Represenation
-
Cola2: Co-Adaptive Hierarchical Language Modeling with Continuous Semantic Compression and Balanced Discrete Generation
2026-10-22 14:04:07Hongcan Guo, Yian Zhao, Feng Wang, Shen Nie, Guoqiang Wei, Yan Zeng, Hengshuang ZhaoAbstract
Hierarchical language generation separates continuous semantic modeling from discrete text realization, but its effectiveness depends on what the latent space compresses and how the discrete generator uses it. We propose Cola2, a framework that jointly studies continuous semantic compression, the balance between reconstruction and generation, and continuous-discrete co-adaptation. Cola2 dynamically segments text into semantic spans, controls the compression rate through auxiliary-loss-free negative feedback, and combines reconstruction and generation supervision to learn compact, predictive latent representations. A block-causal masked diffusion decoder is trained with clean, noisy, and dropped latent conditions, while classifier-free guidance adjusts the balance between semantic reconstruction and autonomous generation. Token-prediction gradients update the encoder through the latents, whereas Flow Matching prior learning is decoupled from representation learning by stop-gradient. During inference, generated text is re-encoded under a fixed span layout to keep the latent history consistent with the actual text context. Experiments on five downstream tasks with about 210 billion training tokens validate the core designs and show competitive, unsaturated scaling. With 346.03 billion source tokens, an unconverged checkpoint reaches a 60.10 average across eight tasks and surpasses Llama-3-8B on HellaSwag and StoryCloze despite its reported pretraining budget being over 43 times larger. These results demonstrate the potential of continuous-latent hierarchical language generation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.31305
PDF
-
I$^2$-KAN: Interpretable Intra- and Inter-Modal Multi-Pathway Representation Learning for Multimodal Sentiment Analysis
2026-10-22 10:57:20Xiaoting Xie, Feng Qiao, Xianwei Zhang, Hailong Chen, Kaiwen Zheng, Joemon M. Jose, Xuri Ge, Yujun LiAbstract
Multimodal sentiment analysis (MSA) aims to infer sentiment by integrating linguistic, acoustic, and visual information. However, many fusion methods integrate modality-specific representations and cross-modal interactions into joint representations, making it difficult to determine whether predictive utility arises from a modality itself or from its interactions with others. More fundamentally, a modality's standalone predictive utility may not faithfully reflect its interaction-conditioned predictive utility. To address this issue, we propose I$^2$-KAN, an interpretable multi-pathway framework that explicitly maintains intra-modal and pairwise inter-modal information in separate structural pathways. Each pathway is refined by a Task-Decoupled Linear-Gated Denoising Bottleneck (TDL-GDB) to learn compact and task-relevant representations, followed by structured Kolmogorov-Arnold Network fusion that retains explicit pathway associations and supports multi-level interpretation. Extensive experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS v2 demonstrate strong overall performance. Ablation and interpretability analyses consistently show across the three benchmarks that low standalone predictive utility does not necessarily imply low interaction-conditioned predictive utility: a modality that appears weak in isolation can remain informative through pairwise interactions. These findings highlight the interaction-dependent nature of multimodal utility and suggest that modality utility should be characterized from both standalone and interaction-conditioned perspectives.
Abstract (translated)
URL
https://arxiv.org/abs/2610.31087
PDF
-
Learning from Next-Frame Prediction: Towards Unified and Scalable Visual Representation Learning
2026-10-22 04:46:49Yifeng Yao, Jinghan Li, Zhihao Zhang, Yajiao Xiong, Yang Jin, Kun Xu, Huishuai Zhang, Dongyan ZhaoAbstract
Video provides a natural source of supervision: what happens next serves as a learning signal for visual world modeling. Yet masked video pretraining focuses on recovering missing content from surrounding context rather than predicting the future from the past. Autoregressive methods instead predict forward one token at a time, but have yet to match joint-embedding methods in representation quality. This gap motivates us to revisit the granularity and space of future prediction. In this work, we introduce VANE, a self-supervised visual pretraining framework built on next-frame prediction in embedding space. A causal encoder summarizes past observations for a state predictor that infers the representation of the entire next frame, making temporal evolution the primary learning signal. This prediction is carried out in a fixed embedding space provided by a frozen semantic tokenizer, emphasizing scene content and structure. To retain fine-grained spatial detail alongside semantic content, a lightweight auxiliary generative decoder reconstructs future-frame patches conditioned on the predicted representations. To unify visual modeling across images and videos, we construct a local-to-global crop sequence for still images, allowing the same encoder to predict newly revealed regions from earlier crops under the same objective. Experiments show that VANE improves with model and data scaling, achieving SOTA on most anticipation, recognition, and dense perception benchmarks among unified encoders. Especially on EPIC-KITCHENS-100 action anticipation, it surpasses V-JEPA 2.1 by 7.1 points in mean-class recall@5. These results demonstrate that next-frame prediction in embedding space is effective and scalable for visual world modeling.
