Segmentation
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Can machines reason non-verbally?
2026-10-09 17:54:36Shaheer U. Saeed, Zion Tse, Gregory Slabaugh, Daniel C. Alexander, Yipeng HuAbstract
Modern machines can solve equations, follow logic and reason through language, yet they still struggle with problems that require visual non-verbal reasoning. This is evident even in advanced systems such as DeepSeek, ChatGPT and Gemini, which remain limited on visual tasks such as completing abstract patterns or delineating ambiguous regions-of-interest. Unlike explicit intermediate steps in verbal reasoning which can be verified through logic or automated tests, visual reasoning involves implicit intermediate steps with no clear methods for verification. Non-verbal reasoning is therefore challenging to model and, as a result, remains a fundamental limitation of current machine intelligence. Here we introduce GenVerOpt, a general framework that unifies generation, verification and optimisation into a reasoning pipeline for machines. By generating candidate solutions, verifying their correctness, and iteratively refining them, GenVerOpt enables per-sample optimisation rather than relying solely on dataset-level trends captured during model training. This process mirrors human problem-solving where multiple possibilities are imagined, evaluated, and refined until a correct solution emerges. Across diverse domains, GenVerOpt demonstrates capabilities beyond existing systems: it surpasses humans on abstract IQ puzzles, matches clinicians in prostate cancer segmentation, exceeds experts in liver vessel reconstruction and outperforms state-of-the-art approaches in tumour boundary alignment. These results show that machine reasoning can extend beyond language, establishing visual non-verbal reasoning as a foundation for more flexible and generalisable intelligence.
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URL
https://arxiv.org/abs/2610.13925
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zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss
2026-10-09 13:52:54Saibo Geng, Andrea Gentilini, Xinxian Ma, Nathan Ranchin, Yunzhen Yao, Robert WestAbstract
Dynamic vocabularies built on-the-fly via Lempel-Ziv-Welch (LZW) compression let a language model represent the same text in fewer tokens, but prior work shows that this efficiency comes at a cost to reasoning and perplexity. We show that this quality gap is not a fundamental price of compression but can be substantially closed through targeted improvements to the compression layer and LZW codec, without modifying the underlying pretrained language model. Our analysis reveals four key issues: (1) LZW's compression algorithm creates an implicit conflict between the input embedding and the output embedding, a tension that a single shared embedding cannot resolve; (2) naively applying RoPE to LZW-compressed sequences ignores that merged tokens can span multiple tokens; (3) merging certain tokens (notably digits) disproportionately hurts downstream accuracy; and (4) dynamic vocabularies introduce an inherent segmentation ambiguity that confounds standard evaluation. We address these through decoupled input/output dynamic embedding layer, size-aware RoPE positioning, and a modified LZW codec supporting selective merges and multi-view segmentations. Across four models, our recipe improves perplexity on every corpus we evaluated as well as mathematical reasoning accuracy, closing roughly half the quality gap between zip2zip and the uncompressed baseline at a similar compression ratio on general text. This represents a significant step toward lossless adaptive-vocabulary compression for language models.
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URL
https://arxiv.org/abs/2610.13605
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Thyroid Nodule Segmentation in Ultrasound Images with Conformal Uncertainty Estimation
2026-10-09 13:51:06Francesco Teodori, Pierpaolo Brutti, Umberto Ferraro PetrilloAbstract
Thyroid nodules affect a large proportion of the general population. Although most are benign, a non-negligible fraction is malignant. Ultrasound imaging is the primary diagnostic modality, and accurate nodule segmentation is a prerequisite for CAD systems to classify malignancy and assess metastatic risk.\\ In this work, we propose a thyroid nodule segmentation framework that integrates a DeepLabV3+, channel-attention modules and post-hoc conformal prediction, providing both accurate mask and pixel-level uncertainty estimates. We address thyroid ultrasound nodule segmentation as the first stage of a pipeline that also includes malignancy classification and metastatic risk prediction. We adopt DeepLabV3+, whose Atrous Spatial Pyramid Pooling (ASPP) captures multi-scale context, with an EfficientNet-B5 backbone for its accuracy-efficiency trade-off. Additionally, we test three channel-attention modules: Squeeze-and-Excitation, CBAM and Grouped Channel Attention (GCA), paired with two loss formulations (Dice-BCE and Boundary).\\ Since binary masks do not indicate prediction confidence, we wrap the trained model with a split conformal prediction layer that produces pixel-level uncertainty maps with statistical coverage guarantees.\\ We evaluate all configurations on the TN3K public benchmark using a fixed split and directly compare them with state-of-the-art methods. The plain backbone already improves upon previously reported results, with CBAM + Dice-BCE emerging as the best overall configuration.
