When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization
Camouflaged object detection (COD) segments objects that intentionally blend with the background, so predictions depend on subtle texture and boundary cues. COD is often needed under tight on-device memory and latency budgets, making low-bit inference highly desirable. However, COD is unusually hard to quantize aggressively. We study post-training W4A4 quantization of Transformer-based COD and find a task-specific cliff: heavy-tailed background tokens dominate a shared activation range, inflating the step size and pushing weak-but-structured boundary cues into the zero bin. This exposes a token-local bottleneck -- remove cross-token range domination and bound the zero-bin mass under 4-bit...
| Authors | Tianqi Li, Wenyu Fang, Xin He et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 April 2026 |
| Last updated at source | 15 July 2026 |
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- cod-tdq/codDataset · cited by
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- cod-tdq/codDataset
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