Abstract (translated)
URL
https://arxiv.org/abs/2610.30724
PDF
-
MoRE: Motion-Regularized Encoding for Articulated 3D Generation
2026-10-21 18:55:12Guantian Zheng, Shan JinAbstract
Generating an articulated object from a single image requires recovering both its appearance and the mechanism governing its parts. A compact encoding of joint attributes does not by itself ensure that reconstruction preserves the resulting motion. We present MoRE, Motion-Regularized Encoding for articulated 3D generation. MoRE represents coordinate-aligned joint lines with axis classification and two axis-orthogonal Plücker moment coefficients, removing the arbitrary choice of an origin along the axis. During representation learning, decoded joint hypotheses are used to move corresponding three-dimensional anchors, and their motions are compared with those of annotated joints. A differentiable expectation over joint types and axis directions couples attribute reconstruction to the motion it induces. An image-conditioned flow learns the resulting functional latents, while a multi-state-adapted Gaussian model supplies appearance. At inference, recovered part assignments and joints articulate a single set of rest-pose Gaussians without regenerating the object for each state. Experiments on PartNet-Mobility and ACD show stronger part-box agreement, articulated-state geometry and visual similarity than the compared baselines, while joint prediction reduces axis, origin and range errors relative to ArtiLatent. Controlled ablations support the joint-line encoding and show that motion supervision reduces anchor-motion error. Code and pretrained checkpoints will be released in the future.
Abstract (translated)
URL
https://arxiv.org/abs/2610.30337
PDF
-
Self-Distilling Future Video Representations for World Action Models
2026-10-21 18:33:13Huiwon Jang, Suhyeok Jang, Hyeonmo Kang, Seungku Kim, Jinwoo Shin, Sihyun YuAbstract
World Action Models (WAMs) have emerged as a promising approach to learning generalist robot policies using rich spatiotemporal priors from pre-trained video diffusion models. A recent line of WAMs improves inference efficiency by conditioning a separate action decoder on intermediate future video representations extracted from a single denoising step, avoiding costly iterative denoising required for video generation. This design makes action prediction depend directly on the quality of these future representations. In this paper, we introduce Future REpresentation learning via self-Distillation with temporal context (FRED), a framework that trains WAMs to improve the quality of future video representations from noise in a single denoising step. Specifically, FRED uses self-distillation to predict future representations from noise, using representations extracted from ground-truth future observations as targets. We further condition the video model on historical observations, providing temporal context for predicting future representations better. Across diverse real-world manipulation scenarios, FRED consistently outperforms competing WAMs. It achieves particularly large gains on long-horizon and out-of-domain tasks under zero-shot environment generalization, and substantially improves human-to-robot skill transfer, reaching 50.0% task success on tasks absent from robot training data and demonstrated only in human videos, compared with 19.4% for the best competing WAM.
Abstract (translated)
URL
https://arxiv.org/abs/2610.30320
PDF
-
Are Joint-Embedding Predictive Objectives Effective for Longitudinal EHRs?
