1 citations · 1 across the 7 of their papers we have counts for
7 papers
BREPS: Bounding-Box Robustness Evaluation of Promptable Segmentation
Andrey Moskalenko, Danil Kuznetsov, Irina Dudko +6
Promptable segmentation models such as SAM have established a powerful paradigm, enabling strong generalization to unseen objects and domains with minimal user input, including poi…
RoboBenchMart: Benchmarking Robots in Retail Environment
Konstantin Soshin, Alexander Krapukhin, Andrei Spiridonov +4
Most existing robotic manipulation benchmarks focus on tabletop or household scenarios. While these setups have driven impressive progress, it remains unclear whether generalist VL…
SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space
Viktoriia Zinkovich, Anton Antonov, Andrei Spiridonov +6
Multimodal large language models (MLLMs) have shown impressive capabilities in vision-language tasks such as reasoning segmentation, where models generate segmentation masks based…
Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning
Alexander Tyurin, Andrei Spiridonov, Varvara Rudenko
We study distributed reinforcement learning (RL) with policy gradient methods under asynchronous and parallel computations and communications. While non-distributed methods are wel…
Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
Nikita Kachaev, Andrei Spiridonov, Andrey Gorodetsky +8
Benchmarks are crucial for evaluating progress in robotics and embodied AI. However, a significant gap exists between benchmarks designed for high-level language instruction follow…
SUPER: Selfie Undistortion and Head Pose Editing with Identity Preservation
Polina Karpikova, Andrei Spiridonov, Anna Vorontsova +4
Self-portraits captured from a short distance might look unnatural or even unattractive due to heavy distortions making facial features malformed, and ill-placed head poses. In thi…
FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exits
Polina Karpikova, Radionova Ekaterina, Anastasia Yaschenko +4
Generative DNNs are a powerful tool for image synthesis, but they are limited by their computational load. On the other hand, given a trained model and a task, e.g. faces generatio…