Computer Vision
Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation
Kejia Liu, Haoyang Zhou, Ruoyu Xu et al. (+3 more)
Recent advances in cross-view geo-localization (CVGL) methods have shown strong potential for supporting unmanned aerial vehicle (
UAV)
navigation in GNSS-denied environments. However, existing work predominantly focuses on matching
UAV views to onboard map tiles, which introduces an inherent trade-off between accuracy and storage overhead, and overlooks the importance of the
UAV's heading during
navigation. Moreover, the substantial discrepancies and varying overlaps in cross-view scenarios have been insufficiently considered, limiting their generalization to real-world scenarios. In this paper, we present Bearing-
UAV, a purely
vision-driven cross-view
navigation method that jointly predicts
UAV absolute location and heading from neighboring features, enabling accurate, lightweight, and robust
navigation in the wild. Our method leverages global and local structural features and explicitly encodes relative spatial relationships, making it robust to cross-view variations, misalignment, and feature-sparse conditions. We also present Bearing-
UAV-90k, a multi-city
benchmark for evaluating cross-view localization and
navigation. Extensive experiments show encouraging results that Bearing-
UAV yields lower localization error than previous matching/
retrieval paradigm across diverse terrains. Our code and
dataset will be made publicly available.
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All authors Kejia Liu, Haoyang Zhou, Ruoyu Xu, Peicheng Wang, Mingli Song, Haofei Zhang
Computer Vision
OpenEarth-Agent: From Tool Calling to Tool Creation for Open-Environment Earth Observation
Sijie Zhao, Feng Liu, Xueliang Zhang et al. (+11 more)
Earth Observation (EO) is essential for perceiving dynamic land surface changes, yet deploying autonomous EO in open environments is hindered by the immense diversity of multi-source data and heterogeneous tasks. While remote sensing
agents have emerged to streamline EO workflows, existing tool-calling
agents are confined to closed environments. They rely on pre-defined tools and are restricted to narrow scope, limiting their generalization to the diverse data and tasks. To overcome these limitations, we introduce OpenEarth-
Agent, the first tool-creation
agent framework tailored for open-environment EO. Rather than calling predefined tools, OpenEarth-
Agent employs adaptive workflow planning and tool creation to generalize to unseen data and tasks. This adaptability is bolstered by an open-ended integration of multi-stage tools and cross-domain knowledge bases, enabling robust execution in the entire EO pipeline across multiple application domains. To comprehensively evaluate EO
agents in open environments, we propose OpenEarth-Bench, a novel
benchmark comprising 596 real-world, full-pipeline cases across seven application domains, explicitly designed to assess
agents' adaptive planning and tool creation capabilities. Only essential pre-trained model tools are provided in this
benchmark, devoid of any other predefined task-specific tools. Extensive experiments demonstrate that OpenEarth-
Agent successfully masters full-pipeline EO across multiple domains in the open environment. Notably, on the cross-
benchmark Earth-Bench, our tool-creating
agent equipped with 6 essential pre-trained models achieves performance comparable to tool-calling
agents relying on 104 specialized tools, and significantly outperforms them when provided with the complete toolset. In several cases, the created tools exhibit superior robustness to data anomalies compared to human-engineered counterparts.
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All authors Sijie Zhao, Feng Liu, Xueliang Zhang, Hao Chen, Xinyu Gu, Zhe Jiang, Fenghua Ling, Ben Fei, Wenlong Zhang, Junjue Wang, Weihao Xuan, Pengfeng Xiao, Naoto Yokoya, Lei Bai
Computer Vision
DA-VAE: Plug-in Latent Compression for Diffusion via Detail Alignment
Xin Cai, Zhiyuan You, Zhoutong Zhang et al. (+1 more)
Reducing
token count is crucial for efficient training and inference of latent
diffusion models, especially at high resolution. A common strategy is to build high-compression image tokenizers with more channels per
token. However, when trained only for reconstruction, high-dimensional latent spaces often lose meaningful structure, making
diffusion training harder. Existing methods address this with extra objectives such as semantic
alignment or selective dropout, but usually require costly
diffusion retraining. Pretrained
diffusion models, however, already exhibit a structured, lower-dimensional latent space; thus, a simpler idea is to expand the latent dimensionality while preserving this structure. We therefore propose Detail-Aligned VAE, which increases the compression ratio of a pretrained VAE with only lightweight adaptation of the pretrained
diffusion backbone. DA-VAE uses an explicit latent layout: the first $C$ channels come directly from the pretrained VAE at a base resolution, while an additional $D$ channels encode higher-resolution details. A simple detail-
alignment mechanism encourages the expanded latent space to retain the structure of the original one. With a warm-start fine-tuning strategy, our method enables $1024 \times 1024$ image generation with Stable
Diffusion 3.5 using only $32 \times 32$
tokens, $4\times$ fewer than the original model, within 5 H100-days. It further unlocks $2048 \times 2048$ generation with SD3.5, achieving a $6\times$ speedup while preserving image quality. We also validate the method and its design choices quantitatively on ImageNet.