2026-10-21 10:18:40Ashvin Gupta, Brendan Delaney, Alessandra RussoAbstract
Masked language modelling (MLM) is the dominant pretraining objective for transformer encoders applied to longitudinal electronic health records (EHRs). However, reconstructing individual events may overemphasise noisy or weakly informative observations and does not explicitly encourage learning higher-level patient states. Joint-Embedding Predictive Architectures (JEPAs) instead learn by predicting representations in latent space and have shown strong performance in a variety of domains. In this paper, we investigate to what extent JEPA can be adapted to EHR sequence representation. Our empirical investigation using MIMIC-IV shows that although all three of our JEPA formulations train stably without representational collapse, they consistently underperform standard MLM. We find that target size, not target heterogeneity, increases prediction error, yet reducing the target size does not recover downstream performance. This suggests that stable latent prediction alone is insufficient for competitive EHR representation learning and highlights the challenge of defining clinically meaningful latent targets for EHR.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29528
PDF
-
YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations
2026-10-21 07:09:14Sen Jia, Siqi Wei, Junyu Wang, Zijun Lv, Pan Liu, Meng Xu, Xiyou Fu, Yuxi Sun, Qiang Liu, Shuyu Zhang, Lin MuAbstract
Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristics across satellite imagery, gridded oceanographic fields, and buoy observations. Existing approaches largely focus on task-specific ocean modeling, while learning transferable representations from heterogeneous observations remains an open problem. To address this challenge, we propose YingLan, a multimodal ocean foundation model that integrates physics-attribute-conditioned dynamic encoding, Spatiotemporal Increment Coupling (STIC), and ocean-state evolution-aware pretraining. Specifically, the dynamic encoding mechanism generates adaptive input projections conditioned on physical semantics and observation characteristics, enabling flexible representation of heterogeneous ocean variables. STIC models multiscale spatiotemporal dependencies through cross-scale interactions of latent temporal increments. The pretraining strategy further combines masked observation reconstruction with temporal evolution objectives to facilitate the learning of coherent ocean-state representations across different prediction horizons. YingLan is implemented using MindSpore and trained on Huawei Ascend 910B NPUs, demonstrating its compatibility with domestic AI computing infrastructure. In a seven-day sea surface salinity (SSS) forecasting task conducted in the Shenzhen coastal waters, YingLan achieves an average RMSE of 0.687508 PSU, corresponding to relative error reductions of 0.58%, 5.31%, and 12.42% compared with LangYa, XiHe, and GloNet2, respectively. These results provide initial evidence of the model's effectiveness in regional ocean forecasting, while broader cross-task and cross-regional evaluations remain necessary to assess its generalization capabilities.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29313
PDF
-
ReSeg: Retraining-free segmentation of abdominal organs via generalizable semantic representation learning
2026-10-21 02:31:19Derong Yu, Guoyan ZhengAbstract
Abdominal organ segmentation is essential in modern medical applications. While existing deep learning approaches demonstrate promising performance, their heavy reliance on large-scale image data raises concerns over generalization capability and privacy risks. This motivates the development of generalizable segmentation training paradigms based on domain-randomized image synthesis, facilitating generalization across unseen real image domains. However, existing paradigms either suffer from limited segmentation performance when applied to abdominal organs, or require dataset-specific finetuning. To address these limitations, we propose ReSeg, a novel two-stage retraining-free framework for domain-agnostic zero-shot abdominal organ segmentation. At the first stage, our organ-agnostic synthetic data-driven pretraining approach exploits semantically and visually diverse synthetic images generated by a dedicated data engine to optimize a Generalizable Semantic Representation Network (GSRNet). The network is thereby capable of learning semantic features robust against variations in anatomical structure, imaging contrast, resolution and spatial context, which provide universal representation capabilities that serve as a foundation for segmentation learning. Subsequently, in the second stage, we continue to train an Organ-specific Encoder-decoder Segmentation Network (OESNet), whose encoder is initialized with the pretrained weights. This training process is facilitated by another dedicated data engine, which generates diversified visual images while strictly retaining the inherent spatial layout of abdominal organs. Comprehensive evaluation on seven abdominal organ segmentation datasets across three different modalities including CT, MRI, and PET demonstrates that ReSeg consistently outperforms existing state-of-the-art (SOTA) methods while eliminating the need for real training images.
