Computer Vision
OpenSGA: Efficient 3D Scene Graph Alignment in the Open World
Gang Chen, Sebastián Barbas Laina, Stefan Leutenegger et al. (+1 more)
Scene graph
alignment establishes object correspondences between two 3D scene graphs constructed from partially overlapping observations. This enables efficient scene understanding and object-level relocalization when a
robot revisits a place, as well as global map fusion across multiple
agents. Such capabilities are essential for
robots that require long-term
memory for long-horizon tasks involving interactions with the environment. Existing approaches mainly focus on subscan-to-subscan (S2S)
alignment and depend heavily on
geometric point-cloud features, leaving frame-to-scan (F2S)
alignment and open-set
vision-
language features underexplored. In addition, existing
datasets for scene graph
alignment remain small-scale with limited object diversity, constraining systematic training and evaluation. We present a unified and efficient scene graph
alignment framework that predicts object correspondences by fusing
vision-
language, textual, and
geometric features with spatial context. The framework comprises modules such as a distance-gated spatial
attention encoder, a minimum-cost-flow-based allocator, and a global scene
embedding generator to achieve accurate
alignment even under large coordinate discrepancies. We further introduce ScanNet-SG, a large-scale
dataset generated via an automated annotation pipeline with over 700k samples, covering 509 object categories from ScanNet labels and over 3k categories from GPT-4o-based tagging. Experiments show that our method achieves the best overall performance on both F2S and S2S tasks, substantially outperforming existing scene graph
alignment methods. Our code and
dataset are released at: https://autonomousrobots.nl/paper_websites/opensga.
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All authors Gang Chen, Sebastián Barbas Laina, Stefan Leutenegger, Javier Alonso-Mora
Computer Vision
Adaptive Context Matters: Towards Provable Multi-Modality Guidance for Super-Resolution
Jinyi Luo, Minghao Liu, Yifan Li et al. (+2 more)
Super-resolution (SR) is a severely ill-posed problem with inherent ambiguity, as widely recognized in both empirical and theoretical studies. Although recent semantic-guided and multi-modal SR methods exploit large models or external priors to enhance semantic
alignment, the fusion of heterogeneous modalities remains insufficiently understood in practice and theory. In this work, we provide the first theoretical modeling of multi-modal SR, revealing that prior methods are bottlenecked by sub-optimal modality utilization. Our analysis shows that the generalization risk bound can be improved by strengthening the
alignment between modality weights and their effective contributions, while reducing representation complexity. This theoretical insight inspires us to propose the novel Multi-Modal Mixture-of-Experts Super-Resolution framework (M$^3$ESR) that employs generalization-oriented dynamic modality fusion for accurate risk control and modality contribution optimization. In detail, we propose a novel spatially dynamic modality weighting module and a temporally adaptive modality temperature scheduling mechanism, enabling flexible and adaptive spatial-temporal modality weighting for effective risk control. Extensive experiments demonstrate that our M$^3$ESR significantly boosts generalization and semantic consistency performances, which confirms our superiority.
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All authors Jinyi Luo, Minghao Liu, Yifan Li, Zejia Fan, Jiaying Liu
Computer Vision
Automated Detection of Abnormalities in Zebrafish Development
Sarath Sivaprasad, Hui-Po Wang, Anna-Lisa Jäckel et al. (+4 more)
Zebrafish embryos are a valuable model for drug discovery due to their optical transparency and genetic similarity to humans. However, current evaluations rely on manual inspection, which is costly and labor-intensive. While machine learning offers automation potential, progress is limited by the lack of comprehensive
datasets. To address this, we introduce a large-scale
dataset of high-resolution microscopic image sequences capturing zebrafish embryonic development under both control conditions and exposure to compounds (3,4-dichloroaniline). This
dataset, with expert annotations at fine-grained temporal levels, supports two benchmarking tasks: (1) fertility classification, assessing zebrafish egg viability (130,368 images), and (2) toxicity assessment, detecting malformations induced by toxic exposure over time (55,296 images). Alongside the
dataset, we present the first
transformer-based baseline model that integrates spatiotemporal features to predict developmental abnormalities at early stages. Experimental results present the model's effectiveness, achieving 98% accuracy in fertility classification and 92% in toxicity assessment. These findings underscore the potential of automated approaches to enhance zebrafish-based toxicity analysis.
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All authors Sarath Sivaprasad, Hui-Po Wang, Anna-Lisa Jäckel, Jonas Baumann, Carole Baumann, Jennifer Herrmann, Mario Fritz
Computer Vision
Automated high-frequency quantification of fish communities and biomass using computer vision
Kota Ishikawa, Takuma Masui, Keita Koeda et al. (+3 more)
Quantifying fish community structure is essential for understanding biodiversity and ecosystem responses in a changing environment, yet existing survey methods provide limited high-frequency, quantitative observations. Conventional approaches, including catch-based methods,
underwater visual censuses, and environmental DNA metabarcoding, either require intensive labor or lack reliable estimates of abundance and biomass. Here, we develop an automated framework for quantifying fish communities from
underwater video using computer
vision. Using
videos acquired with a custom-made stereo camera system, the framework integrates deep learning-based fish identification, multi-object tracking, and 3D reconstruction to estimate species-level abundance and biomass. We applied the approach to a reef fish community over a 20-day period with hourly daytime observations, revealing dynamic fluctuations in species richness, abundance, and biomass associated with changes in species composition. By comparing fish communities estimated from visual census and environmental DNA surveys, we demonstrate that our method provides complementary strengths for continuous, non-invasive, and quantitative monitoring of consistently observed species. This approach provides a scalable foundation for long-term monitoring and advances the capacity to resolve fine-scale temporal dynamics in fish communities.
