arXiv_CV
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EVAThink: Evidence Sufficiency-Guided Training for Adaptive Thinking in Video Understanding
2026-11-10 11:01:57Changti Wu, Ruizhe Fan, Bin Wang, Lei Zhang, Yuxiang ZhangAbstract
Under a fixed frame budget, video understanding with vision-language models (VLMs) depends on whether the sampled frames provide sufficient evidence for the question. Sufficient frame evidence supports direct answering, whereas less informative observations may benefit from reasoning that connects the available cues. We introduce EVAThink, which enhances video understanding through evidence sufficiency-guided training for adaptive thinking. First, Recoverable Evidence Gap Mining constructs evidence-dense and evidence-sparse video views that share the same question and answer under an identical frame budget. It retains pairs where sparse views yield lower direct-answer accuracy than dense views but benefit from explicit reasoning, providing supervision for response-mode selection. Second, Evidence-Guided Two-Stage Reinforcement Learning uses stage-specific rewards to guide training with evidence-based mode preferences and then refine response efficiency, while first-token advantage regularization mitigates mode collapse. Trained on only 6.7K curated samples, EVAThink consistently improves upon the base model across video understanding benchmarks under both 32- and 64-frame budgets, while outperforming strong baselines on average. The code, model, and datasets are available at \href{this https URL}{EVAThink}.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13907
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Persistent Metric-Semantic Maritime Scene Memory from Open-Vocabulary 3D Scene Graphs
2026-11-10 10:59:07Kim Alexander Christensen, Dina Zimmermann, Martin Steinert, Eleni KelasidiAbstract
Three-dimensional scene graphs (3DSGs) can provide robots with persistent metric-semantic representations for spatial reasoning, but the existing methods have primarily been developed for indoor and terrestrial environments. In this paper we investigate how a representative outdoor open-vocabulary method transfers to maritime environments and which adaptations are needed. Our experiments show that zero-shot representative outdoor 3DSG descriptions agree with our labeled ground truth for a majority of geometry-supported objects on our data, but the resulting map exhibits considerable physical-entity fragmentation, and its terrestrial higher-level assumptions don't capture maritime spatial organization needs. We therefore keep the underlying mapping method and post-process its accumulated evidence. We show a metric-semantic consolidation method followed by geometry-grounded multi-view vision-language model (VLM) reasoning that reconciles remaining fragments into physical entities and groups entities through observations into local places. In parallel, we construct maritime macro regions using map priors and onboard water and structure observations, and associate them with the local places. The resulting representation reduces physical-entity fragmentation beyond both the baseline and our geometry-semantic consolidation component alone, supports correspondence of fixed local places across repeated maritime traversals, and improves contextual retrieval on our reviewed queries over tested baselines. These results show how an outdoor open-vocabulary map can be adapted into persistent maritime scene memory with our methods for object/place identity and spatial abstractions.
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URL
https://arxiv.org/abs/2611.13903
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A 10K drone image dataset with component-level annotations for rooftop photovoltaic inspection
2026-11-10 10:52:27Penglei Zhang, Wan Zhang, Ruizhe Deng, Bo Chen, Wenhao Duan, Jinhe Zhang, Yanzhen Zhang, Qi Chen, Zhiling Guo, Haoran Zhang, Yuntian ChenAbstract
Component-level information supports rooftop photovoltaic (PV) inspection, but recognizing individual components across complex scenes remains challenging. We present PVIS-10K, a dataset of 10,065 drone images with component-level annotations covering 63,632 physical PV modules across 22 rooftop installations. The high-resolution red-green-blue (RGB) images were acquired by unmanned aerial vehicles (UAVs, also known as drones) in Ningbo, Shaoxing and Shenzhen, China. Multi-angle oblique observations capture installation-height and scale variation, dense arrangements, occlusion, shadows and reflections. Three-dimensional delineation, reprojection and image-by-image review produced 3,516,675 image-level polygon annotations, stored in Common Objects in Context (COCO) format. Station-disjoint partitions support evaluation on unseen stations within and across cities. Four supervised models and text-prompted zero-shot Segment Anything Model 3 (SAM 3) provide full-resolution benchmarks of instance matching and foreground coverage. PVIS-10K supports component localization, delineation and image-based counting for drone-based rooftop PV inspection research.
