Computer Vision
TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving
Miguel Antunes-García, Santiago Montiel-Marín, Fabio Sánchez-García et al. (+3 more)
Bird's-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the error propagation inherent in traditional modular pipelines. However, current state-of-the-art approaches rely predominantly on
geometric supervision, such as occupancy regression and optical flow, effectively treating scene
agents as generic moving obstacles. This absence of explicit semantic awareness imposes limitations on the capacity of the model to solve ambiguities in complex scenarios, particularly those where object-specific behavior is essential for accurate
forecasting (e.g. overtaking, intersections). In this paper, we introduce Text-Guided Representation for Instance Prediction (TGRIP), a novel framework that bridges this gap by injecting rich semantic priors into the instance prediction loop. The proposed teacher-student pipeline employs
Vision-
Language Foundation Models to generate dense, semantic-enhanced BEV maps from multi-camera images. These maps serve as auxiliary supervision during training, guiding the network to learn spatio-temporal representations that are not only geometrically consistent but also semantically discriminative. To the best of our knowledge, this represents the first attempt to unify semantic guidance with the temporal task of future instance prediction. The experimental results demonstrate that TGRIP surpasses existing state-of-the-art models in nuScenes, validating the hypothesis that semantic enrichment is a fundamental element for robust, end-to-end motion prediction. Code is available on https://github.com/miguelag99/TGRIP.
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All authors Miguel Antunes-García, Santiago Montiel-Marín, Fabio Sánchez-García, Rodrigo Gutiérrez-Moreno, Rafael Barea, Luis M. Bergasa
Computer Vision
Hybrid Deep Learning for Traceability and Classification of Industrial Slate Tiles
Soren Antebi, Stefan Eickeler, Sandra Halscheidt et al. (+4 more)
Applying deep learning to instance-aware reidentification of slate tiles and extraction site classification can improve production efficiency and quality control in the slate tile industry. These tasks are particularly important for handling natural materials where visual variability can make manual inspection costly and error-prone. We present a lightweight, hybrid deep learning approach that combines image matching and classification within a single framework. The system integrates a feature-matching branch based on XFeat with a MobileNetV3- based classification branch. The XFeat branch, combined with a LightGlue matching head, improves instance matching performance by +15.4% AUC. For classification, features from both backbones are shared and fused, resulting in a +10.9% accuracy improvement over a standard MobileNetV3 model. Our approach is evaluated on a newly created industrial
dataset consisting of 2,610 slate tile images from six extraction sites. The results demonstrate the effectiveness of the proposed approach for object re-identification and classification in an industrial setting.
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All authors Soren Antebi, Stefan Eickeler, Sandra Halscheidt, Rene Schmitz, Michael Muellers, Dirk Hecker, Rafet Sifa
Computer Vision
LILAC: Layer-Wise Independent LoRAs and Cascaded Conditioning for Multi-Concept Customization of Diffusion Models
Marian Lupascu, Sebastian Ripa, Mihai Trascau et al. (+2 more)
Personalizing text-to-image
diffusion models to render several specific subjects in a coherent image remains challenging: the model must preserve each subject's identity while keeping the scene spatially and visually coherent. Methods that fuse independently trained concept adapters in a shared weight space (via
federated averaging, gradient fusion, or orthogonality constraints) suffer from identity confusion and style bleeding. In this work, we show that composing concepts as separate image layers, instead of merging their adapters in a shared weight space, avoids parameter-level interference. We introduce LILAC, a framework that composes independently trained low-rank adapters at inference time: each subject is conditioned on the frozen composite of previously placed subjects, with exactly one adapter active at a time, therefore identities never interfere at the parameter level. LILAC composes the adapters without joint training, scales linearly with the number of concepts, and is backbone-agnostic. Under the Orthogonal Adaptation protocol, LILAC applied on Qwen-Image-Edit+Qwen-Image-Layered reaches an ArcFace detection rate of 0.861. Code is available at https://github.com/marianlupascu/LILAC.
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All authors Marian Lupascu, Sebastian Ripa, Mihai Trascau, Mariana-Iuliana Georgescu, Ionut Mironica
Computer Vision
DGSeg: Dynamic Gating of Semantic-Spatial Guided Predictions for Reasoning Segmentation
Ruizhe Zeng, Siyu Cao, Lu Zhang et al. (+1 more)
Reasoning segmentation aims to predict pixel-wise masks for targets given complex
language queries. Existing approaches leverage
Multimodal Large
Language Models (MLLMs) for
vision-
language reasoning and generate intermediate target cues (e.g., points or boxes) to guide a
segmentation model. However, compressing rich
reasoning into sparse cues often introduces ambiguity and noise, preventing these cues from accurately preserving the
reasoning intent. While multiple complementary cues can enrich target information, existing methods typically feed them jointly into a single
segmentation process, allowing ambiguous or erroneous cues to affect the entire prediction. Therefore, we propose DGSeg, a
reasoning segmentation framework that learns to fuse predictions guided by semantic and spatial cues. Specifically, the MLLM jointly reasons about both target identity and spatial location, producing complementary semantic and spatial cues that are fed into separate
segmentation branches. Their predictions are adaptively integrated by a lightweight dynamic gating module trained with relative branch-quality supervision to suppress noisy or conflicting regions. Extensive experiments demonstrate that DGSeg consistently outperforms strong baselines on multiple
benchmarks and achieves 69.6% and 67.3% gIoU on the challenging ReasonSeg validation and test splits. Code is available at https://github.com/RZZeng/DGSeg.
