Lorentz Framework for Semantic Segmentation

• URL: http://arxiv.org/abs/2604.16836v1
• Date: Sat, 18 Apr 2026 05:01:32 GMT
• Title: Lorentz Framework for Semantic Segmentation
• Abstract summary: We propose an architecture-agnostic semantic segmentation framework in the hyperbolic Lorentz model.<n>We employ text embeddings with semantic and visual cues to guide hierarchical pixel-level representations in Lorentz space.<n>Our approach yields free uncertainty estimation, confidence map, boundary delineation, hierarchical and text-based retrieval, and zero-shot performance.
• Score: 1.3904274868884825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic segmentation in hyperbolic space enables compact modeling of hierarchical structure while providing inherent uncertainty quantification. Prior approaches predominantly rely on the Poincaré ball model, which suffers from numerical instability, optimization, and computational challenges. We propose a novel, tractable, architecture-agnostic semantic segmentation framework (pixel-wise and mask classification) in the hyperbolic Lorentz model. We employ text embeddings with semantic and visual cues to guide hierarchical pixel-level representations in Lorentz space. This enables stable and efficient optimization without requiring a Riemannian optimizer, and easily integrates with existing Euclidean architectures. Beyond segmentation, our approach yields free uncertainty estimation, confidence map, boundary delineation, hierarchical and text-based retrieval, and zero-shot performance, reaching generalized flatter minima. We introduce a novel uncertainty and confidence indicator in Lorentz cone embeddings. Further, we provide analytical and empirical insights into Lorentz optimization via gradient analysis. Extensive experiments on ADE20K, COCO-Stuff-164k, Pascal-VOC, and Cityscapes, utilizing state-of-the-art per-pixel classification models (DeepLabV3 and SegFormer) and mask classification models (mask2former and maskformer), validate the effectiveness and generality of our approach. Our results demonstrate the potential of hyperbolic Lorentz embeddings for robust and uncertainty-aware semantic segmentation. Code is available at https://github.com/mxahan/Lorentz_semantic_segmentation.

Related papers

• ModuSeg: Decoupling Object Discovery and Semantic Retrieval for Training-Free Weakly Supervised Segmentation [8.671473711194741]
  Weakly supervised semantic segmentation aims to achieve pixel-level predictions using image-level labels.<n>We present ModuSeg, a training-free weakly supervised semantic segmentation framework centered on explicitly decoupling object discovery and semantic assignment.
  arXiv  Detail & Related papers  (2026-04-08T12:38:07Z)
• Intrinsic Lorentz Neural Network [49.83037691286893]
  Real-world data frequently exhibit latent hierarchical structures, which can be naturally represented by hyperbolic geometry.<n>We propose the emphIntrinsic Lorentz Neural Network (ILNN), a fully intrinsic hyperbolic architecture that conducts all computations within the Lorentz model.<n>At its core, the network introduces a novel emphpoint-to-hyperplane fully connected layer (FC), replacing traditional Euclidean affine logits with closed-form hyperbolic distances.
  arXiv  Detail & Related papers  (2026-02-27T12:48:05Z)
• SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation [0.0]
  We present SPACE-CLIP, an architecture that unlocks and interprets latent geometric knowledge directly from a frozen CLIP vision encoder.<n>A semantic pathway interprets high-level features, dynamically conditioned on global context.<n>A structural pathway extracts fine-grained spatial details from early layers.
  arXiv  Detail & Related papers  (2026-01-25T02:32:01Z)
• HLFormer: Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning [77.434558721499]
  Partially Relevant Video Retrieval (PRVR) addresses the challenge of matching untrimmed videos with text queries describing only partial content.<n>We propose the first hyperbolic modeling framework for PRVR, namely HLFormer, which leverages hyperbolic space learning to compensate for the suboptimal hierarchical modeling capabilities of Euclidean space.
  arXiv  Detail & Related papers  (2025-07-23T10:59:46Z)
• QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction [49.75084732129701]
  3D occupancy prediction is crucial for robust autonomous driving systems.<n>Most existing methods employ dense voxel-based scene representations.<n>We present QuadricFormer, a superquadric-based model for efficient 3D occupancy prediction.
  arXiv  Detail & Related papers  (2025-06-12T17:59:45Z)
• Lightweight Uncertainty Quantification with Simplex Semantic Segmentation for Terrain Traversability [12.765558639563649]
  We propose a simple, light-weight module that can be connected to any pretrained image segmentation model. Our module is based on maximum separation of the segmentation classes by respective prototype vectors. We demonstrate the effectiveness of our module for terrain segmentation.
  arXiv  Detail & Related papers  (2024-07-18T11:00:49Z)
• CAMEL: Curvature-Augmented Manifold Embedding and Learning [21.945022912830044]
  Curvature-Augmented Manifold Embedding and Learning (CAMEL) is proposed for high dimensional data classification, dimension reduction, and visualization. CAMEL has been evaluated on various benchmark datasets and has shown to outperform state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-05T03:12:50Z)
• SlimSeg: Slimmable Semantic Segmentation with Boundary Supervision [54.16430358203348]
  We propose a simple but effective slimmable semantic segmentation (SlimSeg) method, which can be executed at different capacities during inference. We show that our proposed SlimSeg with various mainstream networks can produce flexible models that provide dynamic adjustment of computational cost and better performance.
  arXiv  Detail & Related papers  (2022-07-13T14:41:05Z)
• Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces [39.71927912296049]
  We propose a unified framework for learning scalable and simple hyperbolic linear classifiers. The gist of our approach is to focus on Poincar'e ball models and formulate the classification problems using tangent space formalisms. The excellent performance of the Poincar'e second-order and strategic perceptrons shows that the proposed framework can be extended to general machine learning problems in hyperbolic spaces.
  arXiv  Detail & Related papers  (2022-03-07T21:36:21Z)
• Towards Efficient Scene Understanding via Squeeze Reasoning [71.1139549949694]
  We propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks.
  arXiv  Detail & Related papers  (2020-11-06T12:17:01Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)

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.