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URL
https://arxiv.org/abs/2611.13892
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DroneSegEdit: Adapting Pretrained Generative Editors for UAV Segmentation with Language-Grounded Verification
2026-11-10 10:50:35Dehua Chen, Long Xin, Jiangtao WeiAbstract
Recent work has shown that pretrained image generators can support diverse visual tasks. Yet directly applying them to language-guided unmanned aerial vehicle (UAV) segmentation exposes a key limitation: a plausible mask may correspond to the wrong instance among small and repeated objects. We call this mismatch between mask rendering and expression-level target selection the Grounding-Rendering Gap. We present DroneSegEdit, which combines RGB proposal rendering with language-grounded, proposal-constrained verification to select supported foreground while retaining rendered geometry. DroneSegEdit adapts the open-weight LongCat-Image-Edit model using task-specific LoRA and fixed color-to-label decoding. For referring segmentation, a Language-Grounded Spatial Verifier (LGSV) reads hierarchical states from the frozen multimodal large language model backbone to derive expression-conditioned spatial evidence, which is mapped back to the rendered proposal without introducing foreground outside it. On RIS-LAD, DroneSegEdit reaches a new state of the art at 52.78 mIoU, 7.16 points above the previous best. It remains competitive overall on UAVid-RIS and VDD-RIS, with its clearest class-wise gains on compact, application-relevant categories, notably vehicles and humans. A separate verifier-free experiment extends the RGB interface to full-scene parsing: a task-specific adapter jointly renders all classes from one task-level instruction without enumerating class names or palette colors.
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URL
https://arxiv.org/abs/2611.13890
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The BRIDGE dataset: paired brightfield whole-slide images of prostate core-needle biopsies in paraffin, after deparaffinization and H&E staining
2026-11-10 10:40:09Julia A. Mielcarz, Justyna Dur\'slewicz, Marek Zdrenka, Ahmad Abbadi, Martin Eklund, Mateusz Matusiak, {\L}ukasz Szylberg, Andrea CamilloniAbstract
Computational approaches are increasingly being explored to streamline histopathology workflows, including label-free methods that aim to reduce reliance on chemical staining. Among these, deep learning-based label-free virtual staining uses images of unstained tissue to computationally generate stained images. Despite growing interest, model development and validation remain constrained by the scarcity of paired, clinically annotated whole-slide imaging datasets. We present BRIDGE, a dataset of prostate core-needle biopsies collected at the Prof. Franciszek Lukaszczyk Oncology Centre in Bydgoszcz, Poland. The dataset comprises 869 glass slides from 169 patients, each digitized sequentially after sectioning, deparaffinization, and hematoxylin and eosin staining, yielding in total 2607 whole-slide images. The dataset supports the development and external validation of label-free virtual staining, virtual unstaining, image-registration and segmentation methods. Each slide is accompanied by a Gleason score, enabling downstream analyses of cancer classification and grading concordance. The stained images can be used independently for color normalization and cross-scanner robustness studies. All images are de-identified, distributed in their native scanner format and will be available through the BioImage Archive.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13883
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Spatial-Spectral Coupling as Attention: Scaling Shape Correspondence to High-Resolution Surfaces
2026-11-10 10:31:28Bence Demeter, Kilian Ehrenreich, Amelie Kleber, Ioana Oprisiu, Philipp Spohn, Christian Wachinger, Fabian Bongratz, Lennart BastianAbstract
Establishing dense correspondence between high-resolution 3D shapes remains challenging due to the spatial--spectral coupling at the core of many functional-map methods, which grows quadratically with mesh resolution. We observe that this coupling is equivalent to a scaled dot-product attention formulation, with learned descriptors serving as queries and keys and spectral basis functions as values. This connection makes efficient attention implementations directly applicable to shape matching and an effective mechanism for scaling spatial-spectral coupling. We show that optimized quadratic attention implementations can scale modern deep functional map pipelines to large mesh sizes, improving both runtime and memory consumption, while sub-quadratic methods introduce errors, particularly for non-isometric shape pairs, where an exact coupling proves most necessary. These insights motivate \textit{Geo-FlashAttention}, which restricts the exact softmax to geometry-selected surface patches and achieves near-linear empirical scaling while preserving correspondence accuracy of exact implementations. Across six standard benchmarks and three functional-map pipelines, rigorous analysis shows that efficient couplings preserve correspondence accuracy. At $60k$ vertices, these insights reduce peak training memory from 106.08 to 5.67\,GiB, with Geo-FlashAttention being $1.5\times$ faster per training step than FlashAttention. This enables end-to-end learning for correspondence problems on large meshes such as cortical surfaces, where high resolution is required to preserve disease-relevant intricacies of the surface geometry. Project page available this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.13870
