  Recent camouflaged object detection (COD) attempts to segment objects
visually blended into their surroundings, which is extremely complex and
difficult in real-world scenarios. Apart from the high intrinsic similarity
between camouflaged objects and their background, objects are usually diverse
in scale, fuzzy in appearance, and even severely occluded. To this end, we
propose an effective unified collaborative pyramid network that mimics human
behavior when observing vague images and videos, \ie zooming in and out.
Specifically, our approach employs the zooming strategy to learn discriminative
mixed-scale semantics by the multi-head scale integration and rich granularity
perception units, which are designed to fully explore imperceptible clues
between candidate objects and background surroundings. The former's intrinsic
multi-head aggregation provides more diverse visual patterns. The latter's
routing mechanism can effectively propagate inter-frame differences in
spatiotemporal scenarios and be adaptively deactivated and output all-zero
results for static representations. They provide a solid foundation for
realizing a unified architecture for static and dynamic COD. Moreover,
considering the uncertainty and ambiguity derived from indistinguishable
textures, we construct a simple yet effective regularization, uncertainty
awareness loss, to encourage predictions with higher confidence in candidate
regions. Our highly task-friendly framework consistently outperforms existing
state-of-the-art methods in image and video COD benchmarks. Our code can be
found at {https://github.com/lartpang/ZoomNeXt}.
