Color quantization reduces the number of colors used in an image while preserving its content, which is essential in pixel art and knitting art creation. Traditional methods primarily focus on visual fidelity and treat it as a clustering problem in the RGB space. While effective in large (5-6 bits) color spaces, these approaches cannot guarantee semantics in small (1-2 bits) color spaces. On the other hand, deep color quantization methods use network viewers such as AlexNet and ResNet for supervision, effectively preserving semantics in small color spaces. However, in large color spaces, they lag behind traditional methods in terms of visual fidelity. In this work, we propose ColorCNN+, a novel approach that combines the strengths of both. It uses network viewer signals for supervision in small color spaces and learns to cluster the colors in large color spaces. Noteworthily, it is non-trivial for neural networks to do clustering, where existing deep clustering methods often need K-means to cluster the features. In this work, through a newly introduced cluster imitation loss, ColorCNN+ learns to directly output the cluster assignment without any additional steps. Furthermore, ColorCNN+ supports multiple color space sizes and network viewers, offering scalability and easy deployment. Experimental results demonstrate competitive performance of ColorCNN+ across various settings. Code is available at link.
Due to different installation angles, heights, and positions of the camera installation in real-world scenes, it is difficult for crowd counting models to work in unseen surveillance scenes. In this paper, we are interested in accurate crowd counting based on the data collected by any surveillance camera, that is to count the crowd from any scene given only one annotated image from that scene. To this end, we firstly pose crowd counting as a one-shot learning task. Through the metric-learning, we propose a simple yet effective method that firstly estimates crowd characteristics and then transfers them to guide the model to count the crowd. Specifically, to fully capture these crowd characteristics of the target scene, we devise the Multi-Prototype Learner to learn the prototypes of foreground and density from the limited support image using the Expectation-Maximization algorithm. To learn the adaptation capability for any unseen scene, estimated multi prototypes are proposed to guide the crowd counting of query images in a local-to-global way. CNN is utilized to activate the local features. And transformer is introduced to correlate global features. Extensive experiments on three surveillance datasets suggest that our method outperforms the SOTA methods in the few-shot crowd counting.
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