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URL
https://arxiv.org/abs/2610.13604
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A Real-World CAD Segmentation Benchmark: Comparing Rule-Based and Learning-Based Approaches through Hole Detection
2026-10-09 12:03:15Kai Sch\"utte, Edgar Heinert, Matthias RottmannAbstract
CAD segmentation datasets are predominantly synthetic or automatically labelled, leaving it unclear how well existing methods transfer to real-world engineering tasks. Fastener-relevant hole detection represents one such task, requiring human annotation and exhibiting greater geometric complexity than commonly used benchmarks. We address this gap through three main contributions. First, we develop two task-specific rule-based methods, one graph-wire-search and one multi-view based, and complement them with a human-annotated benchmark dataset derived from engineering CAD data that also enables training of UV-Net and AAGNet, representative learning-based approaches for B-Rep segmentation. Second, we conduct an extensive experimental study including parameter optimisation, evaluation on our benchmark dataset, cross-domain generalisation on a synthetic dataset, and an assessment of the feasibility of the approaches at varying dataset sizes and IoU thresholds. Third, we provide evidence-based insights into the strengths and limitations of different approaches: rule-based methods generalise well without training data, with the graph-wire-search method favouring recall and the multi-view method favouring precision, whereas learning-based methods achieve strong performance with sufficient annotations but exhibit reduced robustness under domain shift. These findings highlight trade-offs between data requirements, and robustness, and provide guidance not only for hole detection, but more broadly for real-world CAD segmentation tasks.
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URL
https://arxiv.org/abs/2610.13466
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SADL: A Size-Adaptive Decision Layer for Chronic Stroke Lesion Segmentation in ISLES 2026
2026-10-09 11:28:58Marek Wodzinski, Gniewosz Drwiega, Wojciech SzymanskiAbstract
Chronic ischemic stroke lesions are difficult to segment on T1-weighted MRI. Their appearance overlaps with healthy tissue, and their volumes span four orders of magnitude, so voxel-level and lesion level metrics often disagree. We build an eleven-member nnU-Net ensemble that covers five design axes: a baseline dice + top-K loss, heavy augmentation, per-lesion volume loss re-weighting, stochastic weight averaging, and a sampling-and-architecture block that pairs cohort-balanced sampling with a residual-encoder backbone. Weighted soft-map fusion of the members feeds a tuned decision layer, SADL, that estimates lesion volume, applies an adaptive per-lesion-size threshold, prunes components by voxel count and peak probability, and adds a never-empty rescue. All fusion weights and per-bucket parameters are fitted on out-of-fold (OOF) predictions, so tuning never touches the held-out test set. On a site-disjoint 91-case holdout, SADL reaches Dice 0.7322, lesion-F1 0.7461 and PR-AUC 0.8430 when scored online with the challenge's released evaluation code. A paired non-parametric bootstrap against a reference single ensemble member confirms the lesion-F1 gain at p=0.024, and the gain is largest in the small-lesion regime, where the rescue step recovers cases that a fixed threshold would silently discard.
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URL
https://arxiv.org/abs/2610.13422
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Towards the Roof of the World: NLP Progress in Tibetan Languages
2026-10-09 11:11:10Xi Cao, Yijie Li, Yuan Sun, Shiwei Dong, Zeguang Huang, Nuo QunAbstract
Tibetan, spoken by some seven million people on and around the Qinghai-Xizang Plateau, remains underrepresented in natural language processing (NLP). We present, to our knowledge, the first peer-reviewed systematic bibliometric survey of Tibetan NLP: 140 publications from 2004 to 2026 in the ACL Anthology and DBLP, annotated for venue type, research area, first-affiliation country, and resource release. Output has grown rapidly, with 84% of the publications appearing since 2018, and has expanded bottom-up along the NLP pipeline, from corpora and word segmentation through script, speech, and applications to large language models (LLMs). Researchers based in China first-authored 87% of the publications, which span all 25 represented research areas, whereas work from other countries concentrates on Classical Tibetan. We argue that this full-pipeline paradigm, realized under the compounded constraints of a complex script, dialectal diversity, and geographic remoteness, offers a reference model for other low-resource languages of the Global South. We also identify gaps in dialect coverage, responsible NLP, native and comparable evaluation, cross-community visibility, and resource release (only 29% of the publications release data, code, or models), and recommend open science, shared standards, and community participation for the LLM era.
