LAD-COD: Language-Aligned Dense Perception for Camouflaged Object Detection

2026-08-08

AI Summary

This work addresses the challenge of camouflaged object detection, where high similarity between foreground and background and weak boundary cues hinder accurate segmentation. To this end, the authors propose the LAD-COD framework, which introduces a novel Language-Aligned Dual Visual Fusion (LADVF) mechanism. This mechanism propagates language-instruction-guided semantic information to the image patch level and aligns it with low-level dense visual features, enabling synergistic optimization between semantic guidance and fine structural perception. Additionally, a trainable hierarchical visual branch is incorporated to extract camouflage-sensitive features, and a residual gating mechanism fuses multi-source information effectively. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance across all 12 dataset–metric combinations on three major benchmarks: CAMO, COD10K, and NC4K.

Abstract

Camouflaged object detection (COD) aims to segment objects that exhibit high visual similarity to their surroundings, which reduces foreground-background discriminability and weakens boundary evidence across appearance, texture, and structure. Such limitations motivate the use of instruction-conditioned semantics as top-down guidance for identifying which weak visual cues are relevant to the target. Recent segmentation systems built on large multimodal models (LMMs) demonstrate this possibility through instruction-conditioned target embeddings that guide mask decoding. However, in this language-to-mask paradigm, the generated target embedding conditions mainly the mask decoder, leaving the dense visual features that must preserve low-contrast boundaries and fine local structure without explicit guidance. We propose Language-Aligned Dense perception for COD (LAD-COD), a framework that aligns top-down semantic target guidance with bottom-up hierarchical visual features. Instead of fully adapting a large generic image encoder, LAD-COD learns a trainable hierarchical visual branch that captures camouflage-sensitive texture, boundary, and contextual information. To align these features with the target embedding, LAD-COD applies Language-Aligned Dual Visual Fusion (LADVF), which extends the embedding beyond sparse prompting to query patch-level language-aligned features and to gate their residual integration with the hierarchical features. This design allows semantic information to guide localization while preserving the fine structural details needed for camouflage segmentation. Experiments on CAMO, COD10K, and NC4K show that LAD-COD obtains the best reported value in all 12 dataset-metric comparisons.

Problem

• Camouflaged Object Detection
• Language Guidance
• Dense Visual Features
• Boundary Ambiguity
• Semantic Alignment

Innovation

• camouflaged object detection
• language-aligned perception
• dense visual features
• multimodal fusion
• instruction-conditioned segmentation