Although multimodal large language models (MLLMs) have achieved remarkable progress, understanding 3D spatial relationships from 2D images remains a critical challenge. Existing methods primarily rely on symbolic text tokens, which inherently lack the fidelity to represent continuous geometric information. While recent methods use latent representations to enhance reasoning, relying on a single latent type cannot adapt to the diversity of spatial tasks, leading to misalignment in complex geometric scenarios. To address these limitations, we propose GeoAnchor, an interleaved text-latent reasoning framework. GeoAnchor decomposes 3D spatial information into three complementary components: position latents for object grounding, direction latents for relational orientation, and geometry latents for scene structure. These components are recombined in a structured space to construct local evidence while capturing global context, enabling dynamic and interpretable reasoning. Furthermore, we introduce a collaborative training strategy that guides the model from local spatial perception to comprehensive 3D understanding. Extensive experiments on diverse and complex 3D reasoning tasks demonstrate that GeoAnchor outperforms the state of the art, validating its effectiveness and generalization capabilities.
Text-based person retrieval (TPR) has become a crucial task in cross-modal retrieval due to its broad application in fields such as public safety and criminal investigation. Existing TPR methods typically rely on fully fine-tuning large-scale pretrained vision-language models like CLIP, which incurs high computational costs and tends to exhibit poor generalization in unseen domains due to overfitting. Fortunately, Parameter-Efficient Transfer Learning (PETL) has emerged as a lightweight alternative. However, applying PETL to TPR remains challenging, as its limited adaptation capacity struggles to capture intricate identity cues and becomes highly susceptible to gradient interference from unreliable image-text pairs. To address these challenges, we present a PETL-based framework named UniGR that unifies granularity and reliability for robust and efficient TPR. Specifically, we design a multi-granularity relational adapter (MRA) to capture both coarse-grained global and fine-grained local relational features among tokens, equipping the generic backbone with the task-specific, precise understanding needed for TPR. To combat the noise sensitivity of PETL, a reliability-aware reweighting strategy (RRS) is introduced to adaptively down-weight unreliable samples during training. Furthermore, we propose a parameter-free cross-modal cyclic verification (CMCV) module to mitigate ambiguities in cross-modal matching computations and refine retrieval ranking further. Experiments on benchmarks corroborate the superiority of UniGR among parameter-efficient methods. Remarkably, with only 4.5% of trainable parameters, UniGR outperforms most fully fine-tuned methods while maintaining strong generalization.
Jingchen Hao, Jiang Liu, Zhen Peng et al.· Annual International ACM SIG...· 0 citations
Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs) have significantly advanced document parsing. However, existing approaches still face two major challenges. First, decoupled VLM-based methods heavily rely on accurate layout analysis, where geometric distortions in camera-captured documents can introduce cascading errors. Second, although end-to-end VLM-based methods alleviate the dependence on explicit layout detection, they often suffer from redundant generation, hallucinations, and insufficient structural reasoning in high-resolution scenarios. To address these challenges, we propose NaviDC-OCR, a unified framework for document parsing. NaviDC-OCR introduces deformation-aware learning to incorporate geometric perception into VLMs and proposes an adaptive sampling mechanism for complex layout representation. Furthermore, a content-structure decoupled learning strategy is developed to explicitly model formula grammars and table structures, enabling more effective structured representation learning. Extensive experiments demonstrate that NaviDC-OCR achieves state-of-the-art performance across diverse document parsing benchmarks. It obtains overall scores of 96.87, 88.53 and 78.41 on OmniDocBench v1.6, Wild-OmniDocBench, and PureDocBench, respectively, and ranks first in the ICDAR 2026 Sci-ImageMiner Challenge. These results validate the effectiveness and generalization capability of NaviDC-OCR in complex document parsing scenarios.
Peng Cai, Zhaofan Zou, Shifa Liu et al.· 0 citations