Abstract (translated)
URL
https://arxiv.org/abs/2610.29090
PDF
-
Depth-guided Geometric Adaptation for Few-shot Image Classification
2026-10-20 19:29:42Zhiqi Miao, Estefan\'ia Talavera, Weichuan Zhang, Jiapan GuoAbstract
Few-shot image classification (FSIC) frequently suffers from visual ambiguity, where background clutter and RGB appearance similarities hinder discriminative feature matching in data-scarce settings. To mitigate this, we propose DAFIC, a geometry-guided framework that incorporates monocular depth into representation learning, episodic adaptation, and dense support-query matching. DAFIC first learns geometry-aware token representations through RGB-D self-supervised pre-training. Subsequently, the Depth-guided Geometric Adaptation (DGA) module uses depth-derived geometry to refine patch representations with geometry-enriched class context while suppressing background interference. Spatial Bidirectional Patch Matching (SBiM) then performs bidirectional dense matching on the refined features, reducing sensitivity to spurious correspondences and background clutter. Extensive experiments on four benchmarks show that DAFIC achieves strong performance against existing methods, with ablation and robustness analyses supporting the effectiveness of coupling geometry-conditioned episodic adaptation with bidirectional matching for few-shot visual recognition.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28856
PDF
-
Spatially Informed and Direction Sensitive Representation Learning for Spatial Transcriptomics
2026-10-20 17:32:57Azim Dehghani Amirabad, Ali Khodabandeh Yalabadi, Artem Moskalev, Yun-Ching Chen, Mehdi Pirooznia, Scott Oloff, Rui LiaoAbstract
Cellular states are shaped by both intrinsic gene expression programs and the spatial microenvironment in which cells reside. Spatial transcriptomics makes this context measurable, but effective representation learning requires capturing both spatially informative molecular signals and intercellular organization. Existing approaches address these challenges only partially: conventional gene selection can overlook spatially structured signals, while methods for encoding cell-cell relationships often either reduce a cell's microenvironment to proximity alone, discarding direction, or rely on absolute coordinates tied to an arbitrary tissue frame. Consequently, cells with similar neighbor distances but distinct spatial organization may receive overly similar representations, despite experiencing different biological contexts that shape their cellular states. We introduce Tessera, a spatial representation learning framework that addresses both molecular selection and cellular context. Tessera combines spatially informed gene selection with a geometric inductive bias that contextualizes each cell within its local tissue environment. By modeling relative cell-cell relationships, Tessera is translation-invariant while preserving direction, allowing neighboring cells to contribute differently according to their spatial context and cellular state. Across held-out tissues and technologies, Tessera outperforms existing spatial foundation models in representation quality and generalizes zero-shot to contexts up to $16\times$ larger than those seen during training.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28579
PDF
-
Touch-JEPA: Tactile Representation Learning from Hand-Object Video with Limited Tactile Supervision
2026-10-20 16:56:11Minseong Kweon, Qiao Feng, Yiming Huang, Lingjie LiuAbstract
Understanding contact and tactile magnitude in hand-object interaction is a key step toward physically intelligent systems, yet learning such representations is limited by the cost and restricted coverage of tactile measurements. We present Touch-JEPA, a framework for learning tactile representations from hand-object videos with limited tactile supervision. We first introduce a self-supervised pretraining objective that learns from video alone without tactile supervision. Using video-derived hand geometry, Touch-JEPA predicts masked visual features at corresponding hand-surface locations from spatial and temporal context, encouraging the representation to capture latent contact dynamics. We then extend this formulation to joint representation learning, where masked prediction is applied to all videos while tactile supervision is available only for a labeled subset, grounding the shared representation in measured contact and magnitude. With self-supervised pretraining alone, simple supervised readouts on frozen Touch-JEPA features outperform supervised baselines in contact discrimination and tactile magnitude estimation across three human-hand benchmarks, and transfer to robotic hands without robot tactile training data. Joint learning with limited tactile supervision further improves contact and magnitude estimation on the human-hand benchmarks. Together, these results show that hand-object video can provide scalable self-supervision for tactile representation learning, while limited tactile measurements provide complementary physical grounding.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28519