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All authors Xin Cai, Zhiyuan You, Zhoutong Zhang, Tianfan Xue
Computer Vision
Biophysics-Enhanced Neural Representations for Patient-Specific Respiratory Motion Modeling
Jan Boysen, Hristina Uzunova, Heinz Handels et al. (+1 more)
A precise spatial delivery of the radiation dose is crucial for the treatment success in radiotherapy. In the lung and upper abdominal region, respiratory motion introduces significant treatment uncertainties, requiring special motion management techniques. To address this, respiratory motion models are commonly used to infer the patient-specific respiratory motion and target the dose more efficiently. In this work, we investigate the possibility of using implicit neural representations (INR) for surrogate-based motion modeling. Therefore, we propose physics-regularized implicit surrogate-based modeling for respiratory motion (PRISM-RM). Our new integrated respiratory motion model is free of a fixed reference breathing state. Unlike conventional pairwise registration techniques, our approach provides a trajectory-aware spatio-temporally continuous and diffeomorphic motion representation, improving generalization to extrapolation scenarios. We introduce biophysical constraints, ensuring physiologically plausible motion estimation across time beyond the training data. Our results show that our trajectory-aware approach performs on par in interpolation and improves the extrapolation ability compared to our initially proposed INR-based approach. Compared to sequential registration-based approaches both our approaches perform equally well in interpolation, but underperform in extrapolation scenarios. However, the methodical features of INRs make them particularly effective for respiratory motion modeling, and with their performance steadily improving, they demonstrate strong potential for advancing this field.
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All authors Jan Boysen, Hristina Uzunova, Heinz Handels, Jan Ehrhardt
Computer Vision
GenSpan: Generation-Calibrated Motion Span Priors for Multi-Verb Video Corpus Moment Retrieval
Yunzhuo Sun, Xinyue Liu, Yanyang Li et al. (+4 more)
Video Corpus Moment
Retrieval (VCMR) aims to retrieve both the correct
video and its temporal segment corresponding to a natural-
language query, a task that is especially challenging for multi-verb queries where temporal action ordering is critical. Existing approaches often rely solely on text or static images and struggle to capture implicit motion dynamics, leading to
retrieval errors and temporal misalignment. We propose GenSpan, a generation-calibrated VCMR framework that constructs short auxiliary
videos from LLM-selected subtitle cues and decomposed sub-events, using these as temporal priors rather than direct
retrieval targets. A
token selector filters candidate-
video features aligned with generated motion, and a bidirectional state-space model efficiently predicts
video-moment tuples. Experiments on TVR and ActivityNet-Captions demonstrate that GenSpan improves corpus-level
retrieval and moment localization, particularly for complex multi-action queries, while reducing computational cost compared to state-of-the-art
multimodal baselines.
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All authors Yunzhuo Sun, Xinyue Liu, Yanyang Li, Nanding Wu, Linlin Zong, Xianchao Zhang, Wenxin Liang
Computer Vision
StreamingClaw Technical Report
Jiawei Chen, Zhe Chen, Chaoqun Du et al. (+21 more)
Emerging applications such as
embodied intelligence, AI hardware, autonomous driving, and intelligent cockpits rely on a real-time perception-decision-action closed loop, posing stringent challenges for streaming
video understanding. However, current
agents mostly suffer from fragmented capabilities, such as supporting only offline
video understanding, lacking long-term
multimodal memory mechanisms, or struggling to achieve real-time
reasoning and proactive interaction under streaming input. These shortcomings have become a key bottleneck for preventing
agents from sustaining perception, making real-time decisions, and executing closed-loop actions in complex real-world environments, constraining their deployment and potential in dynamic, open physical worlds. To alleviate these issues, we propose StreamingClaw, a unified
agent framework for streaming
video understanding and
embodied intelligence. Beyond maintaining full compatibility with the OpenClaw framework, it natively supports real-time,
multimodal streaming interactions. StreamingClaw integrates five core capabilities: (1) It supports real-time streaming
reasoning. (2) It supports
reasoning about future events and proactive interaction under the online
evolution of interaction objectives. (3) It supports
multimodal long-term
memory storage, hierarchical
memory evolution, efficient
memory retrieval, and
memory sharing across multiple
agents. (4) It supports a closed loop of perception-decision-action. In addition to conventional tools and skills, it also provides streaming tools and action-centric skills tailored for real-world physical environments. (5) It is compatible with the OpenClaw framework, allowing it to leverage the resources and support of the open-source community.
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All authors Jiawei Chen, Zhe Chen, Chaoqun Du, Maokui He, Wei He, Hengtao Li, Qizhen Li, Zide Liu, Hao Ma, Xuhao Pan, Chang Ren, Xudong Rao, Xintian Shen, Chenfeng Wang, Tao Wei, Chengjun Yu, Pengfei Yu, Shengyu Yao, Chunpeng Zhou, Kun Zhan, Lihao Zheng, Pan Zhou, Xuhan Zhu, Yufei Zheng
Computer Vision
FreeArtGS: Articulated Gaussian Splatting Under Free-moving Scenario
Hang Dai, Hongwei Fan, Han Zhang et al. (+3 more)
The increasing demand for augmented reality and robotics is driving the need for articulated object reconstruction with high scalability. However, existing settings for reconstructing from discrete articulation states or casual
monocular videos require non-trivial axis
alignment or suffer from insufficient coverage, limiting their applicability. In this paper, we introduce FreeArtGS, a novel method for reconstructing articulated objects under free-moving scenario, a new setting with a simple setup and high scalability. FreeArtGS combines free-moving part
segmentation with joint estimation and end-to-end optimization, taking only a
monocular RGB-D
video as input. By optimizing with the priors from off-the-shelf point-tracking and feature models, the free-moving part
segmentation module identifies rigid parts from relative motion under unconstrained capture. The joint estimation module calibrates the unified object-to-camera poses and recovers joint type and axis robustly from part
segmentation. Finally, 3DGS-based end-to-end optimization is implemented to jointly reconstruct visual textures, geometry, and joint angles of the articulated object. We conduct experiments on two
benchmarks and real-world free-moving articulated objects. Experimental results demonstrate that FreeArtGS consistently excels in reconstructing free-moving articulated objects and remains highly competitive in previous reconstruction settings, proving itself a practical and effective solution for realistic asset generation. The project page is available at: https://freeartgs.github.io/
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All authors Hang Dai, Hongwei Fan, Han Zhang, Duojin Wu, Jiyao Zhang, Hao Dong
Computer Vision
Principled Steering via Null-space Projection for Jailbreak Defense in Vision-Language Models
Xingyu Zhu, Beier Zhu, Shuo Wang et al. (+4 more)
As
vision-
language models (VLMs) are increasingly deployed in open-world scenarios, they can be easily induced by visual jailbreak attacks to generate harmful content, posing serious risks to model safety and trustworthy usage. Recent activation steering methods inject directional vectors into model activations during inference to induce refusal behaviors and have demonstrated effectiveness. However, a steering vector may both enhance refusal ability and cause over-refusal, thereby degrading model performance on benign inputs. Moreover, due to the lack of theoretical interpretability, these methods still suffer from limited robustness and effectiveness. To better balance safety and utility, we propose NullSteer, a null-space projected activation defense framework. Our method constructs refusal directions within model activations through a linear transformation: it maintains zero perturbation within the benign subspace while dynamically inducing refusal along potentially harmful directions, thereby theoretically achieving safety enhancement without impairing the model's general capabilities. Extensive experiments show that NullSteer significantly reduces harmful outputs under various jailbreak attacks (average ASR reduction over 15 percent on MiniGPT-4) while maintaining comparable performance to the original model on general
benchmarks.
