$\mathcal{B}^{3}$-Net: Controlled Posterior Bridge Learning for Multi-Task Dense Prediction

• URL: http://arxiv.org/abs/2605.05722v1
• Date: Thu, 07 May 2026 06:11:12 GMT
• Title: $\mathcal{B}^{3}$-Net: Controlled Posterior Bridge Learning for Multi-Task Dense Prediction
• Abstract summary: Multi-task dense prediction solves complementary pixel-level tasks in a unified model.<n>Existing decoder-side interactions use attention, prompts, routing, diffusion, Mamba, or bridge features to exchange task evidence.<n>We propose $mathcalB3$-Net, a controlled posterior bridge learning framework for multi-task dense prediction.
• Score: 5.2403331899624455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task dense prediction solves complementary pixel-level tasks in a unified model, such as semantic segmentation, depth estimation, surface normal estimation, and edge detection. Existing decoder-side interactions use attention, prompts, routing, diffusion, Mamba, or bridge features to exchange task evidence, but most of them organize this evidence implicitly. They usually fuse task features by similarity or affinity, without explicitly modeling that evidence reliability varies across tasks and spatial locations. As a result, unreliable evidence may contaminate the shared representation and intensify negative transfer. We propose $\mathcal{B}^{3}$-Net, a controlled posterior bridge learning framework for multi-task dense prediction. Our method decomposes decoder-side interaction into reliability estimation, posterior bridge construction, and bounded redistribution. The Precision Field Estimator estimates patch-wise evidence precision from task-reference alignment and local variation. The Posterior Bridge Operator builds a precision-weighted posterior bridge through heteroscedastic evidence fusion, yielding a shared state more reliable than uniform or heuristic mixtures. The Contractive Dispatch Operator redistributes the bridge to each task branch through a bounded update, reducing uncontrolled feature injection. Experiments on NYUD-v2, PASCAL-Context, and Cityscapes show that $\mathcal{B}^{3}$-Net achieves competitive or superior trade-offs over representative CNN-, Transformer-, diffusion-, Mamba-, and bridge-feature-based methods. Backbone-matched comparisons and extensive analyses further verify that the gains arise from controlled posterior bridge learning rather than backbone capacity or decoder scale.

Related papers

• Affordance Agent Harness: Verification-Gated Skill Orchestration [45.231685718099264]
  Affordance grounding requires identifying where and how an agent should interact in open-world scenes.<n>We propose Affordance Agent Harness, a closed-loop runtime that unifies skills with an evidence store and cost control.
  arXiv  Detail & Related papers  (2026-05-01T13:45:16Z)
• CrossCheck-Bench: Diagnosing Compositional Failures in Multimodal Conflict Resolution [20.823419395675412]
  CrossCheck-Bench is a diagnostic benchmark for evaluating contradiction detection in multimodal inputs.<n>We evaluate 13 state-of-the-art vision-language models and observe a consistent performance drop as tasks shift from perceptual matching to logical contradiction detection.
  arXiv  Detail & Related papers  (2025-11-19T12:17:15Z)
• Diffusion Bridge or Flow Matching? A Unifying Framework and Comparative Analysis [57.614436689939986]
  Diffusion Bridge and Flow Matching have both demonstrated compelling empirical performance in transformation between arbitrary distributions.<n>We recast their frameworks through the lens of Optimal Control and prove that the cost function of the Diffusion Bridge is lower.<n>To corroborate these theoretical claims, we propose a novel, powerful architecture for Diffusion Bridge built on a latent Transformer.
  arXiv  Detail & Related papers  (2025-09-29T09:45:22Z)
• DPBridge: Latent Diffusion Bridge for Dense Prediction [49.1574468325115]
  We introduce DPBridge, the first latent diffusion bridge framework for dense prediction tasks.<n>Our method consistently achieves superior performance, demonstrating its effectiveness and capability generalization under different scenarios.
  arXiv  Detail & Related papers  (2024-12-29T15:50:34Z)
• ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer [71.82644727907146]
  We propose a novel $underlineComP$lementary $underlinetr$ansformer, $textbfComPtr$, for diverse bi-source dense prediction tasks.<n>ComPtr treats different inputs equally and builds an efficient dense interaction model in the form of sequence-to-sequence on top of the transformer.
  arXiv  Detail & Related papers  (2023-07-23T15:17:45Z)
• Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty [58.144520501201995]
  Bi-Lipschitz regularization of neural network layers preserve relative distances between data instances in the feature spaces of each layer. With the use of an attentive set encoder, we propose to meta learn either diagonal or diagonal plus low-rank factors to efficiently construct task specific covariance matrices. We also propose an inference procedure which utilizes scaled energy to achieve a final predictive distribution.
  arXiv  Detail & Related papers  (2021-10-12T22:04:19Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis [9.261126434781744]
  We introduce a new framework that transfers the model learned from one bridge to diagnose damage in another bridge without any labels from the target bridge. Our framework trains a hierarchical neural network model in an adversarial way to extract task-shared and task-specific features. We evaluate our framework on experimental data collected from 2 bridges and 3 vehicles.
  arXiv  Detail & Related papers  (2021-07-23T19:39:32Z)
• Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning [6.462702225377603]
  Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as low cost and no need for direct installation or on-site maintenance of equipment on the bridge. Many such approaches require labeled data from every bridge, which is expensive and time-consuming, if not impossible, to obtain. We introduce a transfer learning framework using domain-adversarial training and multi-task learning to detect, localize and quantify damage.
  arXiv  Detail & Related papers  (2020-06-05T19:18:45Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

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.