Liquid Fusion of Heterogeneous Representations Towards General Salient Object Detection

General Salient Object Detection (SOD) aims to identify andsegment visually interesting objects from uni-modality or multi-modalityscenes, recently advanced by cutting-edge State Space Models (SSMs).However, a critical limitation of current approaches is their neglect of theinherent spectral biases exhibited by different neural network paradigms.By digging to the dataset-level spectral analysis of Convolutional Neu-ral Networks (CNNs) and SSMs, their semantic representations are in-herently complementary based on their complementary frequency prefer-ences. Inspired by this, we harmonize heterogeneous representations fromSSMs and CNNs to bridge their spectral biases for general salient objectdetection. To this end, inspired by the dynamic information propaga-tion of Liquid Neural Networks (LNNs), we introduce a liquid fusion todynamically integrates features from two backbones, including VMambaand ConvNeXt, referred to Liquid Fusion Network (LFNet). Concretely,by treating the continuous VMamba features and ConvNeXt features asevolving states and exogenous stimulus, respectively, LFNet employs adynamic gating mechanism for content-aware feature aggregation. Cru-cially, this state-stimulus paradigm enables to scale to multi-modal cues,resulting in flexibility in general SOD. Besides, a Saliency-Guided Up-sampling (SGU) operator to propagate the features to the shallow layer,which leverages a spectral-spatial co-design to suppress upsampling ar-tifacts while preserving semantics. Extensive experiments across five di-verse tasks (RGB, RGB-D, RGB-T, VSOD, and VDT) demonstrate thatLFNet achieves state-of-the-art performance, offering a superior trade-offbetween detection accuracy and model efficiency. Code has been releasedat https://github.com/cke520/LFNet.