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All authors Ruizhe Zeng, Siyu Cao, Lu Zhang, Zhiyong Liu
Computer Vision · Cross-listed from cs.RO
SLAM: Structured and Localized Analytic Manifold Adaptation for Forgetting-Immune and Domain-Robust Lifelong VPR
Kenta Tsukahara, Kanji Tanaka and Rai Hisada
Visual Place Recognition (VPR) under long-term operation is essential for autonomous mobile
robots. While Analytic Class-Incremental Learning (ACIL) provides
memory-free ($O(1)$) task adaptation with exact forgetting immunity, applying it to lifelong VPR suffers from extreme vulnerability to non-linear domain shifts induced by environmental variations. In this work, we introduce the concept of the ACIL-Domain (ACIL-D)—a canonical invariant feature
manifold where autocorrelation states remain locked. We resolve the domain vulnerability via Disentangled Domain
Alignment (D-DA), which decouples latent features into invariant semantics within ACIL-D and variant style vectors for directional projection. Furthermore, by uncovering an algebraic isomorphism between recursive ACIL updates and Extended Kalman Filter (EKF) covariance propagation, we establish a control-theoretic framework designated as SLAM (Structured and Localized Analytic
Manifold adaptation). SLAM integrates dynamic temperature-scaled Gaussian Mixture Models (GMM) to isolate topological non-linearities, Unscented perturbed propagation to dampen feature variations, and minimax $H_{\infty}$-robust criteria to bound worst-case noise accumulation. Empirical evaluations on the non-stationary NCLT
dataset demonstrate that our proposed framework substantially outperforms existing baselines, achieving a final all-class accuracy of 27.7% with the full SLAM framework (and up to 29.0% with the U+H variant) while guaranteeing complete forgetting immunity.
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Computer Vision
DeGenseGS: Geometrically and Semantically Decoupled Surgical Scene Understanding in 4D Gaussian Splatting
Yimo Wang, Bin Kang, Shuojue Yang et al. (+1 more)
Real-time, text-promptable 4D reconstruction is indispensable for autonomous
surgical interaction. Severe misalignment between semantic meaning and physical anatomy still persists, largely because existing solutions integrate
Vision-
Language Models into deformable fields via a rigid coupling scheme that tightly binds semantic features to
geometric warping. In this paper, we propose DeGenseGS, Geometrically and Semantically Decoupled
Surgical Scene Understanding in 4D Gaussian Splatting, a novel framework that independently models semantic
evolution and
geometric deformation. Specifically, we propose a HexPlane-based spatiotemporal
entanglement module that uses shared kinematic latents to synchronize semantic mutations with scene dynamics, while explicitly disentangling semantic updates from
geometric deformation. To further ensure robustness against reconstruction artifacts, we devise a Rasterization-Native Semantic Extraction mechanism that infers semantics from topologically continuous feature maps. Additionally, we incorporate an angular-aligned optimization strategy that conforms to the native hyperspherical latent space, thereby preventing semantic distortion. Extensive evaluations on the CholecSeg8k and EndoVis18
datasets demonstrate that DeGenseGS achieves state-of-the-art performance. Our framework yields enhanced
geometric completeness and robust semantic-anatomic
alignment, enabling spatially continuous
segmentation despite drastic tissue deformation and topological transitions.
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All authors Yimo Wang, Bin Kang, Shuojue Yang, Yueming Jin
Computer Vision
Continual Model Merging with Test-Time Adaptation for Whole-Slide Image Analysis
Duc-Thanh Le, Doanh C. Bui, Maï K. Nguyen et al. (+1 more)
Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previous training data. Recent state-of-the-art model merging methods employ test-time adaptation (TTA-guided merging) to address distribution shifts by adjusting merging-related variables using unlabeled target data. However, these methods have primarily been studied in multi-task or single-target settings, and their behavior under sequential continual learning remains insufficiently understood. We present a
benchmark study that maps this family of methods to rehearsal-free continual Whole Slide Image classification and evaluates them against traditional continual-learning approaches. Experiments on six TCGA cancer-subtyping cohorts cover CLASS-IL and TASK-IL scenarios, in-domain and out-of-domain evaluation, and different task orders. The results show that adapting model merging at test time can provide strong task-specific performance and improve retention of previously acquired knowledge without storing historical WSIs. Nevertheless, performance remains sensitive to task order and to the interaction between adaptation on the current distribution and accumulated knowledge. This
benchmark identifies model merging with test-time adaptation as a promising direction for continual computational pathology and motivates future methods that balance adaptation to domain shift with explicit preservation of historical knowledge.
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All authors Duc-Thanh Le, Doanh C. Bui, Maï K. Nguyen, Khang Nguyen
Computer Vision · Cross-listed from cs.AI
FM-ChangeNet: Learning Change through Pathwise Feature Transport
Roie Kazoom, George Leifman and Genady Beryozkin
We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal
reasoning as continuous transport in feature space rather than static endpoint comparison. Given encoded pre and post-temporal representations, we construct intermediate latent states and learn a time-conditioned velocity field $\hat{v}_\theta(z_t,t)$ along the transformation trajectory. This pathwise formulation constrains the predictor over a continuum of intermediate states, providing a denser and less ambiguous supervision signal than conventional endpoint-only
segmentation and enabling the model to capture temporal
evolution explicitly. The learned velocity field is not only a transport mechanism but also an interpretable representation of change: its magnitude serves as a spatially localized change cue that helps distinguish true structural variation from nuisance effects such as illumination shifts and spatial misalignment. We develop a hierarchical multi-scale architecture with cross-temporal
alignment, time-conditioned coarse-to-fine flow decoding, and a unified objective that couples flow supervision, trajectory consistency, spatial regularization, and
segmentation loss. Experiments on remote sensing
benchmarks show that the proposed framework produces more structured and robust change representations while achieving state-of-the-art performance.
