TailProp: content-adaptive light- and heavy-tailed propagation for vision

• URL: http://arxiv.org/abs/2609.11081v1
• Date: Thu, 10 Sep 2026 04:38:45 GMT
• Title: TailProp: content-adaptive light- and heavy-tailed propagation for vision
• Abstract summary: We introduce TailProp, a hierarchical vision backbone for cross-regime adaptive propagation.<n>Across image classification, object detection, semantic segmentation, robustness, and cross-backbone restoration, TailProp consistently outperforms matched propagation baselines.
• Score: 11.825348402205051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Science-inspired vision models show that explicit propagation dynamics can provide structured and interpretable alternatives to conventional token mixing. Existing formulations, however, typically construct and adapt visual propagation within a particular dynamical family, while visual representations can require substantially different spatial interactions across samples, channels, and network stages. We explore cross-regime adaptive propagation and introduce TailProp, a hierarchical vision backbone built upon the Tail Propagation Operator (TPO). TPO uses Gaussian and Cauchy stable-process propagators as complementary bases with rapidly decaying and heavy-tailed spatial influence, and predicts a content-conditioned channel-wise coefficient to adaptively combine them. Because this coefficient is spatially shared, the two responses are fused directly in the DCT domain with a single DCT/IDCT pair, yielding $O(N^{1.5})$ spatial mixing for square feature maps with $N=HW$ and fixed channel width. Across image classification, object detection, semantic segmentation, robustness, and cross-backbone restoration, TailProp consistently outperforms matched propagation baselines; TailProp-B reaches 84.4% Top-1 accuracy on ImageNet-1K, 50.3/44.8 box/mask AP under the 3x Mask R-CNN schedule, and 50.8% mIoU on ADE20K. Controlled ablations further show that these gains are not explained by single-basis propagation, an additional same-family branch, or within-family adaptive order alone, supporting complementary two-basis propagation as an effective design principle for visual representation learning.

Related papers

• Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer [19.12698354620558]
  Spiking Transformers model token interactions primarily through spiking self-attention (SSA)<n>We introduce Spiking Local Interaction (SLI) and Adaptive Complementary Fusion (ACF)<n>ACF integrates SSA and SLI through layer-specific, channel-wise coefficients that adaptively balance their contributions at different network depths.
  arXiv  Detail & Related papers  (2026-08-11T14:23:26Z)
• Structure-Feature Aligned Graph Learning via Alternating Constrained Optimization [7.881713230406407]
  We introduce a constrained two-view framework for node prediction that aligns structure-conditioned GNN embeddings with a structure-free feature prior learned by an anchor model.<n>We also propose a Channel-Split Adaptive Gated GNN (CSAG-GNN) that dynamically routes representations between global spectral smoothing and local spatial discrimination through a node-wise gating mechanism.
  arXiv  Detail & Related papers  (2026-07-13T13:58:52Z)
• SEED: Targeted Data Selection by Weighted Independent Set [76.68391670109433]
  We develop a robust and scalable data selection pipeline dubbed SEED.<n>SEED consistently outperforms state-of-the-art methods on instruction tuning, visual instruction tuning, and semantic segmentation.
  arXiv  Detail & Related papers  (2026-05-15T07:26:54Z)
• Deep Dreams Are Made of This: Visualizing Monosemantic Features in Diffusion Models [0.12277343096128711]
  This paper proposes latent visualization by optimization (LVO), a mechanistic interpretability technique that extends feature visualization by optimization to latent diffusion models.<n>LVO employs sparse autoencoders (SAEs) to disentangle polysemantic layer representations into monosemantic features.
  arXiv  Detail & Related papers  (2026-05-06T13:21:24Z)
• INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields [1.0487944945684993]
  INST-Align couples a coordinate-based deformation network with a shared Canonical Expression Field.<n>It achieves state-of-the-art mean OT Accuracy (0.702), NN Accuracy (0.719), and Chamfer distance, with Chamfer reductions of up to 94.9% on large-deformation sections.<n>It also yields biologically meaningful spatial embeddings and coherent 3D tissue reconstruction.
  arXiv  Detail & Related papers  (2026-04-13T21:44:18Z)
• TwistNet-2D: Learning Second-Order Channel Interactions via Spiral Twisting for Texture Recognition [8.911086692137593]
  We introduce TwistNet-2D, a lightweight module that computes emphlocal pairwise channel products under directional spatial displacement.<n>The core component, Spiral-Twisted Channel Interaction (STCI), shifts one feature map along a prescribed direction before element-wise channel multiplication.
  arXiv  Detail & Related papers  (2026-02-06T23:25:00Z)
• Harnessing Group-Oriented Consistency Constraints for Semi-Supervised Semantic Segmentation in CdZnTe Semiconductors [71.44213719783703]
  Intra-group Consistency Augmentation Framework (ICAF) developed to label Cadmium Zinc Telluride (CdZnTe) semiconductor images.<n>ICAF consists of two key modules, the View Augmentation Module (VAM) and the View Correction Module (VCM)<n>ICAF achieves a 70.6% mIoU on the CdZnTe dataset using only 2 group-annotated data.
  arXiv  Detail & Related papers  (2025-08-18T09:40:36Z)
• Double-Shot 3D Shape Measurement with a Dual-Branch Network for Structured Light Projection Profilometry [14.749887303860717]
  We propose a dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet) to process different structured light (SL) modalities.<n>Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images.<n>Our method can reduce fringe order ambiguity while producing high-accuracy results on self-made datasets.
  arXiv  Detail & Related papers  (2024-07-19T10:49:26Z)
• ClusterFormer: Clustering As A Universal Visual Learner [80.79669078819562]
  CLUSTERFORMER is a universal vision model based on the CLUSTERing paradigm with TransFORMER. It is capable of tackling heterogeneous vision tasks with varying levels of clustering granularity. For its efficacy, we hope our work can catalyze a paradigm shift in universal models in computer vision.
  arXiv  Detail & Related papers  (2023-09-22T22:12:30Z)
• Learning Partial Correlation based Deep Visual Representation for Image Classification [61.0532370259644]
  We formulate sparse inverse covariance estimation (SICE) as a novel structured layer of CNN. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem. Experiments show the efficacy and superior classification performance of our model.
  arXiv  Detail & Related papers  (2023-04-23T10:09:01Z)
• Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets [91.25055890980084]
  There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets. We propose Dynamic Hybrid Vision Transformer (DHVT) as the solution to enhance the two inductive biases. Our DHVT achieves a series of state-of-the-art performance with a lightweight model, 85.68% on CIFAR-100 with 22.8M parameters, 82.3% on ImageNet-1K with 24.0M parameters.
  arXiv  Detail & Related papers  (2022-10-12T06:54:39Z)
• Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks [57.64866581615309]
  We propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks. Results indicate that Recombination and Recalibration improve the results of a competitive baseline, and generalize across three different problems.
  arXiv  Detail & Related papers  (2020-06-19T15:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.