arXiv (All)
open access
SIMPLE: Semantic Structure-Aligned Prompt Learning for Open-Vocabulary Audio-Visual Event Localization
Open-vocabulary audio-visual event localization aims to infer explicit segment-level categories for both seen and unseen audio-visual events. Existing solutions rely heavily on hand-crafted prompt templates, whose predictions are highly sensitive to subtle wording variations. Learnable prompts remov
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Positive Sample Propagation along the Audio-Visual Event Line#972833
- Hear, Localize, and Reason: Spatially Aware Scene Understanding for Audio-Visual LLMs#1356924
- Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding#1342549
- Towards Fine-grained Temporal Perception: Post-Training Large Audio-Language Models with Audio-Side Time Prompt#14079
- Revisiting Natural Language Prompts from Large Language Models for Open-Vocabulary Video Recognition#1341555
Record · ID 1326592
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive