Fast adapting to unknown peers (partners or opponents) with different strategies is a key challenge in multi-agent games. To do so, it is crucial for the agent to probe and identify the peer's strategy efficiently...
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Hyperspectral images (HSIs) provide rich spectral information that has been widely used in numerous computer vision tasks. However, their low spatial resolution often prevents their use in applications such as image s...
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Over the past decade, the field of holography has gained significant ground due to advances in computational imaging. However, the utilization of computational tools is hampered by the mismatch between experimental se...
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Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of modeling long-range dependencies compar...
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The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in mo...
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Colorectal cancer (CRC) is one of the prominent causes of cancer-related morbidity and mortality worldwide. More AI-assisted methods are conducted for early polyp detection and segmentation to improve the screening ef...
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ISBN:
(纸本)9798400718779
Colorectal cancer (CRC) is one of the prominent causes of cancer-related morbidity and mortality worldwide. More AI-assisted methods are conducted for early polyp detection and segmentation to improve the screening efficacy. However, previous solutions generally exhibit weak segmentation performance due to irregular structures of polyps, while the model robustness suffers from background noise of homogeneous neighbors. To this end, we propose a novel Multi-Focus Attention Network (MFANet) to encode multi-dimensional information (i.e., scale, contour, and shape) as fine-grained cues for polyp segmentation. Concretely, a Scale-Residual-Aware Attention (SRAA) is designed to apply the residual operation over each layer of the feature pyramid architecture, which could minimize the feature interference among different scales. To improve the model robustness, a Geometry-Structure-Aware Attention (GSAA) is formulated to integrate and refine multi-dimensional geometric features via a Channel-Wise Enhance Attention (CWEA), which condenses the spatial information and recalibrates the channel importance for adaptive feature recalibration. Experiments on six public datasets indicate the effectiveness of the proposed method. Notably, on the more challenging BKAI dataset, which is featured by tiny polyps with serious interference of homogeneous neighboring region, our MFANet can outperform the state-of-the-art (SOTA) methods. Additionally, it is experimentally verified that our approach consistently exhibits better segmentation performance with higher robustness against different attack strategies (i.e., FGSM, WaNet and PGD).
In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues. The tasks of self-supervised and weakly-supervised point cloud completion involve reconstructing missing regions of these inc...
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The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confusion is not well assessed within this frame...
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As LLM-as-a-Judge emerges as a new paradigm for assessing large language models (LLMs), concerns have been raised regarding the alignment, bias, and stability of LLM evaluators. While substantial work has focused on a...
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Desires motivate humans to interact autonomously with the complex world. In contrast, current AI agents require explicit task specifications, such as instructions or reward functions, which constrain their autonomy an...
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