Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion

Biological learning proceeds from easy to difficult tasks, gradually reinforcing perception and robustness. Inspired by this principle, we address Context‑Entangled Content Segmentation (CECS)—a challenging setting where objects share intrinsic visual patterns with their surroundings, as in camouflaged object detection. Conventional segmentation networks predominantly rely on architectural enhancements but often ignore the learning dynamics that govern robustness under entangled data distributions. We introduce CurriSeg, a dual‑phase learning framework that unifies curriculum and anti‑curriculum principles to improve representation reliability. In the Curriculum Selection phase, CurriSeg dynamically selects training data based on the temporal statistics of sample losses, distinguishing hard‑but‑informative samples from noisy or ambiguous ones, thus enabling stable capability enhancement. In the Anti‑Curriculum Promotion phase, we design Spectral‑Blindness Fine‑Tuning, which suppresses high‑frequency components to enforce dependence on low‑frequency structural and contextual cues. Combined with Sharpness‑Aware Minimization, this promotes convergence to flatter minima and strengthens generalization. Extensive experiments demonstrate that CurriSeg achieves consistent improvements across diverse CECS benchmarks without adding parameters or increasing total training time, offering a principled view of how progression and challenge interplay to foster robust and context‑aware segmentation. Code will be released.

Some objects are extremely hard to see, like a camouflaged insect on bark or a lesion hidden in a medical scan, because they blend into their surroundings. Teaching a computer to trace their outlines is one of the toughest problems in computer vision. Instead of building a more complex model, we focused on how the model learns. People usually learn easy lessons first, but we found that copying this directly backfires: "easy" examples often have obvious giveaways, and a model that leans on such shortcuts fails on truly hidden objects. Our method, CurriSeg, first learns in a careful order, separating genuinely useful hard examples from confusing or mislabeled ones, then deliberately blurs away fine texture to force the model to rely on deeper structural clues. The result is a more accurate and reliable detection of concealed objects, with no extra computing cost and often faster training.