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Polarization Aware Deep Learning Framework for SAR Despeckling
2026-11-10 10:25:36Gianpaolo Passarello, Sergio Vitale, Hossein Aghababei, Giampaolo Ferraioli, Vito PascazioAbstract
Speckle is an inherent characteristic of synthetic aperture radar (SAR) imagery that complicates image interpretation and the extraction of reliable information. Although deep-learning-based despeckling methods have demonstrated promising performance, their applicability across different polarimetric configurations is often limited by the need for specialized architectures or separate training procedures. This paper presents a unified deep-learning framework for despeckling single-, dual-, and full-polarization SAR data using a single trained convolutional neural network. The proposed approach adopts a common nine-channel representation of the polarimetric covariance matrix and a polarization-aware masking strategy to accommodate different configurations without modifying the network architecture. Qualitative and quantitative comparison on three different scenes from different sensors demonstrate competitive performance across the considered experiments and support the feasibility of a unified, polarization-flexible approach to SAR despeckling across different polarimetric configurations.
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URL
https://arxiv.org/abs/2611.10091
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Towards Text-Only Remote Sensing Scene Classification via Semantic Proxy
2026-11-10 10:13:00Xianping Ma, Kunlin Wu, Xiaofeng Liu, Xiaokang Zhang, Yuanxin YeAbstract
Remote sensing scene classification (RSSC) is traditionally formulated as a vision-centric problem, where inference relies on rich visual representations extracted from images. In this work, we revisit this assumption by introducing text-only RSSC, a new problem setting that investigates whether remote sensing scenes can be recognized using only textual semantic representations. To enable this study, we propose SeProRS, which reformulates RSSC as a text-only classification task through semantic proxy generation. Specifically, a vision-language model (VLM) is first employed to convert remote sensing images into textual descriptions, termed semantic proxies, which are then encoded by a text embedding model for scene classification. This decoupled pipeline provides a unified and reproducible benchmark for evaluating text-only scene understanding. Furthermore, we investigate representative prompting strategies and proxy generators, and analyze their impact on classification performance, text embedding behavior, cross-generator transferability, and efficiency. Extensive experiments on three well-known RSSC datasets show that semantic proxies can preserve substantial discriminative information, while also revealing inherent limitations of purely text-based scene understanding. This paper does not aim to replace image-based RSSC, but instead establishes a new research direction by quantifying the feasibility and boundaries of semantic proxy-based scene recognition. The proposed benchmark and analysis provide a foundation for future research on efficient and modality-reduced remote sensing scene understanding. The source code and datasets will be accessible at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13851
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FastWild-GS: Minutes-Scale In-the-Wild Gaussian Splatting via Compact Per-Image Appearance Adaptation
2026-11-10 09:39:33Donghun Ryu, Seokhyun Kim, Minhyeok LeeAbstract
Internet photos of a scene differ in exposure, illumination and transient objects such as passers-by, so one set of Gaussian colours cannot explain all photos (the in-the-wild setting). Recent in-the-wild Gaussian Splatting methods mostly report hours of per-scene optimization. We present FastWild-GS, which describes each photo by a 128-dimensional Photo Appearance Token and a per-channel transform, a scale and an offset applied to the rendered image (six parameters); a small MLP predicts the colour of each Gaussian from this token and a per-Gaussian feature, replacing the spherical-harmonics colour. The same transform is applied to the renders that score Gaussians for densification and pruning, so appearance differences between photos are not counted as reconstruction error. On PhotoTourism, FastWild-GS trains in 8.8 minutes per scene with the lowest LPIPS among the compared methods, 23x faster than AsymGS, the next best in LPIPS. On NeRF-OSR, it has the best average PSNR, SSIM and LPIPS among the compared methods in 9.7 minutes per scene.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13808
