Search Results for author: Mikhail Sirotenko

Found 7 papers, 3 papers with code

PolyMaX: General Dense Prediction with Mask Transformer

1 code implementation9 Nov 2023 Xuan Yang, Liangzhe Yuan, Kimberly Wilber, Astuti Sharma, Xiuye Gu, Siyuan Qiao, Stephanie Debats, Huisheng Wang, Hartwig Adam, Mikhail Sirotenko, Liang-Chieh Chen

Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction.

Monocular Depth Estimation Semantic Segmentation +2

VideoGLUE: Video General Understanding Evaluation of Foundation Models

1 code implementation6 Jul 2023 Liangzhe Yuan, Nitesh Bharadwaj Gundavarapu, Long Zhao, Hao Zhou, Yin Cui, Lu Jiang, Xuan Yang, Menglin Jia, Tobias Weyand, Luke Friedman, Mikhail Sirotenko, Huisheng Wang, Florian Schroff, Hartwig Adam, Ming-Hsuan Yang, Ting Liu, Boqing Gong

We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a foundation model (FM) for a downstream task.

Action Recognition Temporal Localization +1

Efficient Image Representation Learning with Federated Sampled Softmax

no code implementations9 Mar 2022 Sagar M. Waghmare, Hang Qi, Huizhong Chen, Mikhail Sirotenko, Tomer Meron

This work creates a possibility for efficiently learning image representations on decentralized data with a large number of classes under the federated setting.

Descriptive Federated Learning +1

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

5 code implementations ECCV 2020 Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie

In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).

Attribute Fine-Grained Visual Categorization +5

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