  Camouflaged objects are seamlessly blended in with their surroundings, which
brings a challenging detection task in computer vision. Optimizing a
convolutional neural network (CNN) for camouflaged object detection (COD) tends
to activate local discriminative regions while ignoring complete object extent,
causing the partial activation issue which inevitably leads to missing or
redundant regions of objects. In this paper, we argue that partial activation
is caused by the intrinsic characteristics of CNN, where the convolution
operations produce local receptive fields and experience difficulty to capture
long-range feature dependency among image regions. In order to obtain feature
maps that could activate full object extent, keeping the segmental results from
being overwhelmed by noisy features, a novel framework termed Cross-Model
Detail Querying network (DQnet) is proposed. It reasons the relations between
long-range-aware representations and multi-scale local details to make the
enhanced representation fully highlight the object regions and eliminate noise
on non-object regions. Specifically, a vanilla ViT pretrained with
self-supervised learning (SSL) is employed to model long-range dependencies
among image regions. A ResNet is employed to enable learning fine-grained
spatial local details in multiple scales. Then, to effectively retrieve
object-related details, a Relation-Based Querying (RBQ) module is proposed to
explore window-based interactions between the global representations and the
multi-scale local details. Extensive experiments are conducted on the widely
used COD datasets and show that our DQnet outperforms the current
state-of-the-arts.
