AI 中文总结
针对基础模型跟踪器忽视高光谱材料线索的问题，提出HyperDAM，通过人工标注模态掩码、光谱门控和因果时空扩展增强干扰物分辨记忆，在HOTC 2026中获第二名（AUC 68.0093%，DP@20 87.7703%）。
AI 中文摘要
高光谱视频提供了材料线索，可以区分具有相似假彩色外观的目标，然而基础模型跟踪器主要从空间和外观证据更新记忆。我们提出了HyperDAM，一种基于DAM4SAM3的高光谱跟踪器，具有三个主要贡献。首先，HOTC2026-Modal为所有481个组织者提供的HOTC 2026视频添加了人工验证的逐帧模态掩码和掩码紧贴框。其次，一个帧零校准的HSI门控拒绝光谱不一致的更新到干扰物分辨记忆（DRM），而不改变当前预测。第三，一个因果时空扩展器从冻结的SAM特征添加仅向外的非模态校正。静态场景恢复和空掩码RTS平滑处理目标切换和完全遮挡。模型选择优先考虑跨域鲁棒性而非特定于排行榜的优化。最终系统在HOTC 2026中排名第二，在组织者的私有评估中实现了68.0093%的AUC和87.7703%的DP@20。
英文摘要
Hyperspectral video provides material cues that can disambiguate targets with similar false-color appearance, yet foundation-model trackers update memory primarily from spatial and appearance evidence. We present HyperDAM, a DAM4SAM3-based hyperspectral tracker with three principal contributions. First, HOTC2026-Modal adds human-verified frame-wise modal masks and mask-tight boxes to all 481 organizer-provided HOTC 2026 videos. Second, a frame-zero-calibrated HSI gate rejects spectrally inconsistent updates to the distractor-resolving memory (DRM) without altering the current prediction. Third, a causal spatiotemporal expander adds outward-only amodal corrections from frozen SAM features. Static-scene recovery and empty-mask RTS smoothing address target switches and full occlusion. Model selection prioritizes cross-domain robustness over leaderboard-specific optimization. The final system ranked second in HOTC 2026, achieving 68.0093% AUC and 87.7703% DP@20 in the organizer's private evaluation.
