Weakly-supervised video object segmentation is an emerging video task to track and segment the target given a simple bounding box label, which requires the method to fully catch and utilize the target information. Most existing approaches only rely on the guidance of a single frame and ignore the interaction between different frames when gathering information, making them hard to achieve reliable target representation. In this paper, we propose to capture the temporal dependencies and gather information from multiple frames through bilateral temporal re-aggregation. We explore three schemes to build the aggregation: 1) a two-stage re-aggregation mechanism is applied to provide target prior to the current frame, which obtains more valid feature matching and information aggregation; 2) a query-memory bilateral aggregation module is proposed to aggregate features from an unlimited amount of past frames and enable the mutual perception between different frames to validate the gathered information; 3) we guide the learning of aggregation modules through a novel cross-task representation distillation, transferring the knowledge from a semi-supervised model to our weakly-supervised model without increasing the inference latency. These schemes collaboratively build an efficient and competent aggregation process, thus we can fully exploit the video context to make the inference. Experimental results on four benchmarks show that our method achieves superior performance than previous methods and still maintains the efficiency ( $e.g$ ., overall scores of 70.4% and 72.5% on the YouTube-VOS and DAVIS 2017 validation sets, respectively).

Semi-supervised video object segmentation is the task of segmenting the target in sequential frames given the ground truth mask in the first frame. The modern approaches usually utilize such a mask as pixel-level supervision and typically exploit pixel-to-pixel matching between the reference frame and current frame. However, the matching at pixel level, which overlooks the high-level information beyond local areas, often suffers from confusion caused by similar local appearances. In this paper, we present Prototypical Matching Networks (PMNet) - a novel architecture that integrates prototypes into matching-based video objection segmentation frameworks as high-level supervision. Specifically, PMNet first divides the foreground and background areas into several parts according to the similarity to the global prototypes. The part-level prototypes and instance-level prototypes are generated by encapsulating the semantic information of identical parts and identical instances, respectively. To model the correlation between prototypes, the prototype representations are propagated to each other by reasoning on a graph structure. Then, PMNet stores both the pixel-level features and prototypes in the memory bank as the target cues. Three affinities, i.e., pixel-to-pixel affinity, prototype-to-pixel affinity, and prototype-to-prototype affinity, are derived to measure the similarity between the query frame and the features in the memory bank. The features aggregated from the memory bank using these affinities provide powerful discrimination from both the pixel-level and prototype-level perspectives. Extensive experiments conducted on four benchmarks demonstrate superior results than the state-of-the-art video object segmentation techniques.

Exploring and monitoring underwater environments pose unique challenges due to water’s complex optical properties, which significantly impact image quality. Challenges like light absorption and scattering result in color distortion and decreased visibility. Traditional underwater image acquisition methods face these obstacles, highlighting the need for advanced techniques to solve the image color shift and image detail loss caused by the underwater environment in the image enhancement process. This study proposes a salient region-guided underwater image enhancement fusion method to alleviate these problems. First, this study proposes an advanced dark channel prior method to reduce haze effects in underwater images, significantly improving visibility and detail. Subsequently, a comprehensive RGB color correction restores the underwater scene’s natural appearance. The innovation of our method is that it fuses through a combination of Laplacian and Gaussian pyramids, guided by salient region coefficients, thus preserving and accentuating the visually significant elements of the underwater environment. Comprehensive subjective and objective evaluations demonstrate our method’s superior performance in enhancing contrast, color depth, and overall visual quality compared to existing methods.

Unmanned surface vehicles (USVs) in nearshore areas are prone to environmental occlusion and electromagnetic interference, which can lead to the failure of traditional satellite-positioning methods. This paper utilizes a visual simultaneous localization and mapping (vSLAM) method to achieve USV positioning in nearshore environments. To address the issues of uneven feature distribution, erroneous depth information, and frequent viewpoint jitter in the visual data of USVs operating in nearshore environments, we propose a stereo vision SLAM system tailored for nearshore conditions: SP-SLAM (Segmentation Point-SLAM). This method is based on ORB-SLAM2 and incorporates a distance segmentation module, which filters feature points from different regions and adaptively adjusts the impact of outliers on iterative optimization, reducing the influence of erroneous depth information on motion scale estimation in open environments. Additionally, our method uses the Sum of Absolute Differences (SAD) for matching image blocks and quadric interpolation to obtain more accurate depth information, constructing a complete map. The experimental results on the USVInland dataset show that SP-SLAM solves the scaling constraint failure problem in nearshore environments and significantly improves the robustness of the stereo SLAM system in such environments.

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality.