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GitHub - Cli98 DMNet: Official implementation for DMNet: Density map . . . DMNet has three key components: a density map generation module, an image cropping module and an object detector DMNet generates a density map and learns scale information based on density intensities to form cropping regions
DMNet: A Dense Multiscale Feature Extraction Network With Two-Stage . . . To address the above problems, we develop a fusion network called dense multiscale feature extraction (DMNet) for extracting features at different frequencies and balancing the weights of the source images to enhance the quality of the fused images
DMNet: A Dense Multiscale Feature Extraction Network With Two-Stage . . . To solve the aforementioned difficulties, we propose a dense multiscale fusion network DMNet Through a dual-stream collaborative feature decoupling, the proposed network optimizes both the encoder–decoder network and the diffusion model to extract multimodal information more comprehensively
Density Map Guided Object Detection in Aerial Images DMNet has three key components: a density map generation module, an image cropping module and an object detector DMNet generates a density map and learns scale information based on density intensities to form cropping regions
DMNet: Image dehazing via Dual-Domain Modulation • We propose the Dual-Domain Modulation Network (DMNet), where Dual-Domain Modulation Module (DM) consists of DCM and APGM DMNet outperforms state-of-the-art dehazing methods on multiple benchmark datasets, achieving more accurate dehazing results at faster inference speeds
DMNet: Difference Minimization Network for Semi-supervised . . . - Springer DMNet minimizes the difference between the soft masks predicted by the two decoders to utilize unlabeled data Unlike co-training which is often not end-to-end, the two decoders in DMNet can be updated at the same time in an end-to-end way