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URL
https://arxiv.org/abs/2610.13398
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A4-MR: Abnormality-aware Anatomical Alignment for MR
2026-10-09 06:10:10Noel D'souza, Zhijian Yang, Nishank Singla, Pranay Doshi, Krisztian Koos, Fei Tan, Bruno Nunes, Laszlo Rusko, Bernadett Kolozsvari, Eszter Sabo, Emanuele Valeriano, Vikash Gupta, Seyed Iman Zare Estakhraji, Marc Lebel, Farzin Haddadpour, Taha Kass-Hout, Erhan BasAbstract
Recent advances in large-scale foundation models have enabled strong generalization across a wide range of clinical and research tasks in magnetic resonance imaging (MRI). However, existing vision-language MRI models remain limited in anatomy-specific localization and pathology-aware representation, particularly in real-world settings. We introduce A4-MR, a 3D vision-language foundation model trained on approximately 50,000 MRI studies comprising 491,000 series, roughly half of which are paired with radiology reports. A4-MR is designed to: (1) learn anatomy localization through large-scale pretraining with noisy labels; (2) align image and text representations at the anatomical level; and (3) learn distinct representations of healthy and abnormal anatomy. Across a diverse set of downstream tasks, A4-MR outperforms existing foundation models, including a +1.7 AUROC improvement over MedSigLIP for abnormality detection. Using a frozen encoder, A4-MR also surpasses an end-to-end task-specific MedNeXt model for knee segmentation by +1.4 Dice and performs comparably to the best task-specific model for abdominal organ segmentation on AMOS, within 0.4 Dice. Finally, without anatomical prompts, A4-MR localizes 3D anatomy comparably to MedSAM2 provided with ground-truth bounding boxes. Together, these results demonstrate that anatomy-level multimodal alignment and abnormality-aware representation learning can enable a single MRI foundation model to support localization, segmentation, and pathology-related tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2610.13056
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Learning Features, Not Layouts: Feature-Axis Attention as a Transfer Prior for Medical Imaging
2026-10-09 05:16:26Aon Safdar, Mohamed SaadeldinAbstract
Natural-image-pretrained foundation models are increasingly adapted to medical imaging, yet transfer remains unreliable under domain shift, modality heterogeneity, and label scarcity. We trace part of this fragility to an overlooked design choice. Standard vision transformers place all global modeling on self-attention over spatial tokens, an object-layout prior well suited to natural scenes but not to medical images, where the discriminative signal lies in feature correlations and texture that recur across scanners while layout does not. We ask whether the axis of global attention, spatial versus feature, is itself a lever for transferable medical representations. Existing channel-aware hybrids confound this question, adding channel attention to a spatially-mixed backbone and leaving the axes coupled and the feature axis unmeasurable in isolation. We design FAD-ViT, a backbone that strictly decouples the two axes, making feature-axis attention the primary global operator and the attention axis a controlled variable. Evaluating comprehensively across 23 classification, segmentation, and low-data tasks under matched tokenization, capacity, and protocol, we find that shifting global attention from the spatial to the feature axis leaves from-scratch performance essentially unchanged, yet markedly strengthens transfer. Identical ImageNet pretraining yields up to roughly three times the transfer gain, with consistent improvements on every held-out segmentation task at up to a quarter the parameter count and far lower compute than standard and channel-attention baselines. The advantage is largest where texture rather than geometry is discriminative, tracking greater reuse of pretrained features. The axis of global attention is thus a design lever for medical transfer, and making it channel-primary is a simple, cheap, and principled choice.