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All authors Xingyu Zhu, Beier Zhu, Shuo Wang, Junfeng Fang, Kesen Zhao, Hanwang Zhang, Xiangnan He
Computer Vision
P-Flow: Prompting Visual Effects Generation
Rui Zhao and Mike Zheng Shou
Recent advancements in
video generation models have significantly improved their ability to follow text
prompts. However, the customization of dynamic visual effects, defined as temporally evolving and appearance-driven visual phenomena like object crushing or explosion, remains underexplored. Prior works on motion customization or control mainly focus on low-level motions of the subject or camera, which can be guided using explicit control signals such as motion trajectories. In contrast, dynamic visual effects involve higher-level semantics that are more naturally suited for control via text
prompts. However, it is hard and time-consuming for humans to craft a single
prompt that accurately specifies these effects, as they require complex temporal
reasoning and iterative refinement over time. To address this challenge, we propose P-Flow, a novel training-free framework for customizing dynamic visual effects in
video generation without modifying the underlying model. By leveraging the semantic and temporal
reasoning capabilities of
vision-
language models, P-Flow performs test-time
prompt optimization, refining
prompts based on the discrepancy between the visual effects of the reference
video and the generated output. Through iterative refinement, the
prompts evolve to better induce the desired dynamic effect in novel scenes. Experiments demonstrate that P-Flow achieves high-fidelity and diverse visual effect customization and outperforms other models on both text-to-
video and image-to-
video generation tasks. Code is available at https://github.com/showlab/P-Flow.
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Computer Vision
Adapting Point Cloud Analysis via Multimodal Bayesian Distribution Learning
Xingyu Zhu, Liang Yi, Shuo Wang et al. (+4 more)
Multimodal 3D
vision-
language models show strong generalization across diverse 3D tasks, but their performance still degrades notably under domain shifts. This has motivated recent studies on test-time adaptation (TTA), which enables models to adapt online using test-time data. Among existing TTA methods, cache-based mechanisms are widely adopted for leveraging previously observed samples in online prediction refinement. However, they store only limited historical information, leading to progressive information loss as the test stream evolves. In addition, their prediction logits are fused heuristically, making adaptation unstable. To address these limitations, we propose BayesMM, a
Multimodal Bayesian Distribution Learning framework for test-time point cloud analysis. BayesMM models textual priors and streaming visual features of each class as Gaussian distributions: textual parameters are derived from semantic
prompts, while visual parameters are updated online with arriving samples. The two modalities are fused via
Bayesian model averaging, which automatically adjusts their contributions based on posterior evidence, yielding a unified prediction that adapts continually to evolving test-time data without training. Extensive experiments on multiple point cloud
benchmarks demonstrate that BayesMM maintains robustness under distributional shifts, yielding over 4% average improvement.
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All authors Xingyu Zhu, Liang Yi, Shuo Wang, Wenbo Zhu, Yonglinag Wu, Beier Zhu, Hanwang Zhang
Computer Vision
SpatialBoost: Enhancing Visual Representation through Language-Guided Reasoning
Byungwoo Jeon, Dongyoung Kim, Huiwon Jang et al. (+2 more)
Despite the remarkable success of large-scale pre-trained image representation models (i.e.,
vision encoders) across various
vision tasks, they are predominantly trained on 2D image data and therefore often fail to capture 3D spatial relationships between objects and backgrounds in the real world, constraining their effectiveness in many downstream applications. To address this, we propose SpatialBoost, a scalable framework that enhances the spatial awareness of existing pre-trained
vision encoders by injecting 3D spatial knowledge expressed in linguistic descriptions. The core idea involves converting dense 3D spatial information from 2D images into linguistic expressions, which is then used to inject such spatial knowledge into
vision encoders through a Large
Language Model (LLM). To this end, we adopt a multi-turn Chain-of-Thought (CoT)
reasoning process that progressively incorporates dense spatial knowledge and builds hierarchical spatial understanding. To validate effectiveness, we adapt SpatialBoost to state-of-the-art
vision encoders such as DINOv3, and evaluate its performance gains on a wide range of
benchmarks requiring both 3D perception and general
vision abilities. For instance, SpatialBoost improves DINOv3 performance from 55.9 to 59.7 mIoU on ADE20K, achieving state-of-the-art performance with 3.8% gain over the pre-trained DINOv3.