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All authors Kota Ishikawa, Takuma Masui, Keita Koeda, Rickdane Gomez, Lucas Yutaka Kimura, Michio Kondoh
Computer Vision
Uni-Synergy: Bridging Understanding and Generation for Personalized Reasoning via Co-operative Reinforcement Learning
Zijun Shen, Sihan Yang, Ruichuan An et al. (+5 more)
Unified
Multimodal Models (UMMs) excel in general tasks but struggle to bridge the gap between personalized understanding and generation. Prior works largely rely on implicit
token-level
alignment via supervised fine-tuning, which fails to fully capture the potential synergy between comprehension and creation. In this work, we propose Sync-R1, an end-to-end reinforcement learning framework that jointly optimizes personalized understanding and generation within a single, explicit
reasoning loop. Through this unified feedback process, Sync-R1 enables personalized comprehension to guide content creation, while the resulting generation quality reciprocally refines understanding within an integrated reward landscape. To efficiently orchestrate this dual-task synergy, we introduce Sync-GRPO, a reinforcement learning method utilizing an ensemble reward system. Furthermore, we propose Dynamic Group Scaling (DGS), which adaptively filters low-potential trajectories to reduce gradient variance and accelerate convergence. To better reflect real-world complexity, we introduce UnifyBench++, featuring denser textual descriptions and richer user contexts. Experimental results demonstrate that Sync-R1 achieves state-of-the-art performance, showcasing superior cross-task
reasoning and robust personalization without requiring complex cold-start procedures. The code and the UnifyBench++
dataset will be released at: https://github.com/arctanxarc/UniCTokens.
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All authors Zijun Shen, Sihan Yang, Ruichuan An, Ziyu Guo, Hao Liang, Ming Lu, Renrui Zhang, Wentao Zhang
Computer Vision
Filtering Memorization from Parameter-Space in Diffusion Models
Yu Zhe, Yang Jiayan, Wei Junhao et al. (+2 more)
Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing
diffusion models, enabling users to inject new visual concepts or styles through lightweight parameter updates. However, LoRAs can memorize training images, causing generated outputs to reproduce copyrighted or sensitive content. This risk is particularly concerning in LoRA-sharing ecosystems, where users distribute trained LoRAs without releasing the underlying training data. Existing approaches for mitigating memorization rely on access to the training pipeline, training data, or control over the inference process, making them difficult to apply when only the released LoRA weights are available. We propose Base-Anchored Filtering (BAF), a training-free and data-free framework for post-hoc memorization mitigation in
diffusion LoRAs. BAF decomposes LoRA updates into spectral channels and measures their
alignment with the principal subspace of the pretrained backbone. Channels strongly aligned with this subspace are retained as generalizable adaptations, while weakly aligned channels are suppressed as potential carriers of memorized content. Experiments on multiple
datasets and
diffusion backbones demonstrate that BAF consistently reduces memorization while preserving or even improving generation quality. Our code is available in the supplementary material.
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All authors Yu Zhe, Yang Jiayan, Wei Junhao, Yu-Lin Tsai, Wang Chen
Computer Vision · Cross-listed from cs.LG
Beyond Spatial Compression: Interface-Centric Generative States for Open-World 3D Structure
Xiang Chen and Alexander Binder
Current 3D tokenizers largely treat representation as spatial compression: compact codes reconstruct surface geometry, but leave component ownership and attachment validity implicit. In open-world assets with intersecting components, noisy
topology, and weak canonical structure, this creates a representation mismatch: local shape, component identity, and assembly relations become entangled in a latent stream and are not natively addressable during decoding. We formulate an alternative view, interface-centric generative states, in which tokenization constructs an operational state rather than a passive compressed code. The state exposes local geometry, component ownership, and attachment validity as variables that can be queried, constrained, and repaired during decoding. We instantiate this formulation with Component-Conditioned Canonical Local
Tokens (C2LT-3D), factorizing representation into canonical local geometry, partition-conditioned context, and relational seam variables. Each factor targets a distinct failure mode of compression-centric
tokens: pose leakage, cross-component interference, or invalid local attachment. This exposed state supports attachment validation, latent structural repair, targeted intervention, and constrained serialization without a separate post-hoc structure recovery module. Trained on single-object CAD models and evaluated zero-shot on open-world multi-component assets, C2LT-3D improves structural robustness and shows that its latent variables remain actionable under
adversarial attachment settings. These results suggest that open-world 3D generative representations should be evaluated not only by reconstruction fidelity, but by whether their discrete states remain operational for assembly-level structural
reasoning.