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Computer Vision
MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
Vu Minh Tran, Doanh C. Bui, Van-Hau Pham et al. (+1 more)
Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existing survival models are typically trained independently for each cancer cohort, making continual adaptation computationally expensive for gigapixel-scale WSIs. In this study, we propose MergeSurv, a merging-based continual learning framework for WSI survival analysis. A pathology
vision-
language foundation model is independently fine-tuned on each task, and the learned parameters are sequentially merged into a unified model without storing previous training data. We further investigate two inference strategies: One-for-All (OFA) and Voting-Expert Aggregation (VEA). Experiments on four TCGA cohorts demonstrate that MergeSurv outperforms naive fine-tuning as well as representative regularization-based and rehearsal-based continual learning methods, while effectively reducing catastrophic forgetting. The results suggest that model merging is a promising direction for scalable and
privacy-preserving continual learning in computational pathology.
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All authors Vu Minh Tran, Doanh C. Bui, Van-Hau Pham, Khang Nguyen
Computer Vision · Cross-listed from cs.RO
Route-MHT: Multimodal Transformer Guardrails for Thermal Visual Place Recognition
Zhiyuan Lu and Kanji Tanaka
Strong mapped-region thermal visual place recognition (VPR) does not ensure safe rejection of unmapped queries. We identify and quantify this gap in AnyThermal: high Map-In
retrieval accuracy coexists with confident false loop closures in Map-Out. We address it with ROUTE-MHT, a
multimodal transformer guardrail. Causal motion forms a route-local candidate pool beyond the frontend's Top-$K$. Closed-form $\text{SE}(2)$ SVD verifies candidates, while frozen visual features, nine-dimensional SVD residuals, and motion proxies enter a masked multi-head
transformer (MHT). Their interactions yield a contextual confidence correction to reject unsupported matches without altering
geometric pose
alignment. We collect an indoor thermal
dataset with a physical mobile
robot (
Dataset-A), forming five same-day/cross-day map-query pairs; the protected interface reaches macro R@1@5m of .610/.861.
Dataset-B comprises 20 map-hole scenarios derived from public STheReO-KAIST recordings. Across five scenario-held-out folds and three seeds, ROUTE-MHT reduces FPR from .116 for the SVD baseline to .061 (paired 95% CI [-.100, -.014]), while improving AUC from .945 to .967 and recall from .884 to .914. Public
benchmark transfer checks on STheReO-KAIST, MS2, and IRSLAM-KRI extend the evaluation under their released metric or route-progress protocols.
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Computer Vision
DriftST: One-Step Generative Inference of Spatial Transcriptomics from H&E Histology
Yuhang Yang, Yonggan Bu, Shengyuan Zhou et al. (+2 more)
Spatial Transcriptomics (ST) measures gene expression while preserving spatial context, but its high cost and low throughput leave public
datasets small. Inferring expression directly from widely available Hematoxylin and Eosin (H&E) stained histology offers a cost-effective alternative. However, existing approaches face several limitations: regression methods over-smooth toward the conditional mean, while generative methods are faithful but require slow multi-step inference; most methods treat genes as independent and equally important, ignoring inter-gene dependencies and heterogeneous gene informativeness; and most are tailored to a single resolution, either spot-level or
cell-level. To address these issues, we propose DriftST, a unified framework for inferring spatially resolved gene expression from H&E images. DriftST builds on a Cellular Drifting generative model that learns a direct drift from a histology-conditioned source to the expression distribution, retaining generative expressiveness while enabling efficient one-step generation. To capture gene structure, we introduce the STransformer, which combines a co-expression
attention module for inter-gene dependencies with a gene residual gate for differential gene importance. Operating on a generic gene-panel representation, DriftST applies directly to both spot-level and
cell-level data in one framework, and extensive experiments across diverse tissues and platforms show that it achieves state-of-the-art performance at both resolutions.
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All authors Yuhang Yang, Yonggan Bu, Shengyuan Zhou, Yiming Luo, Kai Zhang
Computer Vision
SparseOcc++: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction
Pin Tang, Zhongdao Wang, Guoqing Wang et al. (+2 more)
Vision-based 3D semantic occupancy prediction is essential for autonomous driving, yet dense voxel representations waste computation on largely empty space, while BEV and TPV projections compromise fine-grained 3D structure. Fully sparse representations offer an attractive alternative, but existing methods, including SparseOcc, entangle scene completion with semantic prediction by indiscriminately propagating high-dimensional features into empty regions and applying voxel-wise classification. This creates excessive activations, computational overhead, and
geometric ambiguity. We present SparseOcc++, a geometry-aware sparse framework that explicitly decouples scene completion from semantic
segmentation. SparseOcc++ reformulates completion as signed-distance regression on sparse anchor voxels through a scene completion field (SCF). To model complex outdoor geometry robustly, it combines orthogonal decomposition with discretized distance learning. A geometry-guided propagation module then converts the SCF into a complete volumetric scene and restricts semantic
segmentation to geometrically verified regions. Experiments establish new state of the art: SparseOcc++ improves IoU by 2.3 points and is 3.9x faster than SparseOcc on nuScenes, while achieving a 5.9x speedup over OccFormer on SemanticKITTI.