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URL
https://arxiv.org/abs/2610.13012
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JMT-YOLO: Multi-task Domain Adaptation via Segmentation-Guided Consistency Learning
2026-10-09 02:47:52Zhenhua Yu, Jiahao Zhang, Wenchao Zhang, Ou Ye, Hengxiang ZhaoAbstract
Domain adaptive object detection (DAOD) aims to reduce performance degradation due to distribution shifts between training (source) and testing (target) datasets. However, owing to insufficient modeling of fine-grained features and limited instance-level alignment, existing approaches become less effective under complex environmental conditions, such as extreme weather conditions and object occlusion. We overcome these challenges by proposing JMT-YOLO, a novel joint multi-task learning framework for DAOD. JMT-YOLO is based on the Mean Teacher architecture. It introduces an auxiliary semantic segmentation task and a masking mechanism to enhance the modeling of foreground-background semantic boundaries in the target domain. Specifically, fine-grained feature degradation is reduced by introducing a multiscale context-aware segmentation head equipped with a dense connection structure to effectively capture local and global contextual features. A mask-consistency loss is formulated by applying random masking operations to target images, thereby guiding the network to learn structural context and improving instance-level feature alignment. Extensive experiments on three benchmark domain adaptation tasks (Pascal VOC to Clipart1k, Cityscapes to Foggy Cityscapes, and KITTI to Cityscapes) demonstrate that JMT-YOLO achieves detection performances of 46.9%, 57.3%, and 61.0%, respectively. These results demonstrate superior performance over existing state-of-the-art and baseline methods, thereby validating the effectiveness and generalization of JMT-YOLO.
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URL
https://arxiv.org/abs/2610.12901
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AD-SRF: Attack-Defense Semantic Reinforcement for Multi-Modal Image Fusion
2026-10-09 02:38:15Hao Zhang, Zhiqian Zheng, Linfeng Tang, Xinyu Xiang, Jiayi MaAbstract
Existing image fusion methods struggle to balance visual quality and semantic attributes due to the inherent conflict between their optimization objectives. To address this challenge, we propose an attack-defense semantic reinforcement framework for multi-modal image fusion, termed AD-SRF. By decoupling the optimization of these two aspects and then losslessly reintegrating them, this framework injects rich semantic attributes into the fused image without compromising its visual quality. First, we develop a transformer-based restorative visual fusion network as a strong visual baseline. Equipped with the cross-modal retrieval module and the information restoration-fusion collaborative loss, it produces highly visible fused images even under degraded conditions. Second, we introduce an attack-defense game mechanism to strengthen the semantic attributes of the fused images. The attacker learns perturbation patterns that impair downstream segmentation, whereas the defender restores missing semantics from the original inputs and reintegrates them into the fused image through an imperceptible semantic map. Through iterative adversarial interaction, this mechanism drives the defender to continually strengthen the fused image's semantic attributes, boosting downstream segmentation performance without compromising visual quality. Extensive experiments demonstrate that AD-SRF achieves a win-win performance in both visual quality and semantic attributes. The code is publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2610.12897
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ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies
2026-10-09 02:34:53Zhe Tao, Feiran Wang, Gaowen Liu, Ramana Rao Kompella, Yan YanAbstract
Robots need to anticipate how their actions will change the world, since manipulation success hinges on the resulting contacts and object motions. However, existing Vision-Language-Action (VLA) policies that predict future observations from shared features leave the forecast decoupled from the actions the policy will actually execute, and impose no physical constraints on how the scene may evolve. We introduce ForeAct3D, a framework for policy-grounded future world modeling within VLA policies. Learnable geometric queries decode depth, semantic segmentation, and camera pose from the policy representation into current and future semantic 3D scene states, and the future queries are conditioned on the policy-generated action chunk to ground the forecast in the planned interaction. A physical-consistency closure relates the two states through background staticity and instance-level rigidity, and anchors the wrist-camera pose to end-effector kinematics. These objectives shape the shared representation used for action generation during training, and no future prediction is required at inference. Without robot pretraining, ForeAct3D achieves 98.3% average success on LIBERO and an average task length of 3.73 on CALVIN, outperforming its base policy on every suite. Ablations show that semantic 3D supervision, physical consistency, and action conditioning each improve manipulation performance, and that action conditioning substantially improves future object localization. Real-world experiments on spatial placement, object insertion, and sequential manipulation further raise average success from 6.7% to 37.8% over the base policy. The project page and code are available at this https URL.
