Dichloridobis(4-pyridylmethyl 1H-pyrrole-2-carboxyl­ate-κN)zinc

In the title mol­ecule, [ZnCl2(C11H10N2O2)2], the ZnII ion, situated on a twofold axis, is in a distorted tetra­hedral coordination environment formed by two chloride anions and two pyridine N atoms of the two organic ligands. In the pyrrole-2-carboxyl­ate unit, the pyrrole N—H group and the carbonyl group point approximately in the same direction. The dihedral angle between the two pyridine rings is 54.8 (3)°. The complex mol­ecules are connected into chains extending along [101] by N—H⋯Cl hydrogen bonds. The chains are further assembled into (-101) layers by C—H⋯O and C—H⋯Cl inter­actions

Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection

Camouflaged objects that blend into natural scenes pose significant challenges for deep-learning models to detect and synthesize. While camouflaged object detection is a crucial task in computer vision with diverse real-world applications, this research topic has been constrained by limited data availability. We propose a framework for synthesizing camouflage data to enhance the detection of camouflaged objects in natural scenes. Our approach employs a generative model to produce realistic camouflage images, which can be used to train existing object detection models. Specifically, we use a camouflage environment generator supervised by a camouflage distribution classifier to synthesize the camouflage images, which are then fed into our generator to expand the dataset. Our framework outperforms the current state-of-the-art method on three datasets (COD10k, CAMO, and CHAMELEON), demonstrating its effectiveness in improving camouflaged object detection. This approach can serve as a plug-and-play data generation and augmentation module for existing camouflaged object detection tasks and provides a novel way to introduce more diversity and distributions into current camouflage datasets