikhail Kuznetsov, Dmitry Vetrov, Aibek Alanov

NeurIPS 2024poster

Our paper addresses the complex task of transferring a hairstyle from a reference image to an input photo for virtual hair try-on. This task is challenging due to the need to adapt to various photo poses, the sensitivity of hairstyles, and the lack of objective metrics. The current state of the art…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2018

A no-regret generalization of hierarchical softmax to extreme multi-label classification

Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski

NeurIPS 2018poster

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels. Large label spaces can be efficiently handled by organizing labels as a tree, like in the hierarchical softmax (HSM) approach c…

Cited by 119 Source ↗PDF ↗code ↗Scholar ↗