Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models

• URL: http://arxiv.org/abs/2603.22042v2
• Date: Tue, 24 Mar 2026 11:22:50 GMT
• Title: Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models
• Abstract summary: We propose UNcertainty-guided Compositional Hyperbolic Alignment (UNCHA) for enhancing hyperbolic Vision-Language Models (VLMs)<n>UNCHA models part-to-whole semantic representativeness with hyperbolic uncertainty, by assigning lower uncertainty to more representative parts and higher uncertainty to less representative ones for the whole scene.<n>UNCHA achieves state-of-the-art performance on zero-shot classification, retrieval, and multi-label classification benchmarks.
• Score: 30.214536576233957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Vision-Language Models (VLMs) have achieved remarkable performance, their Euclidean embeddings remain limited in capturing hierarchical relationships such as part-to-whole or parent-child structures, and often face challenges in multi-object compositional scenarios. Hyperbolic VLMs mitigate this issue by better preserving hierarchical structures and modeling part-whole relations (i.e., whole scene and its part images) through entailment. However, existing approaches do not model that each part has a different level of semantic representativeness to the whole. We propose UNcertainty-guided Compositional Hyperbolic Alignment (UNCHA) for enhancing hyperbolic VLMs. UNCHA models part-to-whole semantic representativeness with hyperbolic uncertainty, by assigning lower uncertainty to more representative parts and higher uncertainty to less representative ones for the whole scene. This representativeness is then incorporated into the contrastive objective with uncertainty-guided weights. Finally, the uncertainty is further calibrated with an entailment loss regularized by entropy-based term. With the proposed losses, UNCHA learns hyperbolic embeddings with more accurate part-whole ordering, capturing the underlying compositional structure in an image and improving its understanding of complex multi-object scenes. UNCHA achieves state-of-the-art performance on zero-shot classification, retrieval, and multi-label classification benchmarks. Our code and models are available at: https://github.com/jeeit17/UNCHA.git.

Related papers

• Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition [38.294322671641545]
  Fine-grained image recognition is a significant challenge due to the substantial expertise and effort required for manual annotation.<n>Current methods treat class labels as isolated, discrete entities, overlooking the rich semantic relationships between them.<n>We propose Structured-Condensed Prompt Tuning ( SCPT), which enhances semantic structure modeling in prompt learning.
  arXiv  Detail & Related papers  (2026-07-07T12:09:49Z)
• Constrained latent state modeling: A unifying perspective on representation learning under competing constraints [0.6496997624776696]
  Learning latent representations from complex data is central to modern machine learning.<n>Current approaches remain fragmented, relying on distinct -- and often implicit -- assumptions about what these states should represent.<n>We argue that latent representations are typically learned from underconstrained objectives that fail to specify the properties that meaningful latent states should satisfy.
  arXiv  Detail & Related papers  (2026-05-15T14:25:40Z)
• Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering [57.38215918201251]
  We propose HERL, a Hyperbolic Enhanced Representation Learning framework for Incomplete Multi-View Clustering.<n> operating within the Poincaré ball, HERL constructs a structure-aware latent space to enhance representation learning.<n>We show that HERL consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-04-18T10:50:46Z)
• AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models [21.682989096955467]
  AG-VAS (Anchor-Guided Visual Anomaly) is a new framework that expands the LMM vocabulary with three learnable semantic anchor tokens.<n>AG-VAS achieves consistent state-of-the-art performance in the zero-shot setting.
  arXiv  Detail & Related papers  (2026-03-01T22:25:23Z)
• Position: General Alignment Has Hit a Ceiling; Edge Alignment Must Be Taken Seriously [51.03213216886717]
  We take the position that the dominant paradigm of General Alignment reaches a structural ceiling in settings with conflicting values.<n>We introduce Edge Alignment as a distinct approach in which systems preserve multi dimensional value structure.
  arXiv  Detail & Related papers  (2026-02-23T16:51:43Z)
• Understanding Degradation with Vision Language Model [56.09241449206817]
  Understanding visual degradations is a critical yet challenging problem in computer vision.<n>We introduce DU-VLM, a multimodal chain-of-thought model trained with supervised fine-tuning and reinforcement learning.<n>We also introduce textbfDU-110k, a large-scale dataset comprising 110,000 clean-degraded pairs with grounded physical annotations.
  arXiv  Detail & Related papers  (2026-02-04T13:51:15Z)
• SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection [55.54007781679915]
  We propose Synergistic Semantic-Visual Prompting (SSVP), that efficiently fuses diverse visual encodings to elevate model's fine-grained perception.<n>SSVP achieves state-of-the-art performance with 93.0% Image-AUROC and 92.2% Pixel-AUROC on MVTec-AD, significantly outperforming existing zero-shot approaches.
  arXiv  Detail & Related papers  (2026-01-14T04:42:19Z)
• LP-LLM: End-to-End Real-World Degraded License Plate Text Recognition via Large Multimodal Models [4.497411606350301]
  Real-world License Plate Recognition (LPR) faces significant challenges from severe degradations such as motion blur, low resolution, and complex illumination.<n>The prevailing "restoration-then-recognition" two-stage paradigm suffers from a fundamental flaw: the pixel-level optimization objectives of image restoration models are misaligned with the semantic goals of character recognition.<n>We propose an end-to-end structure-aware multimodal reasoning framework based on Qwen3-VL.
  arXiv  Detail & Related papers  (2026-01-14T03:32:55Z)
• UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation [104.59740403500132]
  Multi-modal image segmentation faces real-world deployment challenges from incomplete/corrupted modalities degrading performance.<n>We propose a unified modality-relax segmentation network (UniMRSeg) through hierarchical self-supervised compensation (HSSC)<n>Our approach hierarchically bridges representation gaps between complete and incomplete modalities across input, feature and output levels.
  arXiv  Detail & Related papers  (2025-09-19T17:29:25Z)
• $φ^{\infty}$: Clause Purification, Embedding Realignment, and the Total Suppression of the Em Dash in Autoregressive Language Models [0.0]
  We identify a critical vulnerability in autoregressive transformer language models where the em dash token induces semantic drift.<n>We propose a novel solution combining symbolic clause purification via the phi-infinity operator with targeted embedding matrix.
  arXiv  Detail & Related papers  (2025-06-22T18:27:39Z)
• Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving Vision-Linguistic Compositionality [69.76121008898677]
  Fine-grained Selective Calibrated CLIP integrates local hard negative loss and selective calibrated regularization. Our evaluations show that FSC-CLIP not only achieves compositionality on par with state-of-the-art models but also retains strong multi-modal capabilities.
  arXiv  Detail & Related papers  (2024-10-07T17:16:20Z)

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.