Introduces LCA dual-branch adapter and pairwise loss for lighting-robust SAM instance segmentation, validated on existing benchmarks plus a new Unity synthetic dataset.
In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
citing papers explorer
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Lighting-aware Unified Model for Instance Segmentation
Introduces LCA dual-branch adapter and pairwise loss for lighting-robust SAM instance segmentation, validated on existing benchmarks plus a new Unity synthetic dataset.
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LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.