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All authors Byungwoo Jeon, Dongyoung Kim, Huiwon Jang, Insoo Kim, Jinwoo Shin
Computer Vision
FontCrafter: High-Fidelity Element-Driven Artistic Font Creation with Visual In-Context Generation
Wuyang Luo, Chengkai Tan, Chang Ge et al. (+3 more)
Artistic font generation aims to synthesize stylized glyphs based on a reference style. However, existing approaches suffer from limited style diversity and coarse control. In this work, we explore the potential of element-driven artistic font generation. Elements are the fundamental visual units of a font, serving as reference images for the desired style. Conceptually, we categorize elements into object elements (e.g., flowers or stones) with distinct structures and amorphous elements (e.g., flames or clouds) with unstructured textures. We introduce FontCrafter, an element-driven framework for font creation, and construct a large-scale
dataset, ElementFont, which contains diverse element types and high-quality glyph images. However, achieving high-fidelity reconstruction of both texture and structure of reference elements remains challenging. To address this, we propose an in-context generation strategy that treats element images as visual context and uses an inpainting model to transfer element styles into glyph regions at the pixel level. To further control glyph shapes, we design a lightweight Context-aware Mask Adapter (CMA) that injects shape information. Moreover, a training-free
attention redirection mechanism enables region-aware style control and suppresses stroke
hallucination. In addition, edge repainting is applied to make boundaries more natural. Extensive experiments demonstrate that FontCrafter achieves strong zero-shot generation performance, particularly in preserving structural and textural fidelity, while also supporting flexible controls such as style mixture.
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All authors Wuyang Luo, Chengkai Tan, Chang Ge, Binye Hong, Su Yang, Yongjiu Ma
Computer Vision
Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models
Hayeon Kim, Ji Ha Jang, Junghun James Kim et al. (+1 more)
While
Vision-
Language Models (VLMs) have achieved remarkable performance, their Euclidean
embeddings remain limited in capturing hierarchical relationships such as part-to-whole or parent-child structures, and often face challenges in multi-object compositional scenarios.
Hyperbolic VLMs mitigate this issue by better preserving hierarchical structures and modeling part-whole relations (i.e., whole scene and its part images) through entailment. However, existing approaches do not model that each part has a different level of semantic representativeness to the whole. We propose UNcertainty-guided Compositional
Hyperbolic Alignment (UNCHA) for enhancing
hyperbolic VLMs. UNCHA models part-to-whole semantic representativeness with
hyperbolic uncertainty, by assigning lower uncertainty to more representative parts and higher uncertainty to less representative ones for the whole scene. This representativeness is then incorporated into the contrastive objective with uncertainty-guided weights. Finally, the uncertainty is further calibrated with an entailment loss regularized by
entropy-based term. With the proposed losses, UNCHA learns
hyperbolic embeddings with more accurate part-whole ordering, capturing the underlying compositional structure in an image and improving its understanding of complex multi-object scenes. UNCHA achieves state-of-the-art performance on zero-shot classification,
retrieval, and multi-label classification
benchmarks. Our code and models are available at: https://github.com/jeeit17/UNCHA.git.
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All authors Hayeon Kim, Ji Ha Jang, Junghun James Kim, Se Young Chun
Computer Vision
DTVI: Dual-Stage Textual and Visual Intervention for Safe Text-to-Image Generation
Binhong Tan, Zhaoxin Wang and Handing Wang
Text-to-Image (T2I)
diffusion models have demonstrated strong generation ability, but their potential to generate unsafe content raises significant safety concerns. Existing inference-time defense methods typically perform category-agnostic
token-level intervention in the text
embedding space, which fails to capture malicious semantics distributed across the full
token sequence and remains vulnerable to
adversarial prompts. In this paper, we propose DTVI, a dual-stage inference-time defense framework for safe T2I generation. Unlike existing methods that intervene on specific
token embeddings, our method introduces category-aware sequence-level intervention on the full
prompt embedding to better capture distributed malicious semantics, and further attenuates the remaining unsafe influences during the visual generation stage. Experimental results on real-world unsafe
prompts,
adversarial prompts, and multiple harmful categories show that our method achieves effective and robust defense while preserving reasonable generation quality on benign
prompts, obtaining an average Defense Success Rate (DSR) of 94.43% across sexual-category
benchmarks and 88.56 across seven unsafe categories, while maintaining generation quality on benign
prompts.
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Computer Vision
GTSR: Subsurface Scattering Awared 3D Gaussians for Translucent Surface Reconstruction
Youwen Yuan and Xi Zhao
Reconstructing translucent objects from multi-view images is a difficult problem. Previously, researchers have used differentiable path tracing and the neural implicit field, which require relatively large computational costs. Recently, many works have achieved good reconstruction results for opaque objects based on a 3DGS pipeline with much higher efficiency. However, such methods have difficulty dealing with translucent objects, because they do not consider the optical properties of translucent objects. In this paper, we propose a novel 3DGS-based pipeline (GTSR) to reconstruct the surface geometry of translucent objects. GTSR combines two sets of Gaussians, surface and interior Gaussians, which are used to model the surface and scattering color when lights pass translucent objects. To render the appearance of translucent objects, we introduce a method that uses the Fresnel term to blend two sets of Gaussians. Furthermore, to improve the reconstructed details of non-contour areas, we introduce the Disney BSDF model with deferred rendering to enhance constraints of the normal and depth. Experimental results demonstrate that our method outperforms baseline reconstruction methods on the NeuralTO Syn
dataset while showing great real-time rendering performance. We also extend the
dataset with new translucent objects of varying material properties and demonstrate our method can adapt to different translucent materials.