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URL
https://arxiv.org/abs/2610.04607
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Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration
2026-10-09 02:22:40Hao Zhang, Xuhui Zuo, Jiayi MaAbstract
Existing multi-modal image fusion (MMIF) methods generally face an optimization gap between the visual and semantic domains, limiting the enhancement of perception capabilities. To overcome this long-standing barrier, this work proposes W2F, a new visual-semantic mutual reinforcing paradigm for MMIF. First, we reinforce the foundation for visual-semantic collaboration by amplifying feature complementarity through two targeted designs. Specifically, the segmentation-guided fusion loss introduces object-aware structural guidance into visual fusion, while the embedded information restoration prior strengthens the ability of visual fusion to provide reliable perceptual evidence for the semantic branch under degraded conditions. Then, we introduce a strong multi-modal information interaction mechanism composed of interactive gated mixed attention (IGM-Att) and progressive coupling attention (PC-Att). These modules ensure comprehensive scene representation from both local and global perspectives, providing informational support for visual-semantic collaboration. Finally, we construct a collaborative mechanism to couple visual fusion and semantic fusion. It employs a feature search module to extract complementary key features from both tasks, enriching the representational potential of visual and semantic information. Meanwhile, a gradient correction strategy ensures collaboration from the optimization perspective, enhancing performance for both visual and semantic fusion through consistency learning. Extensive experiments demonstrate that W2F surpasses state-of-the-art methods in both visual appeal and decision accuracy. The code is publicly available at this https URL.
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URL
https://arxiv.org/abs/2610.12887
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Domain Adaptation of SAM 2.1 for Building-Component Segmentation in Image-Based 3D Reconstruction
2026-10-08 22:43:47Ehsan Agha Ebrahimi, Yong-Cheol Lee, Keith G. MillsAbstract
3D reconstruction of building components using image-based scanning requires appropriate segmentation of the objects in the 2D building images. In order to benefit from consistent grouping of the 2D masks in 3D environments, vision foundation models such as the Segment Anything Model have been utilized by Group Anything with Radiance Fields (GARField) to perform 3D segmentation. This study fine-tuned SAM 2.1 on the HBD dataset, consisting of 2,679 annotated images from 42 buildings, to enhance multi-class segmentation of 2D building images, covering 11 classes (Wall, Window, Ceiling, Door, Column, Beam, Floor, Opening, Curtain Wall, Roof, and Lift). The model was evaluated using per-class IoU, Dice, and mIoU metrics, and the results show an improvement in mIoU from 49.9% to 69.6%, indicating a meaningful improvement in class segmentation and providing a more accurate model for distilling the masks in 3D. SegFormer was then used as a baseline, generating per-class masks for comparison with SAM 2.1, which indicates improvement in some object classes with larger surface area. The masks in the building images were grouped in the neural radiance field, and the pretrained SAM 2.1 was compared with the fine-tuned SAM 2.1 in the newly rendered images. This comparison shows that fine-tuning improves the grouping of building components at a user-selected grouping scale. This advances the application of Group Anything with Radiance Fields (GARField) in 3D, enabling better segmentation of building structures for 3D auto-reconstruction.
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URL
https://arxiv.org/abs/2610.12786
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Shift-Aware Identity-Guided Latent Refinement for Referring Audio--Visual Segmentation
2026-10-08 22:39:41Kun Li, Sami Sebastian Brandt, Michael Ying YangAbstract
Referring Audio--Visual Segmentation (Ref-AVS) is an emergent multimodal task requiring the precise identification and segmentation of a specific object based on a natural language expression grounded in both visual and auditory cues. Unlike traditional AVS, which segments generic sound sources, or referring video object segmentation (Ref-VOS), which relies solely on visual--textual grounding, Ref-AVS necessitates fine-grained tri-modal interaction across vision, audio, and language. However, existing methods often fail to maintain consistency when subjected to target shift, a phenomenon where the model erroneously transfers its focus between objects due to transient occlusions, acoustic fading, or identity ambiguities. To address this, we propose a novel Shift-Aware Identity-guided Latent (SAIL) refinement framework. It introduces a dual-token strategy that decouples frame-level dynamics from a global semantic anchor to explicitly model target-shift cues. We design a shift-aware refinement module that rectifies drift by aligning tokens with domain-specific evidence. Finally, we leverage a decoder that integrates the refined embeddings with a continuous memory update to maintain spatio-temporal coherence and identity consistency throughout the video sequence. Extensive experiments conducted on two Ref-AVS benchmarks demonstrate the effectiveness of our proposed method, significantly outperforming existing AVS, Ref-VOS, and Ref-AVS approaches. The code will be released after publication.
