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MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts — arXiv:2609.24455

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.24455
License (as archived): CC0-1.0
Archived: 2026-10-09T07:08:48.493Z
SHA-256 of the archived text: b7e7fe698b181a3b8965b8a544aa6f652f26cf5a0f477efc822718a8ff031511
Stable URL of this page: https://metraipolis.com/documento/dc_f15f4e4232bf

How to cite

MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts — arXiv:2609.24455 — arXiv (en), https://arxiv.org/abs/2609.24455 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f15f4e4232bf (Metraipolis archive dc_f15f4e4232bf, sha256 b7e7fe698b181a3b8965b8a544aa6f652f26cf5a0f477efc822718a8ff031511)

BibTeX

@misc{dcf15f4e4232bf,
  title = {MECAIL: Communication-Aware Incremental Learning for Object Detection with 14.6 KB Spatiotemporal Experts — arXiv:2609.24455},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.24455},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f15f4e4232bf; sha256 b7e7fe698b181a3b8965b8a544aa6f652f26cf5a0f477efc822718a8ff031511},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.24455. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring updates to be transmitted from centralized servers. We propose using this setup to obtain dense, specialized module coverage that adapts a fixed base model to specific spatiotemporal contexts, such as parking lots, gas stations, ferries, or construction sites. However, in order to reliably transmit these modules to the edge device, using TCP, UDP, and BTP over V2X, Wi-Fi, and 2G-5G hardware, we establish a strict limit of 14.6 KB per module to fit within the first TCP window and to minimize UDP/BTP fragmentation. We further introduce Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model. We validate MECAIL on D-RICO and ODinW-13, where it largely matches the performance of parameter-heavy approaches while enabling practical, bandwidth-efficient large-scale deployment. This allows comprehensive coverage by experts for highly specific, focused, and temporary situations.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.LG).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.24455 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T06:58:09.647Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).