Image Bitstream Fine-grained Understanding for Privacy-Friendly AIoT
Image Bitstream Fine-grained Understanding (IBFU) aims to directly perform fine-grained classification and semantic description generation from encoded image byte sequences.
ProofPaper ↗
Key points
- In contrast to conventional pixel-domain visual understanding, IBFU conducts semantic analysis without fully decoding images into the pixel domain.
- Since pixel-level visual content is not explicitly reconstructed during inference, this paradigm reduces visual exposure within the processing pipeline and suits privacy-friendly Artificial Intelligence of Things (AIoT) applications.
- In this paper, we propose Bitstream Fine-grained Generator (BFG), a novel foundation model tailored for IBFU.
- BSeE directly models semantic representations from encoded image bitstreams without explicit pixel reconstruction, while FSeG transforms the extracted bitstream semantics into detailed natural-language descriptions through autoregressive generation.
Sources (1)
- [1]Image Bitstream Fine-grained Understanding for Privacy-Friendly AIoTarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 02:18 PM
Image Bitstream Fine-grained Understanding (IBFU) aims to directly perform fine-grained classification and semantic description generation from encoded image byte sequences.
In contrast to conventional pixel-domain visual understanding, IBFU conducts semantic analysis without fully decoding images into the pixel domain.
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