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All authors Pin Tang, Zhongdao Wang, Guoqing Wang, Xiangxuan Ren, Chao Ma
Computer Vision
When Does High-CFG Diffusion Inversion Fail? A Controlled Study of Prompt–Latent Interactions
Yan Zeng, Yusuke Hosoya, Huyen T. T. Tran et al. (+1 more)
Text-guided
diffusion inversion is central to image editing, where an image is mapped to an initial latent and then edited by replaying the denoising process under a modified
prompt. In practice, however, inversion is often performed with a lower classifier-free guidance(CFG) scale than the one used for generation or editing. This mismatch is empirically useful but leaves a basic question unresolved: when a target image is generated by a high-CFG trajectory, when can that trajectory actually be inverted? We study this question in a controlled generation–inversion–reconstruction setting, where the true initial latent and denoising trajectory are known. Using
prompts taken from an existing
diffusion-editing
benchmark, we generate images under high CFG and reconstruct them with fixed-point inversion using the same
prompt and guidance setting. The results reveal three types of
prompt-level reconstruction behavior: easy
prompts that reconstruct for most initial latents, hard
prompts that fail for most initial latents, and intermediate
prompts whose success depends on the
prompt–latent pairing. To analyze the generation side, we define
prompt pressure, a step-wise measure of how strongly CFG moves the denoising update away from the unconditional trajectory. Total pressure correlates with reconstruction quality and separates easy from hard
prompts, but it does not explain the success or failure of intermediate
prompt–latent pairs. Text-side analyses further show that the main visual subject and wording can change inversion difficulty. Finally, we evaluate a compact trajectory-consistency intervention that relaxes guidance only at locally unstable inverse steps. This diagnostic check improves reconstruction and
Prompt-to-
Prompt editing in our controlled setting, supporting the view that high-CFG inversion failure requires local, trajectory-aware analysis.
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All authors Yan Zeng, Yusuke Hosoya, Huyen T. T. Tran, Takayuki Okatani
Computer Vision · Cross-listed from cs.SE
Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards
Tianhao Niu, Ziyu Han, Qiguang Chen et al. (+5 more)
Automatic data visualization generation has advanced rapidly with multi-modal large
language models, yet existing efforts largely focus on static charts and overlook the interactive dashboards commonly used for real-world data exploration. We introduce Dashboard2Code, a novel task that requires a model to proactively explore an interactive dashboard, acquire and integrate feedback from its own interactions (e.g., clicking and filtering), and generate code that reproduces the target dashboard. To support comprehensive evaluation, we present DashboardMimic, the first Plotly+Dash
benchmark for Dashboard2Code, comprising 180 carefully designed and manually verified dashboard-code pairs spanning three difficulty levels and covering eight common real-world interaction patterns. We further propose an automated evaluation framework tailored to dashboards that combines code semantic analysis with dynamic interaction-based testing to assess visual and interaction consistency, showing strong agreement with human judgments. Experiments across a range of open- and closed-source multi-modal models reveal that even the strongest systems struggle on high-complexity dashboards and that a substantial performance gap remains between open-source and closed-source models on the Dashboard2Code task.
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All authors Tianhao Niu, Ziyu Han, Qiguang Chen, Shiqi Zhou, Baocai Shan, Hengjie Fang, Qingfu Zhu, Wanxiang Che
Computer Vision
Reference-Induced Consensus for Selective Posed-Reference Visual Localization
Wonseok Kang, Jaehyun Kim, Jeongmin Lee et al. (+1 more)
We present RIC-Loc (Reference-Induced Consensus localization), a scene-training-free posed-reference localizer that is SfM-point-map-free in its main
estimator: it uses known reference poses, but not precomputed SfM 3D map points, query-to-map 2D-3D matches, or query-to-map PnP. A frozen VGGT pass predicts local camera poses, depth, and query-reference tracks for a query and selected references. Each reference induces one map-frame SE(3) query-pose hypothesis, robust consensus estimates the pose, and the preserved hypothesis structure yields two reliability scores: spatial dispersion and a track-conditioned covariance score. On the covariance-eligible set, the two scores are complementary for held-out, ground-truth-free failure detection across indoor, outdoor, and large-scale low-texture
benchmarks: the joint policy is strongest in textured scenes and the covariance score in the low-texture regime, and the hypothesis-derived scores consistently outperform the standard
retrieval-score gap and random rankings. Without per-scene training the consensus
estimator remains accurate – competitive with structure-based localization indoors and improving over a comparable feed-forward baseline – giving an effective selective operating regime for posed-reference localization. Code is available at https://github.com/SNU-DLLAB/ric_loc.
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All authors Wonseok Kang, Jaehyun Kim, Jeongmin Lee, Tae-Wan Kim
Computer Vision
Learning Probabilistic Prompt for Continual Learning
Hyekang Park, Sanghoon Lee, Geon Lee et al. (+2 more)
Continual learning aims to progressively learn from a sequence of tasks, each containing a disjoint subset of classes, while preserving previously learned knowledge.
