Publication2024
Weakly Supervised Temporal Attention Framework for Video Anomaly Detection
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- 2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024
- Published
- 1 January 2024
- DOI
- 10.1109/icccnt61001.2024.10725808
- Scopus ID
- 85213067844
- Authors
- 3
- Open access
- No
- Citations
- 4
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
16 Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels
Weakly supervised temporal attention framework for surveillance anomaly detection supports institutional security and public safety monitoring systems.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Deep neural network architecture for video analysis represents machine learning and computer vision research methodology.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Surveillance system enhancement through AI-based anomaly detection supports public safety and security infrastructure.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Weakly Supervised Temporal Attention Framework for Video Anomaly Detection”. Kortex.