PDF
-
Exploring Temporal Context-Aware Physical Grounding in Latent World Models
2026-10-20 16:30:27Yilei Man, Yang Chen, Jingcai GuoAbstract
Learning latent dynamics is fundamental to building action-conditioned world models for planning. Among existing families, Joint Embedding Predictive Architectures (JEPAs) offer a natural framework by modeling transitions directly in latent space rather than at the pixel level. However, two issues remain in JEPA-based world models: (i) physical grounding lacks explicit aggregation of temporal context and (ii) action planning always overly relies on generic sampling distributions. To this end, we propose $\mathcal{G}$-JEPA, which incorporates temporal context into physical grounding for latent world models and couples the learned models with prior-guided action planning. Specifically, during learning, we incorporate a shared causal temporal module that aggregates temporal context within observation and prediction sequences under physical state supervision, grounding both in task-relevant physical states. When planning, we further introduce action inference through stepwise conditioning of a generative action distribution on the current and goal latent representations together with their evolving discrepancy, yielding anchored proposals that guide model-based online refinement. Together, physical state supervision guides temporal representation learning, while the action prior guides search through the learned world model. Experiments demonstrate improved planning performance on manipulation tasks, including tasks with increasing temporal separation between initial and goal observations. Code is publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28483
PDF
-
Causal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning
2026-10-20 12:43:23Danru Xu, Aapo Hyv\"arinen, Sara MagliacaneAbstract
Causal discovery with latent confounding is particularly challenging, but a common setting in real-world data. In this work, we focus on linear causal models with latent confounding, represented by acyclic directed mixed graphs (ADMGs). Existing methods that fully identify ADMGs typically assume non-Gaussianity or graphical constraints, such as bow-freeness. In contrast, we propose Linear ADMG Learning (LinADMG), a framework for learning ADMGs for linear causal models through identifiable representation learning. Rather than relying on non-Gaussianity or bow-freeness, LinADMG leverages multi-domain settings to identify the noise variables. We show that the identifiability of the noise variables is sufficient for recovering all direct causal relations, and that, under an additional latent-confounding no-cancellation condition, LinADMG can identify the whole ADMG. We implement LinADMG with four identifiable representation learning approaches from the literature and validate the methods on simulated and semisynthetic datasets.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28134
PDF
-
Robust Representation Learning for Spiking Neural Networks based on Conditional Information Bottleneck
2026-10-20 12:21:31Yuli Zha, Wang Fan, Yixiao Dai, Lingjie Wang, Bang Hu, Wei Cao, Fengzhe Zhang, Fan ZhangAbstract
Spiking Neural Networks (SNNs) have shown great potential for low-power neuromorphic computing due to their event-driven processing and spatiotemporal sparsity, but they remain vulnerable to input perturbations, hardware-intrinsic noise, and adversarial attacks in real-world deployment. We attribute this vulnerability partly to input-specific hidden representations learned by standard SNNs trained with conventional cross-entropy loss, as our information-plane analysis empirically shows that substantial input-related information can still be retained after label-predictive information becomes stable. To address this issue, we propose SCIB, a simple yet effective robust learning objective for SNNs based on the Conditional Information Bottleneck. Different from conventional variational IB methods, SCIB replaces the mutual information compression term $I(X;S)$ with the conditional mutual information $I(X;S|Y)$ and derives a tractable variational upper bound. Moreover, it introduces a learnable class-conditional prior instead of a label-agnostic isotropic Gaussian prior, improving the expressiveness of variational compression and encouraging class-structured representations when combined with the supervised classification objective. Systematic experiments on Fashion-MNIST, CIFAR-10, and CIFAR-100 under external environmental noise, neuromorphic hardware noise, and adversarial attacks show that SCIB improves robustness over standard SNNs and IB-based baselines under most evaluated noise conditions, improves adversarial accuracy under the evaluated attacks without using adversarial training, and reduces the SynOps-based single-sample inference energy consumption by 24.9% relative to an unconstrained SNN baseline.