Prompt-based continual learning methods propose to learn a small set of parameters, i.e.,
prompts, by associating them with a query feature of an input image. These methods optimize the
prompts, attempting to represent diverse patterns of images. However, we have observed that existing
prompt-based methods suffer from a
prompt collapse problem, that is, the
prompts tend to be highly similar to each other, thereby failing to capture the diverse data distributions in continual learning scenarios. To address this issue, we propose in this paper a novel
prompt-based continual learning framework that captures diverse patterns of images across a sequence of tasks. To this end, we model each
prompt as a probabilistic distribution and construct a mixture of these distributions, from which we sample diverse
prompts. This enables our model to effectively capture highly diverse image distributions in the continual learning process. We also present a distribution regularization loss to prevent abrupt changes in the
prompt distributions throughout the training process. We show extensive experimental results for continual learning on standard
benchmarks, including ImageNet-R, CIFAR-100, and CUB-200, demonstrating the effectiveness of our framework.
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All authors Hyekang Park, Sanghoon Lee, Geon Lee, Jongyoun Noh, Bumsub Ham
Computer Vision · Cross-listed from cs.LG
Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity
Juhyoung Park, Jaehyuk Bae, Hyeonbo Yang et al. (+1 more)
Modern machine learning systems demand extensive
datasets for visual recognition. Conversely, humans learn with high efficiency despite severe data limitations, often by acquiring broad categorical structures before refining finer distinctions. Inspired by this contrast, we introduce SCALA (Scaffolded Cognitive Architecture for Learning under limited dAta), a hierarchical learning framework grounded in cognitive psychology that guides models from coarse conceptual structures to fine-grained recognition. Our model exhibits human-like cognitive selectivity by effectively prioritizing task-relevant features while suppressing background distractors, a mechanism that induces a fundamental shift in representation learning. This shift is characterized by accelerated cluster formation, reduced intra-class dispersion, and enhanced semantic separability. Empirically, SCALA achieves significant accuracy improvements under severe data scarcity. Furthermore, this hierarchical scaffolding promotes robust generalization to unseen classes and accelerates the acquisition of novel categories. Collectively, our results establish SCALA as a powerful framework for achieving human-level sample efficiency and resilient category generalization in data-constrained environments.
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All authors Juhyoung Park, Jaehyuk Bae, Hyeonbo Yang, Se-Bum Paik
Computer Vision
Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification
Liuyun Jiang, Yanchao Zhang, Jinyue Guo et al. (+5 more)
Establishing large-scale, high-resolution neural connectivity maps is fundamental to elucidating the structural basis of
brain function. However, when processing terabyte- or petabyte-scale electron microscopy data, over-
segmentation inherent in automated reconstruction
algorithms remains a critical bottleneck, requiring extensive manual proofreading spanning person-years. To alleviate the heavy reliance on annotated data and the limited flexibility of conventional tracing methods, we propose a training-free, targeted
neuron tracing framework. Specifically, we introduce a skeleton-guided Heuristic Spatial Search paradigm that leverages
geometric priors to iteratively reconstruct neuronal morphologies through a probing-verification cycle. To achieve robust zero-shot semantic verification, we further develop a Dimension-Aware Semantic Verification strategy built upon the foundation model NeuroSAM 2. This strategy resolves intra-slice splits via Planar Ensemble Consensus and inter-slice splits via Axial Spatio-Temporal Propagation. Notably, we integrate the proposed workflow into the Neuroglancer visualization platform, enabling an interactive human-in-the-loop proofreading system. Experimental results demonstrate that the proposed method outperforms supervised baselines and reduces manual proofreading time by 33.4%. The source code is publicly available at https://github.com/HeadLiuYun/Probe-EM.
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All authors Liuyun Jiang, Yanchao Zhang, Jinyue Guo, Chuanyue Chen, Haiyang Yan, Ye Yuan, Jing Liu, Hua Han
Computer Vision
Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?
Xin Chen, Dongliang Xu, Cunhao Zhu et al. (+5 more)
As
vision-
language models (VLMs) are increasingly applied to medical AI, existing
benchmarks mainly focus on evaluating their diagnostic ability over given medical images and texts, implicitly assuming that standardized medical images, texts, or question-answer pairs are already prepared. However, this assumption does not hold when we apply VLMs in real clinical practice, where medical data is often raw, heterogeneous, and fragmented across different sources. In this paper, we study this missing step, i.e., raw medical data standardization. Specifically, models are given raw
dataset folders and evaluated on their ability to identify source formats, convert raw medical images into VLM-compatible visual inputs, extract relevant textual information, and organize the results into structured image-text pairs. To construct this Medical Data Standardization
Benchmark (MDS-Bench), we manually annotate 1,939 raw medical data standardization tasks covering diverse clinical practice, radiology modalities, annotation formats, and directory layouts. Extensive experiments show that even the best performing VLM, i.e., Gemini 3 Flash, achieves only a 48.6% end-to-end success rate. Our research highlights raw medical data standardization as a critical bottleneck for medical AI diagnosis in real practice.