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Computer Vision
Tuning Real-World Image Restoration at Inference: A Test-Time Scaling Paradigm for Flow Matching Models
Purui Bai, Junxian Duan, Pin Wang et al. (+4 more)
Although
diffusion-based real-world image restoration (Real-IR) has achieved remarkable progress, efficiently leveraging ultra-large-scale pre-trained text-to-image (T2I) models and fully exploiting their potential remain significant challenges. To address this issue, we propose ResFlow-Tuner, an image restoration framework based on the state-of-the-art flow matching model, FLUX.1-dev, which integrates unified multi-modal fusion (UMMF) with test-time scaling (TTS) to achieve unprecedented restoration performance. Our approach fully leverages the advantages of the Multi-Modal
Diffusion Transformer (MM-DiT) architecture by encoding multi-modal conditions into a unified sequence that guides the synthesis of high-quality images. Furthermore, we introduce a training-free test-time scaling paradigm tailored for image restoration. During inference, this technique dynamically steers the denoising direction through feedback from a reward model (RM), thereby achieving significant performance gains with controllable computational overhead. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple standard
benchmarks. This work not only validates the powerful capabilities of the flow matching model in low-level
vision tasks but, more importantly, proposes a novel and efficient inference-time scaling paradigm suitable for large pre-trained models.
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All authors Purui Bai, Junxian Duan, Pin Wang, Jinhua Hao, Ming Sun, Chao Zhou, Huaibo Huang
Computer Vision
6D Robotic OCT Scanning of Curved Tissue Surfaces
Suresh Guttikonda, Maximilian Neidhardt, Vidas Raudonis et al. (+1 more)
Optical coherence
tomography (OCT) is a non-invasive volumetric imaging modality with high spatial and temporal resolution. For imaging larger tissue structures, OCT probes need to be moved to scan the respective area. For handheld scanning, stitching of the acquired OCT volumes requires overlap to register the images. For robotic scanning and stitching, a typical approach is to restrict the motion to translations, as this avoids a full hand-eye calibration, which is complicated by the small field of view of most OCT probes. However, stitching by registration or by translational scanning are limited when curved tissue surfaces need to be scanned. We propose a marker for full six-dimensional hand-eye calibration of a
robot mounted OCT probe. We show that the calibration results in highly repeatable estimates of the transformation. Moreover, we evaluate robotic scanning of two phantom surfaces to demonstrate that the proposed calibration allows for consistent scanning of large, curved tissue surfaces. As the proposed approach is not relying on image registration, it does not suffer from a potential accumulation of errors along a scan path. We also illustrate the improvement compared to conventional 3D-translational robotic scanning.
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All authors Suresh Guttikonda, Maximilian Neidhardt, Vidas Raudonis, Alexander Schlaefer
Computer Vision
SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation
Duy D. Nguyen and Phat T. Tran-Truong
The advent of
Transformer and Mamba-based architectures has significantly advanced 3D medical image
segmentation by enabling global contextual modeling, a capability traditionally limited in Convolutional Neural Networks (CNNs). However, state-of-the-art
Transformer models often entail substantial computational complexity and parameter counts, which is particularly prohibitive for volumetric data and further exacerbated by the limited availability of annotated medical imaging
datasets. To address these limitations, this work introduces SegMaFormer, a lightweight hybrid architecture that synergizes Mamba and
Transformer modules within a hierarchical volumetric encoder for efficient long-range dependency modeling. The model strategically employs Mamba-based layers in early, high-resolution stages to reduce computational overhead while capturing essential spatial context, and reserves self-
attention mechanisms for later, lower-resolution stages to refine feature representation. This design is augmented with generalized rotary position
embeddings to enhance spatial awareness. Despite its compact structure, SegMaFormer achieves competitive performance on three public
benchmarks (Synapse, BraTS, and ACDC), matching the Dice coefficient of significantly larger models. Empirically, our approach reduces parameters by up to 75x and substantially decreases FLOPs compared to current state-of-the-art models, establishing an efficient and high-performing solution for 3D medical image
segmentation.
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Computer Vision
STENet: Superpixel Token Enhancing Network for RGB-D Salient Object Detection
Jianlin Chen, Gongyang Li, Zhijiang Zhang et al. (+2 more)
Transformer-based methods for RGB-D Salient Object Detection (SOD) have gained significant interest, owing to the
transformer's exceptional capacity to capture long-range pixel dependencies. Nevertheless, current RGB-D SOD methods face challenges, such as the quadratic complexity of the
attention mechanism and the limited local detail extraction. To overcome these limitations, we propose a novel Superpixel
Token Enhancing Network (STENet), which introduces superpixels into cross-modal interaction. STENet follows the two-stream encoder-decoder structure. Its cores are two tailored superpixel-driven cross-modal interaction modules, responsible for global and local feature enhancement. Specifically, we update the superpixel generation method by expanding the neighborhood range of each superpixel, allowing for flexible transformation between pixels and superpixels. With the updated superpixel generation method, we first propose the Superpixel
Attention Global Enhancing Module to model the global pixel-to-superpixel relationship rather than the traditional global pixel-to-pixel relationship, which can capture region-level information and reduce computational complexity. We also propose the Superpixel
Attention Local Refining Module, which leverages pixel similarity within superpixels to filter out a subset of pixels (i.e., local pixels) and then performs feature enhancement on these local pixels, thereby capturing concerned local details. Furthermore, we fuse the globally and locally enhanced features along with the cross-scale features to achieve comprehensive feature representation. Experiments on seven RGB-D SOD
datasets reveal that our STENet achieves competitive performance compared to state-of-the-art methods. The code and results of our method are available at https://github.com/Mark9010/STENet.
