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English(EN) DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding

新的基准 DishSeg24k 和 FEAST 模型推动食物分割技术发展

研究人员推出了 DishSeg24k,这是一个新推出的大规模食物分割基准,包含 24,096 张图像和 278 个类别,旨在解决真实用餐场景的复杂性。为了应对密集物体重叠和长尾分布等挑战,他们还开发了食物专家自适应分割 Transformer (FEAST)。FEAST 模型将基于查询的解码视为马尔可夫决策过程,并结合了强化学习引导的专家混合模块,以提高专家专业化和路由能力。实验表明,FEAST 在 DishSeg24k 基准上取得了最先进的性能,优于以往的方法。 AI

影响 推动了食物分割能力的发展,可能影响智能餐饮和饮食评估等应用。

排序理由 该集群描述了一篇介绍基准和新颖模型的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的基准 DishSeg24k 和 FEAST 模型推动食物分割技术发展

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该集群描述了一篇介绍基准和新颖模型的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yilin Wang, Haochen Shi, Guanyu Chen, Weiqing Min, Jinkai Zheng, Chenggang Yan, Shuqiang Jiang ·

    DishSeg24k:具有随机专家解码的大规模食物分割基准

    arXiv:2607.23070v1 Announce Type: new Abstract: Food segmentation is essential for applications such as intelligent catering, dietary assessment, and recommendation. However, existing benchmarks fail to capture the complexity of real-world dining scenes. The challenges of dense i…