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All authors Xin Chen, Dongliang Xu, Cunhao Zhu, Xudong Luo, Haoyang Lyu, Xiaoxiao Sun, Serena Yeung-Levy, Yue Yao
Computer Vision
From Open Loop to Closed Loop: A Test-Time Iterative Optimization Framework for Reference-Consistent Image Generation
Baixuan Zhao, Xinyu Zhang, Huayu Zheng et al. (+4 more)
While controllable image generation has made significant strides by incorporating visual reference conditions, existing methods predominantly operate as open-loop systems. They inject control signals in a strictly feed-forward manner, failing to guarantee strict fidelity to the reference due to the absence of active feedback and error correction mechanisms. To address this fundamental limitation, we propose a novel test-time iterative optimization framework that reformulates reference-consistent generation as a closed-loop dynamic tracking problem. By treating the pre-trained generative model as a control plant, our framework employs a sensor-controller architecture driven by a modified Proportional-Integral-Derivative (PID)
algorithm. This mechanism iteratively optimizes the latent control signals at test time based on the sensed discrepancy between the generated output and the reference target. Notably, this approach is entirely training-free, model-agnostic, and integrates seamlessly around existing
diffusion pipelines. Extensive evaluations across ID-preserving, pose-controlled, and depth-controlled generation tasks validate the universality of our method. Empirical results demonstrate improvements over computation-matched open-loop baselines, achieving relative performance gains of up to 25.36% for facial similarity, alongside spatial error reductions of up to 27.71% for pose
alignment and 28.50% for depth consistency. More broadly, this work offers a new conceptual perspective: it demonstrates that controllable generation can be effectively managed as a dynamic feedback system, bringing the rigorous principles of classical control theory into the optimization of generative models. Code is available at https://github.com/zzdrill/From-Open-Loop-to-Closed-Loop.
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All authors Baixuan Zhao, Xinyu Zhang, Huayu Zheng, Shuaicheng Liu, Xiongkuo Min, Guangtao Zhai, Xiaohong Liu
Computer Vision · Cross-listed from cs.RO
A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving
Argho Dey, Yunfei Yin, Swachha Ray et al. (+5 more)
Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded. In real driving conditions, camera observations are frequently corrupted by occlusion, motion blur, illumination change, and sensor noise, and when such degraded observations are aggregated indiscriminately over time, trajectory planning becomes unstable and collision risk rises for both the ego vehicle and surrounding road users. Recent Bird's-Eye-View (BEV) approaches unify perception and planning through a shared spatial representation, but most fuse temporal information across frames without assessing the reliability of the underlying observations. We present a Reliable Context-Aware and Temporal Planning framework for Autonomous Driving (RCT-AD) that explicitly models feature quality and temporal consistency to support safer, more consistent planning. A Reliable Context Awareness module scores per-frame reliability and selectively retains trustworthy features through a quality-gated First-In-Last-Out (FILO)
memory mechanism, reconstructing degraded observations from reliable historical context so that corrupted inputs do not destabilize the scene representation. A Temporal Trajectory Planner captures long-term dependencies and multi-
agent interactions to produce smoother, safety-aware trajectories, while a joint detection-and-
segmentation head injects semantic and motion cues into the shared BEV space to strengthen scene understanding. Experiments on the nuScenes autonomous driving
benchmark show that RCT-AD improves perception accuracy, motion prediction, and planning robustness over recent end-to-end baselines, achieving 61.5 nuScenes Detection Score, 52.9 mean Average Precision, and 52.3 mean Intersection over Union, while maintaining competitive computational efficiency suitable for real-time deployment.
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All authors Argho Dey, Yunfei Yin, Swachha Ray, Md Minhazul Islam, Zheng Yuan, Sijing Xiong, Hongyu Liu, Zhiqiu Huang
Computer Vision
TubeLite: Lightweight Multi-Actor Spatio-Temporal Action Detection
Ali Soltaninezhad, Melissa Cote, Alejandro Rico Espinosa et al. (+2 more)
Spatio-temporal action detection in
videos requires jointly localizing actors in space and identifying action boundaries over time. A common challenge is constructing temporally stable action tubes, as frame-level detectors often suffer from jitter, fragmentation, and imprecise temporal localization. Many recent approaches address this by introducing heavy spatio-temporal
transformers or optical-flow-based pipelines, leading to high computational cost and limited scalability. We propose TubeLite, a lightweight framework for spatio-temporal action detection that focuses on stable tube construction and boundary-aware temporal modeling. TubeLite represents each actor as a tube, defined as a sequence of bounding boxes associated with a single actor over time, and explicitly enforces temporal consistency at both the spatial and semantic levels. The method combines low-jitter actor detection, Gaussian-weighted actor feature extraction, efficient short-term temporal propagation, and a boundary-focused temporal prediction head, while avoiding optical flow and large-scale temporal
attention. Despite its compact design, TubeLite achieves strong
video-level localization performance. It improves
Video-mAP@0.5 by 4.5 and 7.1 percentage points over the best compared method on the MultiSports and UCF101-24
datasets, respectively, with substantially fewer parameters and floating-point operations than
transformer-based alternatives, demonstrating that effective spatio-temporal action detection can be obtained through principled, lightweight temporal modeling.
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All authors Ali Soltaninezhad, Melissa Cote, Alejandro Rico Espinosa, Tunai Porto Marques, Alexandra Branzan Albu
Computer Vision
Failing to See or Failing to Know? Attributing Errors in Vision-Language Models
Khang Nhat Hoang Vo, Artem Vazhentsev, Artem Shelmanov et al. (+2 more)
Vision-
language models (VLMs) can recognize entities in clear images yet still fail when answering questions that require factual knowledge beyond what is directly observable. Prior work has either examined individual failure modes in isolation or treated incorrect answers as monolithic, binary failures. We propose a tree-structured framework that organizes failures in knowledge-intensive visual question answering into model-specific operational outcomes. Across two
datasets and four VLMs, we observe consistent distributions of operational outcomes: some failures occur before entity recognition, while others persist after the relevant entity is recognized. Visual
token representations are most informative for recognition-related decisions.