Abstract (translated)
URL
https://arxiv.org/abs/2610.12783
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Reference-Free Food Volume Estimation from Egocentric Stereo Video
2026-10-08 21:30:38Vivan Kumar, Gautham Vinod, Fengqing ZhuAbstract
Current stereo vision-based approaches for food portion estimation are hindered by the user burden of using a mobile phone to capture multiple images or placing a reference object in the scene for scale calibration. As a step towards passive dietary intake monitoring, we introduce a method that uses stereo video from a wearable egocentric device to reconstruct food items in true physical scale to accurately estimate volume. By utilizing FoundationStereo for zero-shot stereo matching, SAM 2 for food segmentation, and TSDF fusion over a set of keyframes, our method yields a visually and metrically accurate 3D mesh of any plated food item from a single stereo recording. We leverage the unique offerings of wearables, such as a factory-calibrated stereo baseline and camera poses estimated on-device via visual-inertial odometry (VIO), to perform fully automatic reconstruction without a scale reference object in the scene. We show strong initial volume estimation results across five recorded food items, achieving a mean absolute percentage error of 9.1%.
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URL
https://arxiv.org/abs/2610.12735
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Lightweight Systems for Spatial Semantic Segmentation of Sound Scenes
2026-10-08 20:19:46Mayank Mishra, Paul Magron, Romain SerizelAbstract
Spatial semantic segmentation of sound scenes (S5) jointly performs audio source separation and tagging on a multichannel audio mixture. Therefore, an S5 system can be decomposed into an audio tagging model and a separation model. The baseline S5 system in task~4 of the DCASE 2025 challenge uses M2D and ResUNet models for tagging and separation, respectively. These are large in overall size and therefore not appropriate for use in embedded devices. The main aim of this paper is to assess the performance loss in an S5 system when using lightweight tagging and separation models instead. To this end, we first compare the tagging performance of lightweight audio tagging models, namely PANNs and DyMNs, in terms of label prediction accuracy against the baseline M2D model. Secondly, we assess separation performance in terms of the scale-invariant signal-to-distortion ratio of the baseline ResUNet separator relative to that of a convolutional-based separator using SuDoRMRF. Finally, we combine the tagging and separation models across various configurations and assess the overall performance of the S5 system using the class-aware signal-to-distortion ratio.
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URL
https://arxiv.org/abs/2610.12693
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One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts
2026-10-08 17:58:36Adrian Bulat, Yassine Ouali, Georgios TzimiropoulosAbstract
In this work, we show that a single Transformer block, applied recurrently, can match the accuracy of a full-depth vision encoder at comparable inference FLOPs without intermediate feature distillation. reViT restores depth-specific transformations by representing the FFN at each recurrent depth as a convex combination of a small shared expert bank. A continuous normalized-depth coordinate programs this mixture, defining a resampleable trajectory through FFN parameter space. We evaluate this design in two regimes: supervised ImageNet-1k training and distillation from a DINOv2 teacher. Across both regimes, controlled adaptations identify weight-space merging as the strongest tested MoE family at a matching one-FFN budget, ahead of the token-dispatch and output-mixture alternatives. Trained from scratch, reViT-B/16 attains DeiT III accuracy with about 70\% fewer stored parameters. An 8-experts model distilled using only the teacher's output features retains nearly all of its DINOv2 teacher's linear-probe accuracy and transfers across classification, segmentation, and depth prediction. Elastic-depth training allows one checkpoint (trained model) to operate at multiple tested depths by resampling the same normalized coordinate interval. For fixed-depth deployment, the recurrent block can be materialized as a conventional dense graph, removing online routing and merging without changing the one-FFN-per-depth compute but expanding deployment storage.
