Open-vocabulary semantic segmentation (OVSS) extends traditional closed-set segmentation by enabling pixel-wise annotation for both seen and unseen categories using arbitrary textual descriptions. While existing methods leverage vision-language models (VLMs) like CLIP, their reliance on image-level pretraining often results in imprecise spatial alignment, leading to mismatched segmentations in ambiguous or cluttered scenes. However, most existing approaches lack strong object priors and region-level constraints, which can lead to object hallucination or missed detections, further degrading performance. To address these challenges, we propose LoGoSeg, an efficient single-stage framework that integrates three key innovations: (i) an object existence prior that dynamically weights relevant categories through global image-text similarity, effectively reducing hallucinations; (ii) a region-aware alignment module that establishes precise region-level visual-textual correspondences; and (iii) a dual-stream fusion mechanism that optimally combines local structural information with global semantic context. Unlike prior works, LoGoSeg eliminates the need for external mask proposals, additional backbones, or extra datasets, ensuring efficiency. Extensive experiments on six benchmarks (A-847, PC-459, A-150, PC-59, PAS-20, and PAS-20b) demonstrate its competitive performance and strong generalization in open-vocabulary settings.

Existing deep learning-based methods for synthetic aperture radar (SAR) target recognition typically rely solely on the amplitude images without considering the complex characteristic of SAR images, making it difficult to recognize SAR targets with high visual similarity. To solve this issue, a novel dual-stream manifold multiscale network fused with electromagnetic features, i.e., EFMM-Net is proposed for target recognition in complex-valued SAR images. In EFMM-Net, the attributed scattering center (ASC) model is first utilized to reconstruct the complex-valued SAR image, thereby highlighting the electromagnetic scattering features of the target. Subsequently, the reconstructed complex-valued SAR image is combined with the original one to construct the dual-stream input. Second, a scattering-guided manifold multiscale (SGMM) backbone is proposed for parallel extraction of data features and electromagnetic scattering features of the target from the dual-stream input. During feature extraction, the SGMM backbone can effectively leverage the phase information of complex-valued SAR images and inject target scattering information into data features through scattering-guided channel-wise feature alignment, thus enhancing the awareness of data features to critical scattering characteristics. Finally, to effective fuse the data features and electromagnetic scattering features, a location awareness feature fusion (LAFF) recognition module is proposed. By exploiting coordinate attention, LAFF utilizes the target location information captured from electromagnetic scattering features to direct the feature fusion process, thereby increasing the focus of fusion features on the target region. The extensive recognition results of three-class and six-class ship targets in the OpenSARShip 2.0 dataset demonstrate the effectiveness and superiority of the proposed method.

Creating realistic quadruped animations is challenging. Producing realistic animations using methods such as key-framing is time consuming and requires much artistic expertise. Alternatively, motion capture methods have their own challenges (getting the animal into a studio, attaching motion capture markers, and getting the animal to put on the desired performance) and the resulting animation will still most likely require cleaning up. It would be useful if an animator could provide an initial rough animation and in return be given a corresponding high quality realistic one. To this end, we present a deep-learning approach for the automatic enhancement of quadruped animations. Given an initial animation, possibly lacking the subtle details of true quadruped motion and/or containing small errors, our results show that it is possible for a neural network to learn how to add these subtleties and correct errors to produce an enhanced animation while preserving the semantics and context of the initial animation. Our work also has potential uses in other applications, for example, its ability to be used in real-time means it could form part of a quadruped embodiment system.

The low spatial resolution of acquired depth maps is a major drawback of most RGBD sensors. However, there are many scenarios in which fast acquisition of high-resolution and high-quality depth maps would be desirable. One approach to achieve higher quality depth maps is through super-resolution. However, edge preservation is challenging, and artifacts such as depth confusion and blurring are easily introduced near boundaries. In view of this, we propose a method for fast, high-quality hierarchical depth-map super-resolution (HDS). In our method, a high-resolution RGB image is degraded layer by layer to guide the bilateral filtering of the depth map. To improve the upsampled depth map quality, we construct a feature-based bilateral filter (FBF) for the interpolation, by using the extracted RGB shallow and multi-layer features. To accelerate the process, we perform filtering only near depth boundaries and through matrix operations. We also propose an extension of our HDS model to a Classification-based Hierarchical Depth-map Super-resolution (C-HDS) model, where a context-aware trilateral filter reduces the contributions of unreliable neighbors to the current missing depth location. Experimental results show that the proposed method is significantly faster than existing methods for generating high-resolution depth maps, while also significantly improving depth quality compared to the current state-of-the-art approaches, especially for large-scale 16x super-resolution.

A novel color image segmentation-based depth map upsampling method is proposed in this paper. In this method, the color image is segmented into a certain number of connected regions first. Based on the segmentation result, the target pixels will be interpolated by the seed pixels11The seed pixels are directly from the low resolution depth maps, i.e., the ones that have depth values. The targets are those without depth and to be interpolated. regionally. In the segmentation part, simple linear iterative clustering is introduced to generate superpixels in the first place. Then, the obtained superpixels will be judged whether they are correct-clustered or not, and the incorrect-clustered ones will be subdivided with an adaptive region-growing strategy. Third, the regions that have no seed will be constantly merged into their nearest neighbors, until seed pixel can be found in each independent region. Finally, adjacent regions that have quite small depth gaps will be united as one. The proposed color image segmentation strictly follows the guidance of the depth; therefore, the segmented regions adhere to the depth boundary well. In the interpolation part, the targets will be interpolated with their surrounding seeds weighted by a joint trilateral filter (JTF). The JTF is constructed by three terms: the color term, the distance term, and the region term, which are driven by the previous segmentation result. Experimental results indicate that our method greatly reduces depth bleeding and depth confusion artifacts, and leads to clear depth boundary in the up-sampled image. Comparisons with the state of art verify the advantages of the proposed method in both visual experience and quantitative evaluations.

Depth-image-based-rendering (DIBR) has received much attention in recent years as a promising technology for 3DTV systems. However, holes are inevitable in DIBR during the view synthesis procedure because the scene area, which has been occluded in the reference image, become visible in the synthesized virtual view. In this paper, we propose dictionary based hole filling with the assistance of depth. Because holes generally come from background area, we first segment background from foreground with the assistance of depth. Then, we construct a dictionary for hole filling from background. Finally, we employ the dictionary to fill holes in the synthesized virtual view. Experimental results demonstrate that the proposed method achieves good performance in hole filling in terms of visual quality and quantitative measures.