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How Many Demonstrations Does a VLA Need? On the Sample Efficiency of VLA Finetuning
Abstract
Pretrained vision language action (VLA) models show strong zero-shot performance but they are rarely deployed directly and are often first post-trained on demonstrations from the target setting. Yet VLAs are typically compared by zero-shot or full-data fine-tuned success which reveals little about the data and compute required for downstream adaptation. We study adaptation efficiency for six VLAs ($\pi_0$, $\pi_{0.5}$, and GR00T N1-N1.7) on four LIBERO suites, varying both demonstrations and training steps. This reveals differences hidden by full-data fine-tuning: models which are within two points on the full LIBERO-Spatial dataset differ by up to 3$\times$ in success with 50 demonstrations. $\pi_{0.5}$ needs 2-5$\times$ fewer demonstrations than $\pi_0$ and is the most sample-efficient model at every budget, with the largest gap on the long-horizon suite. Within GR00T, N1.5 adapts more efficiently than N1.6 and N1.7 on three of four suites, despite their stronger zero-shot performance. We also study one pretraining factor behind these differences by freezing the VLM during finetuning: this severely degrades the $\pi$ models whose VLM is jointly trained with the action expert during pretraining, but barely affects GR00T models.
Abstract (translated)
URL
https://arxiv.org/abs/2611.09964
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The Target Is a Predicate: Predicate-State Grounding for Semantic-Level Video Reasoning Segmentation
2026-11-10 09:21:56Yuhuan Hong, Yiming Ren, Zhiqiang Lin, Feize Wu, Xingyun Liu, Yitong Xie, Lei Zhou, Lisheng Wu, Junjie Wang, Yujiu YangAbstract
In this paper, we introduce Predicate-State Video Reasoning Segmentation (PS-VRS), where a query defines a dynamic predicate $P(o,t)$ over objects and time rather than a fixed object identity. Existing methods typically treat the target as permanently active once identified, leading to over-emission of masks during vacant intervals and failure to handle target switching. To address this, we construct PS-Bench, a large-scale benchmark with 2,077 videos and 16,250 queries annotated with explicit active/vacant predicate states, temporal intervals, and pixel masks. We further propose Predicate-State Grounding (PSG), a State-Bind-Emit pipeline that: (1) predicts [ACTIVE]/[VACANT] state tokens; (2) aligns them to video frames via dense cross-modal similarity; and (3) emits masks via SAM2 only for active states. Experiments show that PSG outperforms existing baselines by +9.32 J&F on PS-Bench and significantly reduces false-positive mask emissions. Our results demonstrate the effectiveness of explicit predicate-state modeling for video reasoning segmentation with dynamic visual witnesses.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13785
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Marrying Capacity and Precision for Efficient 2D Gaussian Image Representation
2026-11-10 09:21:35Lin Dong, Qi Ming, Liuqian Wang, Wentao Chao, Yufei Gao, Chengming Liu, Lei ShiAbstract
2D Gaussian representations provide an explicit and efficient way to represent images, but their rate-distortion performance under limited budgets depends critically on how Gaussian capacity and parameter precision are allocated. Existing methods typically optimize these two resources separately, overlooking content-dependent capacity demands and heterogeneous quantization sensitivity. We present CP-GS, a capacity-precision coupled Gaussian splatting framework for budget-aware 2D Gaussian image representation that jointly optimizes capacity allocation and attribute precision under a shared rate constraint. CP-GS first constructs a content-adaptive Gaussian representation by dynamically reallocating a fixed Gaussian budget from redundant regions to areas with higher representation demand. It then introduces hierarchical distortion-aware representation quantization, using efficient rendering-based calibration to allocate precision across Gaussian attributes and further refine it within heterogeneous attribute groups. Finally, it jointly optimizes Gaussian capacity and attribute precision by constructing candidate representations with different Gaussian counts and precision configurations and selecting operating points according to decoded distortion and actual serialized size. Experiments on DIV2K and Kodak show that CP-GS achieves improved rate-distortion performance over representative 2D Gaussian and implicit neural representations across a range of budgets, while maintaining a fixed overall representation budget and low optimization cost. These results demonstrate the benefit of jointly optimizing spatial capacity and parameter precision for compact Gaussian image representation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13784