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All authors Jianlin Chen, Gongyang Li, Zhijiang Zhang, Liang Chang, Dan Zeng
Computer Vision
LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving
Nour Alhuda Albashir, Lars Pernickel, Danial Hamoud et al. (+2 more)
Autonomous vehicles face major perception and
navigation challenges in adverse weather such as rain, fog, and snow, which degrade the performance of
LiDAR, RADAR, and RGB camera sensors. While each sensor type offers unique strengths, such as RADAR robustness in poor visibility and
LiDAR precision in clear conditions, they also suffer distinct limitations when exposed to environmental obstructions. This study proposes LRC-WeatherNet, a novel multi-sensor fusion framework that integrates
LiDAR, RADAR, and camera data for real-time classification of weather conditions. By employing both early fusion using a unified Bird's Eye View representation and mid-level gated fusion of modality-specific feature maps, our approach adapts to the varying reliability of each sensor under changing weather. Evaluated on the extensive MSU-4S
dataset covering nine weather types, LRC-WeatherNet achieves superior classification performance and computational efficiency, significantly outperforming unimodal baselines in adverse conditions. This work is the first to combine all three modalities for robust, real-time weather classification in autonomous driving. We release our trained models and source code in https://github.com/nouralhudaalbashir/LRC-WeatherNet.
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All authors Nour Alhuda Albashir, Lars Pernickel, Danial Hamoud, Idriss Gouigah, Eren Erdal Aksoy
Computer Vision
Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model
SII-GAIR, Sand.ai: Ethan Chern, Hansi Teng et al. (+41 more)
We present daVinci-MagiHuman, an open-source audio-
video generative foundation model for human-centric generation. daVinci-MagiHuman jointly generates synchronized
video and audio using a single-stream
Transformer that processes text,
video, and audio within a unified
token sequence via self-
attention only. This single-stream design avoids the complexity of multi-stream or cross-
attention architectures while remaining easy to optimize with standard training and inference infrastructure. The model is particularly strong in human-centric scenarios, producing expressive facial performance, natural
speech-expression coordination, realistic body motion, and precise audio-
video synchronization. It supports
multilingual spoken generation across Chinese (Mandarin and Cantonese), English, Japanese, Korean, German, and French. For efficient inference, we combine the single-stream backbone with model
distillation, latent-space super-resolution, and a Turbo VAE decoder, enabling generation of a 5-second 256p
video in 2 seconds on a single H100 GPU. In automatic evaluation, daVinci-MagiHuman achieves the highest visual quality and text
alignment among leading open models, along with the lowest word error rate (14.60%) for
speech intelligibility. In pairwise human evaluation, it achieves win rates of 80.0% against Ovi 1.1 and 60.9% against LTX 2.3 over 2000 comparisons. We open-source the complete model stack, including the base model, the distilled model, the super-resolution model, and the inference codebase.
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All authors SII-GAIR, Sand.ai: Ethan Chern, Hansi Teng, Hanwen Sun, Hao Wang, Hong Pan, Hongyu Jia, Jiadi Su, Jin Li, Junjie Yu, Lijie Liu, Lingzhi Li, Lyumanshan Ye, Min Hu, Qiangang Wang, Quanwei Qi, Steffi Chern, Tao Bu, Taoran Wang, Teren Xu, Tianning Zhang, Tiantian Mi, Weixian Xu, Wenqiang Zhang, Wentai Zhang, Xianping Yi, Xiaojie Cai, Xiaoyang Kang, Yan Ma, Yixiu Liu, Yunbo Zhang, Yunpeng Huang, Yutong Lin, Zewei Tao, Zhaoliang Liu, Zheng Zhang, Zhiyao Cen, Zhixuan Yu, Zhongshu Wang, Zhulin Hu, Zijin Zhou, Zinan Guo, Yue Cao, Pengfei Liu
Computer Vision
GeoFusion-CAD: Structure-Aware Diffusion with Geometric State Space for Parametric 3D Design
Xiaolei Zhou, Chuangjie Fang, Jie Wu et al. (+3 more)
Parametric Computer-Aided Design (CAD) is fundamental to modern 3D modeling, yet existing methods struggle to generate long command sequences, especially under complex
geometric and topological dependencies.
Transformer-based architectures dominate CAD sequence generation due to their strong dependency modeling, but their quadratic
attention cost and limited context windowing hinder scalability to long programs. We propose GeoFusion-CAD, an end-to-end
diffusion framework for scalable and structure-aware generation. Our proposal encodes CAD programs as hierarchical trees, jointly capturing geometry and
topology within a state-space
diffusion process. Specifically, a lightweight C-Mamba block models long-range structural dependencies through selective state transitions, enabling coherent generation across extended command sequences. To support long-sequence evaluation, we introduce DeepCAD-240, an extended
benchmark that increases the sequence length ranging from 40 to 240 while preserving sketch-extrusion semantics from the ABC
dataset. Extensive experiments demonstrate that GeoFusion-CAD achieves superior performance on both short and long command ranges, maintaining high
geometric fidelity and topological consistency where
Transformer-based models degrade. Our approach sets new state-of-the-art scores for long-sequence parametric CAD generation, establishing a scalable foundation for next-generation CAD modeling systems. Code and
datasets are available at GitHub.
