MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts
Image compression for machines calls for a unified codec that serves multiple downstream vision tasks. Existing approaches either adopt task-specific end-to-end designs, raising parameter and deployment overhead, or rely on transfer-based adaptations that remain externally attached and heuristic task design. A key limitation shared by both lines of work is their largely static computation pattern, which applies similar transformations across tokens despite the fact that different image regions exhibit markedly different semantic importance and complexity for machine perception. We propose MoECodec, a token-aware image compression framework that supports multiple downstream tasks within a ...
| Authors | Jiancheng Zhao, Xiang Ji, Yifan Zhan et al. |
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| Primary category | eess.IV |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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