In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on non-maximum suppression with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD.

Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typically fine-tuned on single-source data with limited quality and diversity, which may insufficiently elicit the potential of pre-trained Code LLMs. In this paper, we present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. To achieve this, we pioneer to unveil inherent conflicts among the various styles and qualities in multi-source code corpora and introduce data-specific prompts with hindsight relabeling, termed AlchemistPrompts, to harmonize different data sources and instruction-response pairs. Additionally, we propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review. Extensive experiments demonstrate that AlchemistCoder holds a clear lead among all models of the same size (6.7B/7B) and rivals or even surpasses larger models (15B/33B/70B), showcasing the efficacy of our method in refining instruction-following capabilities and advancing the boundaries of code intelligence. Source code and models are available at https://github.com/InternLM/AlchemistCoder.

Recently, state space models (SSM) with efficient design, i.e., Mamba, have shown great potential in modeling long-range dependencies with linear complexity. However, the pure SSM-based model yields sub-optimal underwater enhancement performance due to insufficient local details. Given the superiority of convolution in local perception, we propose a hybrid network, named UWMamba, which combines SSM and convolution for underwater image enhancement. We introduce a conv mamba layer (CML) as the foundation layer to combine the visual state space block (VSSB) with convolution. The convolution is used to capture local detailed features, while the VSSB is employed to capture long-range global features, which complement each other. Furthermore, considering underwater images suffer from severe and uneven degradation of spatial regions and color channels, we propose a Mamba Attention Fusion Module (MAFM), which fuses VSSB with an attention mechanism for better perception of channels and spatial regions. Extensive experiments on real-world underwater image datasets demonstrate the promising performance of our method in both objective metrics and subjective comparisons.

Underwater object detection plays an important role in a variety of marine applications. However, the complexity of the underwater environment (e.g. complex background) and the quality degradation problems (e.g. color deviation) significantly affect the performance of the deep learning-based detector. Many previous works tried to improve the underwater image quality by overcoming the degradation of underwater or designing stronger network structures to enhance the detector feature extraction ability to achieve a higher performance in underwater object detection. However, the former usually inhibits the performance of underwater object detection while the latter does not consider the gap between open-air and underwater domains. This paper presents a novel framework to combine underwater object detection with image reconstruction through a shared backbone and Feature Pyramid Network (FPN). The loss between the reconstructed image and the original image in the reconstruction task is used to make the shared structure have better generalization capability and adaptability to the underwater domain, which can improve the performance of underwater object detection. Moreover, to combine different level features more effectively, UNet-based FPN (UFPN) is proposed to integrate better semantic and texture information obtained from deep and shallow layers, respectively. Extensive experiments and comprehensive evaluation on the URPC2020 dataset show that our approach can lead to 1.4% mAP and 1.0% mAP absolute improvements on RetinaNet and Faster R-CNN baseline with negligible extra overhead. The code is available at https://github.com/BIGWangYuDong/uwtoolbox.