Streaming Multiscale Deep Equilibrium Models

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2022-1-01

Author

Ertenli, Can Ufuk
Akbaş, Emre
Cinbiş, Ramazan Gökberk

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We present StreamDEQ, a method that infers frame-wise representations on videos with minimal per-frame computation. In contrast to conventional methods where compute time grows at least linearly with the network depth, we aim to update the representations in a continuous manner. For this purpose, we leverage the recently emerging implicit layer models, which infer the representation of an image by solving a fixed-point problem. Our main insight is to leverage the slowly changing nature of videos and use the previous frame representation as an initial condition on each frame. This scheme effectively recycles the recent inference computations and greatly reduces the needed processing time. Through extensive experimental analysis, we show that StreamDEQ is able to recover near-optimal representations in a few frames time, and maintain an up-to-date representation throughout the video duration. Our experiments on video semantic segmentation and video object detection show that StreamDEQ achieves on par accuracy with the baseline (standard MDEQ) while being more than 3× faster. Code and additional results are available at https://ufukertenli.github.io/streamdeq/.

Subject Keywords

Implicit layer models, Video analysis and understanding, Video object detection, Video semantic segmentation

URI

https://hdl.handle.net/11511/101838

DOI

https://doi.org/10.1007/978-3-031-20083-0_12

Conference Name

17th European Conference on Computer Vision, ECCV 2022

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

C. U. Ertenli, E. Akbaş, and R. G. Cinbiş, “Streaming Multiscale Deep Equilibrium Models,” Tel-Aviv-Yafo, İsrail, 2022, vol. 13671 LNCS, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/101838.