Prompt hidden states predict answer success more effectively, although factual-access attribution remains difficult and exhibits only a weak signal. These pre-generation signals support attribution-guided routing to targeted interventions, including image repair, entity support, question rewriting, and factual evidence.
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All authors Khang Nhat Hoang Vo, Artem Vazhentsev, Artem Shelmanov, Timothy Baldwin, Yova Kementchedjhieva
Computer Vision
AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer
Yongwen Lai and Chaoqun Wang
Image-guided style transfer aims to apply the artistic characteristics of a style image to a content image while preserving its semantic structure and layout. Despite advances in
diffusion-based methods, existing approaches often face challenges in disentangling content and style, particularly when independently optimized adapters are naively combined, causing conflicts between adapters and limiting controllability over the content-style balance in inference. We further demonstrate that training-free structural guidance directly derived from the content image through the internal
attention of pre-trained model outperforms a dedicated content LoRA adapter in terms of structural fidelity and computational efficiency. Building on these observations, we propose AnyStyle, a streamlined framework for image-guided style transfer. The framework adopts a unified single-adapter paradigm for coherent style capture from the style image and incorporates training-free structural guidance from the content image, thus avoiding complex
entanglement between multiple adapters and improving controllability and stability. Extensive experiments show that our method delivers competitive quantitative performance and significantly improved perceptual quality. Code is available at https://github.com/Yvan1001/AnyStyle.
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Computer Vision
ICME 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing
Wei Sun, Weixia Zhang, Linhan Cao et al. (+30 more)
This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most
benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization. To address these limitations, we design two complementary tracks: Track 1 (Cross-Scenario Defect Detection) targets accurate defect detection, localization, and classification across diverse unseen production environments; Track 2 (Fine-Grained Severity Grading) requires assigning each detected defect an industry-standard severity level, including Acceptable, Marginal NG, NG, and Gross NG. We construct a large-scale industrial
dataset of high-resolution microscopic images spanning seven representative defect categories, comprising over 3,800 images with pixel-level instance annotations for Track 1 and over 2,600 images with severity-grade labels for Track 2. The challenge attracted 86 registered participants with 130 submissions; during the final testing phase, 21 teams submitted results and 12 teams provided models with technical reports. The resulting
benchmark, together with the diverse and effective solutions contributed by participating teams, sets a new standard for industrial defect analysis research.
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All authors Wei Sun, Weixia Zhang, Linhan Cao, Mingkai Lu, Xiongkuo Min, Xiaoping Zhang, Patrick Le Callet, Guangtao Zhai, Hongxing Chen, Wenqi Wu, Zhenhao Hu, Shanshan Lin, Guanjie Huang, Kai Xie, Rui Xin, Zilong Zhao, Runmin Cong, Ningjing Li, Siqi Ma, Yi Jin Ong, Tianfei Zhou, Shunzhou Wang, Zhiyang Chen, Hao Fang, Chen Zhang, Tze-Hsiang Tang, Dikai Li, Xianjin Wu, Avinash Kumar Sharma, Zhaoyang Wang, Haiyong Chen, Binyi Su, Atik Shahariar
Computer Vision
Video Generation Models Are Inherent Lighting Estimators
Ziqi Cai, Shuchen Weng, Kaiqi Liu et al. (+5 more)
Recovering dynamic environment maps from a single in-the-wild
video is crucial for photorealistic rendering, yet remains a challenge. Recent
video generation models can produce photorealistic scenes with complex lighting, possessing an inherent understanding of lighting. In this paper, we introduce V-LITE (
Video generation models are inherent lighting
estimators), a framework that unlocks this internal knowledge by reframing lighting estimation as a guided
video inpainting task. Inspired by VFX industry practices, we insert a synthetic chrome ball into the scene to compel the model to generate physically plausible reflections from the surrounding spatio-temporal context. To bridge the gap from LDR-native models to the HDR domain, we design an HDR-aware VAE and employ an efficient LoRA-based fine-tuning strategy. We then construct a mixed
dataset comprising high-fidelity HDR images to provide realistic HDR priors, and in-the-wild HDR
videos to provide dynamic spatio-temporal context. Extensive experiments demonstrate that V-LITE produces temporally coherent HDR environment maps, revealing that modern
video diffusion models are not merely synthesizers but also powerful, inherently capable
estimators of physical scene lighting.
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All authors Ziqi Cai, Shuchen Weng, Kaiqi Liu, Zifeng Wang, Zhiquan Zhang, Minggui Teng, Han Jiang, Boxin Shi
Computer Vision
GlaKG: A Biomarker-Centric Fundus Knowledge Graph for Explainable Glaucoma Diagnosis and Risk Assessment
Cheng Huang, Jia Zhang, Yi Jiang et al. (+7 more)
Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer little clinical interpretability. We present GlaKG, a biomarker-centric fundus knowledge graph that integrates structural biomarkers, clinically grounded rules, and image features to produce traceable
reasoning for glaucoma diagnosis and risk stratification. GlaKG encodes six entity types (Fundus Image, Optic Disc, Neural Rim, Pathology, Diagnosis, Risk Level), eight relation types, and 11 clinically validated rules into a unified graph, so that every prediction is accompanied by an explicit
reasoning chain linking biomarker evidence to activated clinical rules. To keep knowledge-based
reasoning strictly separate from label information, we adopt a post-processing fusion framework that combines ResNet50 image
embeddings with a normalized KG
reasoning-chain score via a tunable weight alpha, with all fitting confined to the training split. On a publicly available, AI-annotated fundus
dataset, GlaKG reaches F1 = 0.9953 for binary glaucoma classification and 0.930 accuracy with 0.922 weighted F1 for four-class risk stratification; we report openly that the
dataset's biomarker annotations are highly label-correlated, and therefore frame these figures as an upper bound attainable with clean structured biomarkers rather than as leakage-free image-only performance. Feature-importance analysis shows KG-derived and biomarker features contributing near-equally (51.1% vs. 48.9%), and the
reasoning chain flags borderline cases by exposing low chain scores rather than failing silently. GlaKG's central contribution is therefore a clinically auditable
reasoning framework that complements raw predictive performance by explicitly exposing the biomarker evidence and rule activations behind each decision.