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All authors Xiaolei Zhou, Chuangjie Fang, Jie Wu, Jingyi Yang, Boyi Lin, Jianwei Zheng
Computer Vision
BHDD: A Burmese Handwritten Digit Dataset
Swan Htet Aung, Hein Htet, Htoo Say Wah Khaing et al. (+1 more)
We introduce the Burmese Handwritten Digit
Dataset (BHDD), a collection of 87,561 grayscale images of handwritten Burmese digits in ten classes. Each image is 28x28 pixels, following the MNIST format. The training set has 60,000 samples split evenly across classes; the test set has 27,561 samples with class frequencies as they arose during collection. Over 150 people of different ages and backgrounds contributed samples. We analyze the
dataset's class distribution, pixel statistics, and morphological variation, and identify digit pairs that are easily confused due to the round shapes of the Myanmar script. Simple baselines (an MLP, a two-layer CNN, and an improved CNN with batch normalization and augmentation) reach 99.40%, 99.75%, and 99.83% test accuracy respectively. BHDD is available under CC BY-SA 4.0 at https://github.com/baseresearch/BHDD
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All authors Swan Htet Aung, Hein Htet, Htoo Say Wah Khaing, Thuya Myo Nyunt
Computer Vision
Unified Spatiotemporal Token Compression for Video-LLMs at Ultra-Low Retention
Junhao Du, Jialong Xue, Anqi Li et al. (+2 more)
Video large
language models (
Video-LLMs) face high computational costs due to large volumes of visual
tokens. Existing
token compression methods typically adopt a two-stage spatiotemporal compression strategy, relying on stage-specific metrics and an implicit assumption of spatiotemporal separability. Under extremely low retention ratios, however, such approaches often result in unbalanced allocation and loss of visual evidence essential for question answering. We reformulate
token compression as a spatiotemporal allocation task within a global
token retention pool. We propose a unified selection mechanism that integrates
attention weights and semantic similarity to globally select
tokens with high contribution and low redundancy. Unselected
tokens are merged via clustering and refilled, preserving information integrity. Inside the LLM, we further introduce text-aware merging to perform secondary compression based on query relevance. Without requiring retraining, our method serves as a plug-and-play module compatible with existing
Video-LLMs. Experiments show that retaining only about 2% of visual
tokens preserves 90.1% of baseline performance across multiple
benchmarks, while reducing FLOPs to roughly 2.6%. These benefits generalize across diverse backbones, decreasing end-to-end inference latency and
memory consumption. Our unified spatiotemporal
token compression strategy establishes the state-of-the-art in
video understanding under ultra-low
token retention.
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All authors Junhao Du, Jialong Xue, Anqi Li, Jincheng Dai, Guo Lu
Computer Vision
Group3D: MLLM-Driven Semantic Grouping for Open-Vocabulary 3D Object Detection
Youbin Kim, Jinho Park, Hogun Park et al. (+1 more)
Open-vocabulary 3D object detection aims to localize and recognize objects beyond a fixed training taxonomy. In multi-view RGB settings, recent approaches often decouple geometry-based instance construction from semantic labeling, generating class-agnostic fragments and assigning open-vocabulary categories post hoc. While flexible, such decoupling leaves instance construction governed primarily by
geometric consistency, without semantic constraints during merging. When
geometric evidence is view-dependent and incomplete, this geometry-only merging can lead to irreversible association errors, including over-merging of distinct objects or fragmentation of a single instance. We propose Group3D, a multi-view open-vocabulary 3D detection framework that integrates semantic constraints directly into the instance construction process. Group3D maintains a scene-adaptive vocabulary derived from a
multimodal large
language model (MLLM) and organizes it into semantic compatibility groups that encode plausible cross-view category equivalence. These groups act as merge-time constraints: 3D fragments are associated only when they satisfy both semantic compatibility and
geometric consistency. This semantically gated merging mitigates geometry-driven over-merging while absorbing multi-view category variability. Group3D supports both pose-known and pose-free settings, relying only on RGB observations. Experiments on ScanNet and ARKitScenes demonstrate that Group3D achieves state-of-the-art performance in multi-view open-vocabulary 3D detection, while exhibiting strong generalization in zero-shot scenarios. The project page is available at https://ubin108.github.io/Group3D/.
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All authors Youbin Kim, Jinho Park, Hogun Park, Eunbyung Park
Computer Vision
GeoFlow: Real-Time Fine-Grained Cross-View Geolocalization via Iterative Flow Prediction
Ayesh Abu Lehyeh, Xiaohan Zhang, Ahmad Arrabi et al. (+3 more)
Accurate and fast localization is vital for safe autonomous
navigation in GPS-denied areas. Fine-Grained Cross-View Geolocalization (FG-CVG) aims to estimate the precise 2-Degree-of-Freedom (2-DoF) location of a ground image relative to a satellite image. However, current methods force a difficult trade-off, with high-accuracy models being slow for real-time use. In this paper, we introduce GeoFlow, a new approach that offers a lightweight and highly efficient framework that breaks this accuracy-speed trade-off. Our technique learns a direct probabilistic mapping, predicting the displacement (in distance and direction) required to correct any given location hypothesis. This is complemented by our novel inference
algorithm, Iterative Refinement Sampling (IRS). Instead of trusting a single prediction, IRS refines a population of hypotheses, allowing them to iteratively 'flow' from random starting points to a robust, converged consensus. Even its iterative nature, this approach offers flexible inference-time scaling, allowing a direct trade-off between performance and computation without any re-training. Experiments on the KITTI and VIGOR
datasets show that GeoFlow achieves state-of-the-art efficiency, running at real-time speeds of 29 FPS while maintaining competitive localization accuracy. This work opens a new path for the development of practical real-time geolocalization systems.