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RF3DGS: Reflection-Free 3D Gaussian Splatting with Self-Priors
2026-11-10 09:11:01Qiang Wen (The Hong Kong University of Science and Technology), Guotao Meng (The Hong Kong University of Science and Technology), Junhong Ruan (The Hong Kong University of Science and Technology), Hongyu Liu (The Hong Kong University of Science and Technology), Chenyang Lei (Huawei Research), Qifeng Chen (The Hong Kong University of Science and Technology)Abstract
3D reflection removal is crucial in applications demanding high-fidelity visualization, such as museum exhibitions. In this work, we propose Reflection-Free 3D Gaussian Splatting (RF3DGS), a novel method for superior reflection removal. RF3DGS exclusively leverages a widely-adopted photometric prior for reflection removal: that the minimum pixel intensity across multi-view images corresponding to the same 3D point strongly correlates with reflection-free information. However, generalizing this principle to a 3D Gaussian Splatting within a multi-view context is non-trivial. To address this, RF3DGS first optimizes a vanilla 3DGS model using a geometric prior to estimate a coarse, reflection-free depth map for each view. Leveraging these depth maps and photometric prior, a 3D warping process aggregates reflection-free information from auxiliary views to construct a clean transmission pseudo-label for a given reference view. This pseudo-label provides a robust self-supervised signal for optimization. We further introduce an Adaptive Twin Rendering (ATR) loss to synergistically utilize this pseudo-label while preserving high-frequency details from the original, reflection-degraded images. By adeptly fusing these internal 3D self-priors with the ATR loss, RF3DGS demonstrates state-of-the-art reflection removal performance across both synthetic and real-world scenarios. Furthermore, we establish the first 3D reflection removal benchmark, based on the Unity rendering engine, for rigorous quantitative evaluation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13772
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From Depth to Safe Depth: Uncertainty-Aware Monocular Perception for Mobile Robot Navigation
Abstract
Monocular depth foundation models make it tempting to replace depth sensors on mobile robots with a single RGB camera. We show that, used off the shelf, this is unsafe: in a simulated indoor navigation benchmark, a standard Nav2 stack driven by Depth Anything V2 (metric) collides in 63\% of 120 episodes, because the model systematically over-estimates the distance to nearby obstacles. We propose a lightweight plug-in module that turns predicted depth into \emph{safe depth}. A 0.58\,M-parameter head on top of the \emph{frozen} depth model predicts, per pixel, a log-depth correction $\mu$ and an uncertainty $\sigma$, trained with a $\beta$-weighted Gaussian negative log-likelihood. The navigation stack consumes the one-sided bound $\hat d_{\text{safe}}=d\,e^{\mu-k\sigma}$, where $k$ is set by conformal calibration on a held-out world, and uncertainty also decides which pixels may be treated as floor. The correction halves the depth error (AbsRel $0.262\rightarrow0.134$) and reduces near-range over-estimation from 59.8\% to 12.0\%. In navigation, safe depth raises the success rate from 37\% to 70\% and lowers collisions from 63\% to 3\%, close to the 2\% of ground-truth depth. A constant safety margin calibrated to the same pixel coverage reaches only 30\% success because it freezes the robot; uncertainty-aware safety wins 52 of 56 discordant paired episodes against it. Gains hold on glass, texture-less walls and dark regions.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13769
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Exploring Synchrony for Communication in Modular Neural Networks
2026-11-10 09:00:23Nikolas Willems, Qiyang Sun, Bj\"{o}rn W. SchullerAbstract
