The paper "Multi-view Aggregation Network for Dichotomous Image Segmentation" addresses the challenge of high-precision object segmentation from high-resolution (HR) natural images. The main issue is balancing the semantic dispersion of high-resolution targets in small receptive fields and the loss of high-precision details in large receptive fields. Inspired by the human visual system's ability to capture regions of interest from multiple views, the authors propose a parsimonious multi-view aggregation network (MVANet). MVANet unifies the feature fusion of distant and close-up views into a single stream with an encoder-decoder structure, enhancing long-range and detailed visual interactions. The proposed multi-view complementary localization and refinement modules improve object localization and detail refinement, respectively. Experiments on the DIS-5K dataset show that MVANet outperforms state-of-the-art methods in both accuracy and speed. The source code and datasets are publicly available.The paper "Multi-view Aggregation Network for Dichotomous Image Segmentation" addresses the challenge of high-precision object segmentation from high-resolution (HR) natural images. The main issue is balancing the semantic dispersion of high-resolution targets in small receptive fields and the loss of high-precision details in large receptive fields. Inspired by the human visual system's ability to capture regions of interest from multiple views, the authors propose a parsimonious multi-view aggregation network (MVANet). MVANet unifies the feature fusion of distant and close-up views into a single stream with an encoder-decoder structure, enhancing long-range and detailed visual interactions. The proposed multi-view complementary localization and refinement modules improve object localization and detail refinement, respectively. Experiments on the DIS-5K dataset show that MVANet outperforms state-of-the-art methods in both accuracy and speed. The source code and datasets are publicly available.