Yi Zhang

Yue Cao, Quansong He, Kaishen Wang et al.

U-like networks have become fundamental frameworks in medical image segmentation through skip connections that bridge high-level semantics and low-level spatial details. Despite their success, conventional skip connections exhibit two key limitations: inter-feature constraints and intra-feature constraints. The inter-feature constraint refers to the static nature of feature fusion in traditional skip connections, where information is transmitted along fixed pathways regardless of feature content. The intra-feature constraint arises from the insufficient modeling of multi-scale feature interactions, thereby hindering the effective aggregation of global contextual information. To overcome these limitations, we propose a novel Dynamic Skip Connection (DSC) block that fundamentally enhances cross-layer connectivity through adaptive mechanisms. The DSC block integrates two complementary components. (1) Test-Time Training (TTT) module. This module addresses the inter-feature constraint by enabling dynamic adaptation of hidden representations during inference, facilitating content-aware feature refinement. (2) Dynamic Multi-Scale Kernel (DMSK) module. To mitigate the intra-feature constraint, this module adaptively selects kernel sizes based on global contextual cues, enhancing the network capacity for multi-scale feature integration. The DSC block is architecture-agnostic and can be seamlessly incorporated into existing U-like network structures. Extensive experiments demonstrate the plug-and-play effectiveness of the proposed DSC block across CNN-based, Transformer-based, hybrid CNN-Transformer, and Mamba-based U-like networks.

Junyi Hu, Tian Bai, Fengyi Wu et al.

Feature fusion is critical for high-performance vision models but often incurs prohibitive complexity. However, prevailing attention-based fusion methods often involve significant computational complexity and implementation challenges, limiting their efficiency in resource-constrained environments. To address these issues, we introduce the Pyramid Sparse Transformer (PST), a lightweight, plug-and-play module that integrates coarse-to-fine token selection and shared attention parameters to reduce computation while preserving spatial detail. PST can be trained using only coarse attention and seamlessly activated at inference for further accuracy gains without retraining. When added to state-of-the-art real-time detection models, such as YOLOv11-N/S/M, PST yields mAP improvements of 0.9%, 0.5%, and 0.4% on MS COCO with minimal latency impact. Likewise, embedding PST into ResNet-18/50/101 as backbones, boosts ImageNet top-1 accuracy by 6.5%, 1.7%, and 1.0%, respectively. These results demonstrate PST's effectiveness as a simple, hardware-friendly enhancement for both detection and classification tasks.