One motivation for modular neural networks is to divide computation across components that learn distinct, reusable functions, which could then be recombined to support compositional generalisation. In practice, modular structure alone does not guarantee that such roles develop, and unrestricted communication between modules can undermine any specialisation that does emerge. This raises the question of how modular communication should be organised. The problem can be viewed as an analogue of the perceptual binding problem, where synchrony has been proposed as a mechanism for coordinating distributed representations. We introduce SyncNet, a modular architecture that separates module content from a dynamical phase state: learnt Kuramoto dynamics evolve the phases, while phase alignment determines communication through a shared channel. We evaluate SyncNet on Sort-of-CLEVR for relational reasoning and on SQOOP for systematic generalisation, where lower pair variety demands more recombination at test time. SyncNet gives the best held-out accuracy of the models compared at the three lowest pair varieties, by 5.1 percentage points at the hardest, and is competitive on Sort-of-CLEVR though below the strongest attention baselines. Test-time overrides show that performance depends on phases that evolve: fixed phases cost as much as no addressing at all, and permuting which module holds which region is nearly as damaging. A content-addressed variant trained from scratch nonetheless matches it, so the benefit appears to come from the modular decomposition rather than synchrony itself.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13759
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Word-Level Alignment of OCR and Ground Truth under Zoning and Reading-Order Errors
Abstract
OCR post-processing needs OCR text aligned word by word with its ground truth. Data sets rarely provide that mapping, and a monotone aligner can lose its place when zoning errors permute the reading order. We revisit this problem, the Zoning Problem of our 2019 paper, with a protocol that tests correctness under such permutations. We apply OCR-side reading-order permutations with construction-time word-level gold and score correct-match F1, cascade length and recovery. We compare the 2019 location-aware aligner, a re-anchoring extension of it, a RETAS-style baseline (RETAS-lite), a multi-region aligner that combines unique-word and unique n-gram anchors with chunk-to-window assignment, genalog's RETAS and the original Java RETAS. Experiments use synthetic character noise with block moves, paragraph swaps and column interleaving, historical newspaper reading-order permutations, geometry-derived gold for Tesseract OCR, and a small word-box set. Synthetic tests show where local tracking fails after large displacements and where unique-anchor methods collapse when unique anchors are scarce. On our synthetic boilerplate page the 2019 method had the highest F1 among those tested. Historical newspaper and geometry-derived evaluations carry the limitations of approximate gold and the article-order convention. The first multi-region version has a quadratic scaling cost. Local tracking remains fragile under large reading-order changes; the tested anchor-based methods handle many of those cases but fail when unique anchors are scarce. Approximate gold, the article-order convention, missing residual annotation, and the lack of modern multi-column word-level ground truth remain limitations.
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URL
https://arxiv.org/abs/2611.13752
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Effects of Laser Beam Width and Measurement Noise on Tree Diameter Estimation in Laser Scanning
2026-11-10 08:40:16Jesse Muhojoki, Nuutti Ervasti, Eric Hyypp\"a, Aapo Erkkil\"a, Eppu Honkanen, Tuomas Yrttimaa, Mikko Vastaranta, Juha Hyypp\"aAbstract
Laser scanning has been utilized for decades to measure tree structure and geometry. Despite continuous advances in sensor technology, uncertainties arising from scanner characteristics and measurement conditions has remained relevant sources of error. In particular, factors such as laser beam diameter can introduce both systematic and random errors in the reconstructed tree geometry. This study investigates the main sources of these errors in mobile laser scanning (MLS) and quantifies their effects on stem curve (stem diameter) estimation. We discovered through simulations and experiments that the heteroscedasticity (difference in variance in ranging and scan angle) in the measurement noise is a key factor along with the beam width of the lidar, which cause the bias of the diameter estimates to depend not only on distance, but also on the tree diameter. We demonstrated that this bias can be reliably predicted and negated for trees with a diameter at the breast height greater than 10 cm using technical specifications of the sensor without any additional calibration. In experiments, the proposed bias compensation method reduced the root-mean-square error of stem diameter estimates to sub-centimeter level on boreal forest test sites. However, for small trees with diameter below 10 cm, the diameter bias becomes sensitive to the diameter of the tree$\unicode{x2013}$an unknown factor$\unicode{x2013}$and other factors, such as different bark and out-of-roundness of the stem, limiting the precision of lidar for measuring geometry of trees and applicability of the presented method.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13739