Abstract (translated)
URL
https://arxiv.org/abs/2610.28110
PDF
-
Learning Tactile-Aware Hand Representations from Human Hand-Object Interaction Data
2026-10-20 10:31:45Yeonguk Yu, Jaehong Kim, Byungok HanAbstract
Tactile representation learning for dexterous manipulation often requires substantial robot-side data collection, limiting its scalability across different hands and sensors. We explore whether large-scale human hand-object interaction (HOI) data can provide a useful prior for robotic tactile learning. To this end, we propose HuTact, a framework that pretrains tactile-aware hand representations from human HOI data without requiring real tactile signals during pretraining. HuTact transfers the interaction structure learned from human hand motions to robotic tactile observations and can be further adapted with a small amount of unlabeled robot interaction data. We evaluate HuTact on tactile perception and manipulation tasks, including grasp prediction, slip detection, object classification, and tactile-conditioned policy learning. Across these tasks, human HOI pretraining improves data efficiency and provides a strong initialization for downstream learning. Notably, with HOI pretraining followed by midtraining on unlabeled robot play data, HuTact achieves 88.26% Macro-F1 on grasp prediction using only 25\% of the labeled downstream data, comparable to training from scratch on the full labeled dataset (88.23%). These results suggest that human interaction data can serve as a scalable complementary source for robotic tactile representation learning.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27995
PDF
-
HypTI: Hyperbolic Geometry-Aware Multimodal Feature Fusion Framework for Dementia Detection Based on Tabular-Image Data
2026-10-20 09:49:08Jianfei Xu, Huizhi Liang, Judith Harrison, John-Paul Taylor, Jaume Bacardit, Varun OjhaAbstract
AI-assisted dementia detection usually requires multimodal fusion of diverse data, such as clinical tabular assessments and neuroimaging data. However, most existing approaches perform feature fusion in Euclidean space. This overlooks the geometric structure of representation, including hierarchical organization in clinical assessments and multi-scale anatomical patterns in neuroimaging data. Recent studies have proved hyperbolic space provides a natural geometry for modelling hierarchical and multi-scale structures due to its exponentially expanding space. Motivated by these, we propose HypTI, a hyperbolic geometry-aware multimodal fusion framework for dementia detection based on clinical tabular assessments and MRI scans. HypTI extends hyperbolic representation learning to an underexplored tabular-MRI setting, jointly modelling clinical tabular, MRI scans, and cross-modal interactions in hyperbolic space. Specifically, clinical tabular variables are represented through learnable Lorentz prototypes to capture hierarchical relationships among assessments, while MRI scans are encoded using ROI-guided multi-scale representation learning to capture anatomical patterns across brain regions and spatial scales in Lorentz space. The multimodal representations are integrated through an adaptive fusion module generates subject-specific scale and shift coefficients to dynamically modulate and integrate the multimodal embeddings, enabling effective patient-level tabular-image representation learning for dementia detection. Experiments on OASIS-3 dataset show that the proposed framework achieves strong performance compared with representative approaches.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27941
PDF
-
EndoWorld-JEPA: Motion-Guided Global-Local Latent World Modeling for Endoscopic Video Representation Learning
2026-10-20 09:11:49Jie Wang, Shilong Yang, Tao Zhou, Juan Yu, Rong Qu, Jianfeng Ren, Zhicheng Zhang, Linlin ShenAbstract
Endoscopic video contains state transitions induced by camera displacement, deformable tissue, and instrument interaction, yet existing pre-training methods treat dynamics implicitly. Inspired by action-conditioned joint-embedding predictive world models and by the observation that endoscopic optical flow contains both shared image-plane displacement and localized residual motion, we reformulate passive endoscopic video pre-training as causal motion-guided latent-state prediction. We present EndoWorld-JEPA, to our knowledge the first latent world-model pre-training framework designed for general-purpose endoscopic video representation learning. In place of recorded actions, calibrated poses, or robot kinematics, the model conditions prediction of the next latent state on motion observed up to the current state. Its Optical Motion-guided module separates dominant displacement from residual motion and converts the resulting evidence into an ordered temporal motion condition and spatial local-motion weights. Complementary global and motion-local predictors use these outputs: the temporal condition modulates both latent transitions, whereas the local weights define an auxiliary motion-local state that extends prediction from scene-level evolution to spatially selective dynamics. Patch-wise motion weighting emphasizes localized change while retaining anatomical context, and past-motion-window dropout randomly withholds the complete motion condition to reduce dependence on unreliable motion. This global--local formulation learns predictable visual--motion dynamics in representation space without reconstructing stochastic appearance artifacts. Evaluation shows that a common pre-trained checkpoint achieves strong transfer across four downstream benchmarks, supporting motion-guided global--local latent prediction as an effective pre-training paradigm for endoscopic video.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27890