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All authors Cheng Huang, Jia Zhang, Yi Jiang, Yang Liu, Karanjit Kooner, Yadi Liu, Tsengdar Lee, Yang Xie, Wenqi Shi, Guanghua Xiao
Computer Vision
DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval
Gwang-Ho Na, Ho-Joong Kim and Seong-Whan Lee
Zero-shot composed image
retrieval (ZS-CIR) aims to retrieve a target image from a
multimodal query consisting of a reference image and an edit text describing the desired modification. Recent ZS-CIR studies have relied on projection-based methods that map a reference image into pseudo-word
tokens in the text
embedding space. However, such methods require additional projection and re-encoding steps, increasing training complexity, reducing efficiency, and introducing a discrepancy between training and inference. In this paper, we propose DiCE-CIR, a direct composition learning method that predicts composed query representations by directly composing a reference image and an edit text. To enable scalable training without manually annotated triplets, we automatically construct compositional training samples from large-scale image-caption pairs using a large
language model. Based on these samples, we train a lightweight composition module with objectives that promote
alignment with the target, edit-consistent semantic transformation, and
retrieval discriminability. We conduct extensive experiments on ZS-CIR
benchmarks and show that DiCE-CIR achieves state-of-the-art performance on CIRCO and competitive performance on CIRR while maintaining high computational efficiency.
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Computer Vision
Targeted Structure Completion for Sparse-View 3D Reconstruction in Autonomous Driving
Guoqing Wang, Pin Tang, Xiangxuan Ren et al. (+2 more)
Reconstructing 3D scene structures from sparse, low-overlap observations remains a fundamental challenge in autonomous driving. Recent state-of-the-art frameworks achieve promising results by incorporating voxel-based Gaussians, but incur substantial computational redundancy due to a uniform volumetric processing strategy. To bridge the gap between the efficiency of pixel-based Gaussian methods and the structural completeness of voxel-based Gaussian approaches, we propose FocusGS, a simple yet effective framework that shifts the paradigm from global densification to targeted structural completion. Our central insight is that structural completion should be decoupled from deterministic regions, with computation concentrated exclusively on areas exhibiting
geometric ambiguity. Specifically, FocusGS addresses the localization challenge by deriving a 3D
Geometric Ambiguity
Manifold to accurately isolate localized areas prone to occlusion and high
geometric uncertainty. To overcome the subsequent
manifold completion challenge, we design a lightweight targeted structure completion module that selectively instantiates and optimizes continuous Gaussian queries strictly within this unstructured, sparse topological subspace. Extensive experiments demonstrate that FocusGS achieves a superior efficiency-quality trade-off, advancing state-of-the-art performance on driving-centric
benchmarks while naturally reducing the total number of Gaussians by ~74% and decreasing rendering time by ~34%.
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All authors Guoqing Wang, Pin Tang, Xiangxuan Ren, Liping Hou, Chao Ma
Computer Vision
Enhancing Video Physical Consistency via Role-aware Joint Training and Modality-decoupled Denoising
Guangting Zheng, Haojing Chen, Hao Li et al. (+6 more)
While modern
video diffusion models excel in visual fidelity, maintaining long-range physical consistency remains a formidable challenge. Conventional pixel-reconstruction objectives mainly focus on appearance details and often fail to capture the underlying dynamics of a scene. To mitigate this, recent efforts have integrated auxiliary modalities (e.g., optical flow) to introduce physics priors via joint training with
video appearance. However, these methods have three main limitations: (1) they do not distinguish the different motion patterns of different entity types; (2) joint modeling of visual and auxiliary modalities can cause capacity conflicts and weaken the pretrained visual prior; and (3) auxiliary modalities may accumulate errors during inference. To address these issues, we propose VPT, a fine-tuning framework for improving physical consistency in
video diffusion models. VPT introduces a role-aware signal that groups entities into
agents, controlled objects, passive objects, and background, so that different physical roles can be modeled more clearly. We further propose a modality-decoupled denoising strategy, where the visual and auxiliary channels are assigned independent noise levels. Together with a loss-weight decay strategy, this design makes auxiliary modalities serve as soft constraints rather than strong dependencies, mitigating recursive prediction errors during inference. We also introduce cross-step auto-guidance to further strengthen physical dynamics. Experiments show that VPT improves physical consistency while preserving visual quality, achieving relative gains of 39.4% in SA and 17.9% in PC on VideoPhy
benchmark over Wan2.1-T2V-1.3B, and consistent improvements on VideoPhy-2
benchmark. The project page is available at https://tom-zgt.github.io/VPT.
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All authors Guangting Zheng, Haojing Chen, Hao Li, Jingtao Zhang, Zhen Yang, Xiaosong Jia, Xue Yang, Shaofeng Zhang, Yanyong Zhang