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Hint, Not Hole: Localization-Robust Video Overlay Removal
2026-11-10 08:04:45Dongyang Zhang, Wei Zhang, Renjie Zou, Zhiwei HuangAbstract
Video overlay removal recovers the scene beneath burned-in subtitles, karaoke lyrics, titles, credits, logos and watermarks. Generative video models now fill a known region convincingly, but in practice the region comes from a detector or a segmenter, and it is wrong in both directions: it misses strokes, halos, whole frames and whole objects, and it covers background between characters, around boxes and on genuine scene text. Erasers that treat the mask as a hole, regenerating inside and copying outside, inherit every one of these errors. We present HintErase, which treats localization as a hint, not a hole. The model sees the full, unmasked video together with a hint channel; during training the hint is corrupted the way real localizers fail, while supervision follows the true overlay. The result is a single model that removes overlay the hint missed, keeps background the hint wrongly covered, and is trained to keep genuine scene text even when the hint lands on it. For full-length releases, a streaming pipeline handles interlaced sources, shot cuts, missed and jittering hints, paste-back and audio, with memory independent of the length of the video. This report describes the approach, the pipeline that turns it into full-length deliveries, and qualitative results; video results, with input, hint and output side by side, are on the project page.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13711
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When Does Sliced Inference Pay Off? Resolution, Tiling and Dense Fusion for Small Object Detection
2026-11-10 07:50:27Stefan Becker, Jens Bayer, David M\"unch, Wolfgang H\"ubnerAbstract
Sliced inference such as SAHI is the standard remedy for small objects in high-resolution imagery, but its gains are usually measured against low-resolution baselines. We revisit sliced inference with scale as the explicit variable and ask when tiling actually pays off. We compare seven dense detectors from four families on VisDrone and DOTA, under pre-specified hypotheses, calibration-only tuning and a single evaluation on held-out test data. Two findings emerge. First, much of the benefit attributed to slicing is a resolution effect: on VisDrone, a single pass trained at a higher resolution outperforms tiling with box fusion (merging decoded boxes) for every detector that could be trained at that resolution, and at equal compute a higher resolution helps more than a larger model. Second, tiles carry complementary information that box fusion wastes. We introduce SCAHI (Slicing with Coverage-Aware Hyper Inference), which accumulates the dense outputs of all passes with weights that give every image position the same total coverage, and reads objects off a peak grid fine enough to keep crowded neighbours apart. On identical passes, SCAHI outperforms box fusion for every YOLO detector on both datasets and matches it for FCOS. On DOTA, whose images far exceed the resolution a single pass can be trained at, tiles with SCAHI beat the best single pass for every detector, by 4.3 to 5.6 mAP50 at 2048 px, and replacing only SAHI's merge step by SCAHI adds 2.3 to 4.0 mAP50. An ablation attributes the gain to the border weighting and the resolution of the peak grid. Source code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.12657
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CJRR-Net: A Candidate-Joint Reprojection Ranking for Future-View Selection in Human Pose Estimation
2026-11-10 07:46:05Huiting Xi, Marsha Mariya Kappan, Eduardo Benitez Sandoval, Francisco CruzAbstract
Multi-camera systems support performance recording, sports and human-movement analysis, where selecting an informative viewpoint is important for reliable human pose estimation. We propose the Candidate-Joint Reprojection Ranking Network (CJRR-Net) for future-view selection among eight calibrated cameras in MPI-INF-3DHP. The model combines current-view pose observations, camera configuration, and candidate-specific geometric features obtained by reprojecting the estimated 3D pose and its uncertainty into each candidate view. CJRR-Net achieves a selected-view 2D mean per-joint position error (MPJPE) of 105.18 pixels, compared with 114.64 pixels for the shared-input MLP and 117.56 pixels for adapted PoseErrNet. It also achieves the lowest mean error among the listed policies in each evaluated occlusion group. A blender rollout illustrates repeated camera selection. These findings support candidate-specific geometry for single-view selection in the evaluated fixed eight-camera setting.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13700
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