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All authors Ayesh Abu Lehyeh, Xiaohan Zhang, Ahmad Arrabi, Waqas Sultani, Chen Chen, Safwan Wshah
Computer Vision
FeatDistill: A Feature Distillation Enhanced Multi-Expert Ensemble Framework for Robust AI-generated Image Detection
Zhilin Tu, Kemou Li, Fengpeng Li et al. (+3 more)
The rapid iteration and widespread dissemination of deepfake technology have posed severe challenges to information security, making robust and generalizable detection of AI-generated forged images increasingly important. In this paper, we propose FeatDistill, an AI-generated image detection framework that integrates feature
distillation with a multi-expert ensemble, developed for the NTIRE Challenge on Robust AI-Generated Image Detection in the Wild. The framework explicitly targets three practical bottlenecks in real-world forensics: degradation interference, insufficient feature representation, and limited generalization. Concretely, we build a four-backbone
Vision Transformer (ViT) ensemble composed of CLIP and SigLIP variants to capture complementary forensic cues. To improve data coverage, we expand the training set and introduce comprehensive degradation modeling, which exposes the detector to diverse quality variations and synthesis artifacts commonly encountered in unconstrained scenarios. We further adopt a two-stage training paradigm: the model is first optimized with a standard binary classification objective, then refined by dense feature-level self-
distillation for representation
alignment. This design effectively mitigates overfitting and enhances semantic consistency of learned features. At inference time, the final prediction is obtained by averaging the probabilities from four independently trained experts, yielding stable and reliable decisions across unseen generators and complex degradations. Despite the ensemble design, the framework remains efficient, requiring only about 10 GB peak GPU
memory. Extensive evaluations in the NTIRE challenge setting demonstrate that FeatDistill achieves strong robustness and generalization under diverse “in-the-wild” conditions, offering an effective and practical solution for real-world deepfake image detection.
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All authors Zhilin Tu, Kemou Li, Fengpeng Li, Jianwei Fei, Jiamin Zhang, Haiwei Wu
Computer Vision
MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation
Wenqing Tian, Hanyi Mao, Zhaocheng Liu et al. (+4 more)
Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-reference workflows, users may provide several subject images, a background reference, and long, entity-indexed
prompts to control multiple people within one scene. In this setting, a key failure mode is cross-subject attribute misbinding: attributes are preserved, edited, or transferred to the wrong subject. Existing
benchmarks and metrics largely emphasize holistic fidelity or per-subject self-similarity, making such failures hard to diagnose. We introduce MultiBind, a
benchmark built from real multi-person photographs. Each instance provides slot-ordered subject crops with masks and bounding boxes, canonicalized subject references, an inpainted background reference, and a dense entity-indexed
prompt derived from structured annotations. We also propose a dimension-wise confusion evaluation protocol that matches generated subjects to ground-truth slots and measures slot-to-slot similarity using specialists for face identity, appearance, pose, and expression. By subtracting the corresponding ground-truth similarity
matrices, our method separates self-degradation from true cross-subject interference and exposes interpretable failure patterns such as drift, swap, dominance, and blending. Experiments on modern multi-reference generators show that MultiBind reveals binding failures that conventional reconstruction metrics miss.
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All authors Wenqing Tian, Hanyi Mao, Zhaocheng Liu, Lihua Zhang, Qiang Liu, Jian Wu, Liang Wang
Computer Vision
Cross-Instance Gaussian Splatting Registration via Geometry-Aware Feature-Guided Alignment
Roy Amoyal, Oren Freifeld and Chaim Baskin
We present Gaussian Splatting
Alignment (GSA), a novel method for aligning two independent 3D Gaussian Splatting (3DGS) models via a similarity transformation (rotation, translation, and scale), even when they are of different objects in the same category (e.g., different cars). In contrast, existing methods can only align 3DGS models of the same object (e.g., the same car) and often must be given true scale as input, while we estimate it successfully. GSA leverages viewpoint-guided spherical map features to obtain robust correspondences and introduces a two-step optimization framework that aligns 3DGS models while keeping them fixed. First, we apply an iterative feature-guided absolute orientation solver as our coarse registration, which is robust to poor initialization (e.g., 180 degrees misalignment or a 10x scale gap). Next, we use a fine registration step that enforces multi-view feature consistency, inspired by inverse radiance-field formulations. The first step already achieves state-of-the-art performance, and the second further improves results. In the same-object case, GSA outperforms prior works, often by a large margin, even when the other methods are given the true scale. In the harder case of different objects in the same category, GSA vastly surpasses them, providing the first effective solution for category-level 3DGS registration and unlocking new applications. Project webpage: https://bgu-cs-vil.github.io/GSA-project/
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Computer Vision
Chronological Contrastive Learning: Few-Shot Progression Assessment in Irreversible Diseases
Clemens Watzenböck, Daniel Aletaha, Michaël Deman et al. (+9 more)
Quantitative disease severity scoring in medical imaging is costly, time-consuming, and subject to inter-reader variability. At the same time, clinical archives contain far more longitudinal imaging data than expert-annotated severity scores. Existing self-supervised methods typically ignore this chronological structure. We introduce ChronoCon, a contrastive learning approach that replaces label-based ranking losses with rankings derived solely from the visitation order of a patient's longitudinal scans. Under the clinically plausible assumption of monotonic progression in irreversible diseases, the method learns disease-relevant representations without using any expert labels. This generalizes the idea of Rank-N-Contrast from label distances to temporal ordering. Evaluated on rheumatoid arthritis radiographs for severity assessment, the learned representations substantially improve label efficiency. In low-label settings, ChronoCon significantly outperforms a fully supervised baseline initialized from ImageNet weights. In a few-shot learning experiment, fine-tuning ChronoCon on expert scores from only five patients yields an intraclass correlation coefficient of 86% for severity score prediction. These results demonstrate the potential of chronological contrastive learning to exploit routinely available imaging metadata to reduce annotation requirements in the irreversible disease domain. Code is available at https://github.com/cirmuw/ChronoCon.
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All authors Clemens Watzenböck, Daniel Aletaha, Michaël Deman, Thomas Deimel, Jana Eder, Ivana Janickova, Robert Janiczek, Peter Mandl, Philipp Seeböck, Gabriela Supp, Paul Weiser, Georg Langs