Zero-shot object navigation (ZSON) in unseen environments remains a challenging problem for household robots, requiring strong perceptual understanding and decision-making capabilities. While recent methods leverage metric maps and Large Language Models (LLMs), they often depend on depth sensors or prebuilt maps, limiting the spatial reasoning ability of Multimodal Large Language Models (MLLMs). Mapless ZSON approaches have emerged to address this, but they typically make short-sighted decisions, leading to local deadlocks due to a lack of historical context. We propose PanoNav, a fully RGB-only, mapless ZSON framework that integrates a Panoramic Scene Parsing module to unlock the spatial parsing potential of MLLMs from panoramic RGB inputs, and a Memory-guided Decision-Making mechanism enhanced by a Dynamic Bounded Memory Queue to incorporate exploration history and avoid local deadlocks. Experiments on the public navigation benchmark show that PanoNav significantly outperforms representative baselines in both SR and SPL metrics.

The retinal vasculature reflected by fundus images provides ophthalmologists with information to diagnose eye-related diseases. Therefore, the development of an accurate and automatic retinal vessel segmentation system is critical. Most deep learning-based methods directly use a color image or use a grayscale image simply transformed from the color image as input, and few models focus on the relationship between the three RGB channels. In this paper, we analyze the characteristics of the separated RGB channel images and propose an adaptive RGB-selection network with swin transformer (Swin-ASNet). The input of Swin-ASNet includes the original fundus image and three grayscale images separated from the color image. Our method can select the useful information adaptively through a designed adaptive selection aggregation module. In addition, we adopt the latest swin transformer as the backbone to extract strong features. To better fuse high and low-level features, we design a high-low interaction module, which applies a modified non-local operation under the graph convolution domain. The low-level features are injected into deep semantic information to enhance the detail representation. Experimental results show that our method can achieve state-of-the-art results in three public datasets, comparing with the existing methods.

Regular colonoscopy is an effective way to prevent colorectal cancer by detecting colorectal polyps. Automatic polyp segmentation significantly aids clinicians in precisely locating polyp areas for further diagnosis. However, polyp segmentation is a challenge problem, since polyps appear in a variety of shapes, sizes and textures, and they tend to have ambiguous boundaries. In this paper, we propose a U-shaped model named Feedback Enhancement Gate Network (FEGNet) for accurate polyp segmentation to overcome these difficulties. Specifically, for the high-level features, we design a novel Recurrent Gate Module (RGM) based on the feedback mechanism, which can refine attention maps without any additional parameters. RGM consists of Feature Aggregation Attention Gate (FAAG) and Multi-Scale Module (MSM). FAAG can aggregate context and feedback information, and MSM is applied for capturing multi-scale information, which is critical for the segmentation task. In addition, we propose a straightforward but effective edge extraction module to detect boundaries of polyps for low-level features, which is used to guide the training of early features. In our experiments, quantitative and qualitative evaluations show that the proposed FEGNet has achieved the best results in polyp segmentation compared to other state-of-the-art models on five colonoscopy datasets.