Home › Entities › academia › ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients

ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients

Research article (ACM Transactions on Sensor Networks, 2021) · cited 22× · AI/ML
academia openalex:W3203251378
Press Enter · cited answer in seconds

ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients

Summary

ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients is a scholarly article[1].

Key Facts

  • ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients. Retrieved May 24, 2026, from https://4ort.xyz/entity/approxnet-content-and-contention-aware-video-object-classification-system-for-embedded-clients
MLA “ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/approxnet-content-and-contention-aware-video-object-classification-system-for-embedded-clients.
BibTeX @misc{4ortxyz_approxnet-content-and-contention-aware-video-object-classification-system-for-embedded-clients_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients}}, year = {2026}, url = {https://4ort.xyz/entity/approxnet-content-and-contention-aware-video-object-classification-system-for-embedded-clients}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): ApproxNet: Content and Contention-Aware Video Object Classification System for Embedded Clients — https://4ort.xyz/entity/approxnet-content-and-contention-aware-video-object-classification-system-for-embedded-clients (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/approxnet-content-and-contention-aware-video-object-classification-system-for-embedded-clients · Last refreshed: