Reproduce “LAD-COD: Language-Aligned Dense Perception for Camouflaged Object Detection” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Shangye Song, Tianzhi Zhu, Syed Ariff Syed Hesham, Xin He, Yun Liu
LAD-COD segments camouflaged objects by linking an instruction-derived target embedding to detailed visual features. Its [SEG] token guides retrieval of frozen CLIP patch features, which a gated fusion module combines with multi-scale Swin features before SAM predicts a mask. On CAMO, it reports a weighted F-measure of 0.875 versus 0.843 for ESCNet, and leads all 12 comparisons across three datasets.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.