Papers by Denis Shepelev
SPARTA: Evaluating Reasoning Segmentation Robustness through Black-Box Adversarial Paraphrasing in Text Autoencoder Latent Space (2026.eacl-long)
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Viktoriia Zinkovich, Anton Antonov, Andrei Spiridonov, Denis Shepelev, Andrey Moskalenko, Daria Pugacheva, Elena Tutubalina, Andrey Kuznetsov, Vlad Shakhuro
| Challenge: | Existing work on semantically equivalent textual paraphrases has focused on perturbing image inputs. |
| Approach: | They propose a novel adversarial paraphrasing task that generates grammatically correct paraphrases that sighed the original query meaning while degrading segmentation performance. |
| Outcome: | The proposed task outperforms previous methods by up to 2x on ReasonSeg and LLMSeg-40k datasets. |
Bring the Apple, Not the Sofa: Impact of Irrelevant Context in Embodied AI Commands on VLA Models (2026.eacl-srw)
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Andrey Moskalenko, Daria Pugacheva, Denis Shepelev, Andrey Kuznetsov, Vlad Shakhuro, Elena Tutubalina
| Challenge: | Embodied AI is undergoing rapid development, with robots increasingly exhibiting practical utility in everyday environments. |
| Approach: | They evaluate the robustness of vision language action models under linguistic perturbations . they categorize irrelevant contexts into two groups according to their length and proximity to robot commands . |
| Outcome: | The proposed model can exhibit relative robustness to random context, with a performance drop within 10%, the authors show . human paraphrases of instructions lead to a drop of nearly 20%, the study shows . |