PDF
-
Decoupled Representation Learning for Face Presentation Attack Detection under Smart Glass Capture
2026-10-20 07:59:40Raghavendra Ramachandra, Narayan Vetrekar, Neha Naik, Rajendra GadAbstract
Face presentation attack detection is typically studied under fixed camera or handheld acquisition, whereas smart glass capture introduces a more challenging setting with ego motion, changing viewpoint, background clutter, distance variation, and less controlled illumination. To address this challenge, we present \emph{SmaGla-FPAD}, a smart glass based face presentation attack database acquired using Ray-Ban Meta Wayfarer Smart Glasses across indoor, outdoor, and in-car settings. The database contains 297,000 sample images with both bona fide and silicone mask based attacks. We further propose \emph{DeCoupPAD-FAS++}, a decoupled representation learning framework that separates spoof related evidence from nuisance sensitive capture variation through complementary attack and style branches, pseudo-domain supervision, adversarial suppression, and prompt guided semantic alignment. Experiments on SmaGla-FPAD show that the proposed method achieves the best balance between attack rejection and bona fide acceptance in both in domain and cross scenario evaluation. Ablation results further confirm the contribution of disentanglement, pseudo-domain supervision, and prompt based regularisation under smart glass capture.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27800
PDF
-
PanoGraphGS: Unifying Panoptic Segmentation and Hierarchical 3D Scene Graphs with Gaussian Splatting
2026-10-20 06:15:59Jinghan Gao, Siyu Wang, Dongyu Feng, Yiyuan Pan, Rong XiongAbstract
3D scene graphs support language-guided navigation by organizing physical entities and their containment and spatial relations into hierarchical maps. Building such structured maps requires a scene representation that consistently distinguishes semantic regions and object instances across viewpoints. To this end, we present PanoGraphGS, a unified framework that integrates panoptic 3D Gaussian representation learning with hierarchical 3D scene graph construction. The framework learns semantic and instance features on shared Gaussian geometry. Prototype-based semantic learning and association-based instance learning produce consistent panoptic predictions across viewpoints. The learned representation supports novel-view panoptic segmentation and direct Gaussian-level panoptic decoding. A semantic-aware scene graph module further recovers a floor-room-stuff-object hierarchy with containment and spatial relations. We further release NaviGS, a real-world dataset for panoramic scene understanding. The dataset provides 3DGS, mesh, RGB, semantic, and depth data. For 3D scene graph identification, PanoGraphGS outperforms the strongest evaluated baseline in object-node accuracy by more than 50\% points on average across Replica and NaviGS. PanoGraphGS also achieves state-of-the-art performance among the evaluated methods in 2D panoptic segmentation. These results demonstrate the effectiveness of PanoGraphGS in both hierarchical 3D scene understanding and 2D panoptic segmentation. The project website is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27696
PDF
-
Learning Inter-Graph Interactions Between Heterogeneous Substructures of Chemical Systems
Abstract
Complex chemical systems containing heterogeneous substructures are common in real-world applications. Various physical phenomena of the complex chemical systems are derived from the interactions between the heterogeneous substructures. However, existing graph representation learning methods for inter-graph interactions assumed graph-level interactions between homogeneous structures, such as organic molecules and inorganic crystalline materials. We propose a data descriptor of the complex chemical systems and a graph neural network for learning inter-graph interactions between organic and inorganic compounds. We applied the proposed method to predict the physical properties of hybrid solar cell materials containing heterogeneous substructures, which have received significant attention for sustainable energy resources. By learning heterogeneous inter-graph interactions, the proposed method achieved state-of-the-art accuracy in predicting band gaps of 1,682 hybrid solar cell materials.
Abstract (translated)
URL
https://arxiv.org/abs/2610.27614
PDF