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URL
https://arxiv.org/abs/2610.12448
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Stride Independent Patching for Deep Learning
Abstract
This paper presents semi-automatic stride-independent patching (SSP) as an alternative to automatic stride-dependent patching techniques. SSP uses user or expert input to position predefined patches over one or more objects of interest. To evaluate its effectiveness, three patch-based datasets were created using SSP, overlapping patching (Overlap), and non-overlapping patching (Noverlap). DeepLabV3+ models with ResNet50, ResNet18, and MobileNetV2 backbones were trained sepa-rately on each dataset. Quantitative evaluations were performed on the respective test splits and a common external test set. SSP generally achieved higher segmentation scores on the test splits and required the shortest model training time across all three backbones. On the external test set, SSP achieved the highest average precision and F1-score across backbones, whereas Noverlap achieved the highest average recall. These preliminary results demonstrate that the potentially greater spatial coverage of Noverlap and Overlap does not generally translate into better segmentation perfor-mance and that SSP offers a favorable balance between segmentation performance and model training time.
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URL
https://arxiv.org/abs/2610.12216
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ContourVLA: A Closed-Loop Perception-Action Contour Policy for Generalized Referring Expression Segmentation
2026-10-08 15:06:00Ruicheng Zhang, Kaiwen Shen, Jiaqi Hou, Shuhan Yang, Junchao Huang, Kewei Zhang, Jun Zhou, Li Jiang, Shen ZhaoAbstract
Generalized referring expression segmentation (GRES) requires dynamically balancing high-level semantics for identifying a variable number of language-specified referents with fine-grained visual evidence for precise boundary delineation. This requirement challenges existing cascaded vision-language architectures, which typically rely on static feature interfaces and single-pass mask prediction, limiting adaptive perception and geometric correction. We introduce ContourVLA, a vision-language-action policy that recasts GRES as a closed-loop visuomotor process, in which editable contours serve as explicit policy states that condition multimodal perception and are updated by geometric action chunks. Evolution-Aware Semantic Scheduling (EASS) couples contour-guided bidirectional boundary sampling with state-conditioned routing of multilevel multimodal features, adapting perception to each contour state. Following supervised initialization, Dustbin-Augmented Entropic Credit Transport GRPO (DECT-GRPO) jointly optimizes discrete grounding and continuous contour actions with instance-level credits. Its rollout rewards and credits are derived from soft prediction-target correspondences that account for false positives and missed targets. ContourVLA improves gIoU over the strongest evaluated baselines by 8.7, 2.8, and 2.7 points on gRefCOCO val, testA, and testB, respectively, and achieves the highest mIoU across all eight RefCOCO, RefCOCO+, and RefCOCOg splits.
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URL
https://arxiv.org/abs/2610.12107
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SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills
2026-10-08 14:33:48Ryosei Tamura, Xiaoxiang Dong, Uksang Yoo, Yuemin Mao, Romina Mir, Jonathan Francis, Jeffrey IchnowskiAbstract
Dexterous manipulation requires both large-scale task progression and precise contact-rich interaction, making it challenging to collect demonstrations that effectively support both regimes. We present SkillWeave, a heterogeneous demonstration framework for long-horizon dexterous manipulation that combines teleoperation for coarse reaching and transport with kinesthetic teaching for precise, contact-rich skills. To address the visual mismatch introduced by the demonstrator's presence during kinesthetic data collection, we propose an object-mask-conditioned diffusion policy that uses offline object segmentation for training supervision and a lightweight learned mask predictor at deployment, avoiding online segmentation and image inpainting. To mitigate distribution shift between independently trained sub-task policies, we introduce successor-aware terminal steering, which selects among actions sampled from the predecessor policy to guide the system toward states supported by the successor's demonstrated initial-state distribution. Across three real-world long-horizon tasks, SkillWeave achieves 27% average end-to-end success. Mask-conditioned kinesthetic policies improve dexterous sub-task success to an average of 65%, while successor-aware handoffs achieve an average composition efficiency of 87%. These results show that matching demonstration modality to interaction regime, explicitly addressing kinesthetic visual mismatch, and steering policy handoffs toward successor-supported states substantially improves long-horizon dexterous manipulation. Videos and code are available at this http URL .
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URL
https://arxiv.org/abs/2610.12046
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