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Computer Vision
WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors
Keming Wu, Yijing Cui, Wenhan Xue et al. (+11 more)
Commercial
video generation systems such as Seedance2.0 and Veo3.1 have rapidly improved, strengthening the view that
video generators may be evolving into "world simulators." Yet the community still lacks a
benchmark that directly tests whether a model can reason about how an observed world should evolve over time. We introduce WorldReasonBench, which reframes
video generation evaluation as world-state prediction: given an initial state and an action, can a model generate a future
video whose state
evolution remains physically, socially, logically, and informationally consistent? WorldReasonBench contains 436 curated test cases with structured ground-truth QA annotations spanning four
reasoning dimensions and 22 subcategories. We evaluate generated
videos with a human-aligned two-part methodology: Process-aware
Reasoning Verification uses structured QA and
reasoning-phase diagnostics to detect temporal and causal failures, while Multi-dimensional Quality Assessment scores
reasoning quality, temporal consistency, and visual aesthetics for ranking and reward modeling. We further introduce WorldRewardBench, a preference
benchmark with approximately 6K expert-annotated pairs over 1.4K
videos, supporting pair-wise and point-wise reward-model evaluation. Across modern
video generators, our results expose a persistent gap between visual plausibility and world
reasoning:
videos can look convincing while failing dynamics, causality, or information preservation. We will release our
benchmarks and evaluation toolkit to support community research on genuinely world-aware
video generation at https://github.com/UniX-AI-Lab/WorldReasonBench/.
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All authors Keming Wu, Yijing Cui, Wenhan Xue, Qijie Wang, Xuan Luo, Zhiyuan Feng, Zuhao Yang, Sudong Wang, Sicong Jiang, Haowei Zhu, Zihan Wang, Ping Nie, Wenhu Chen, Bin Wang
Computer Vision
CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
Minqing Huang, Yujiao Xiang, Zihan Liang et al. (+8 more)
Vision-
Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing
reasoning mechanisms still struggle to provide planning-oriented intermediate representations: textual Chain-of-Thought (CoT) fails to preserve continuous spatiotemporal structure, while latent world
reasoning remains difficult to use as a direct condition for action generation. In this paper, we propose CoWorld-VLA, a multi-expert world
reasoning framework for autonomous driving, where world representations serve as explicit conditions to guide action planning. CoWorld-VLA extracts complementary world information through multi-source supervision and encodes it into expert
tokens within the VLA, thereby providing planner-accessible conditioning signals. Specifically, we construct four types of
tokens: semantic interaction,
geometric structure, dynamic
evolution, and ego trajectory
tokens, which respectively model interaction intent, spatial structure, future temporal dynamics, and behavioral goals. During action generation, CoWorld-VLA employs a
diffusion-based hierarchical multi-expert fusion planner, which is coupled with scene context throughout the joint denoising process to generate continuous ego trajectories. Experiments on NAVSIM v1 & v2 demonstrate future-scene modeling capability and strong planning performance, including collision avoidance and trajectory accuracy. Ablation studies further validate the complementarity of expert
tokens and their effectiveness as planning conditions for action generation. Code will be available at https://github.com/AFARI-Research/CoWorld-VLA.
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All authors Minqing Huang, Yujiao Xiang, Zihan Liang, Jiajie Huang, Jingqi Wang, Yuheng Zhou, Zhi Xu, Feiyang Tan, Hangning Zhou, Mu Yang, Gong Che
Computer Vision
Progressive Photorealistic Simplification
Adi Rosenthal, Dana Berman, Yedid Hoshen et al. (+1 more)
Existing image simplification techniques often rely on Non-Photorealistic Rendering (NPR), transforming photographs into stylized sketches, cartoons, or paintings. While effective at reducing visual complexity, such approaches typically sacrifice photographic realism. In this work, we explore a complementary direction: simplifying images while preserving their photorealistic appearance. We introduce progressive semantic image simplification, a framework that iteratively reduces scene complexity by removing and inpainting elements in a controlled manner. At each step, the resulting image remains a plausible natural photograph. Our method combines semantic understanding with generative editing, leveraging
Vision-
Language Models (VLMs) to identify and prioritize elements for removal, and a learned verifier to ensure photorealism and coherence throughout the process. This is implemented via an iterative Select-Remove-Verify pipeline that produces high-quality simplification trajectories. To improve efficiency, we further distill this process into an image-to-
video generation model that directly predicts coherent simplification sequences from a single input image. Beyond generating cleaner and more focused compositions, our approach enables applications such as content-aware decluttering, semantic layer decomposition, and interactive editing. More broadly, our work suggests that simplification through structured content removal can serve as a practical mechanism for guiding visual interpretation within the photorealistic domain, complementing traditional abstraction methods.
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All authors Adi Rosenthal, Dana Berman, Yedid Hoshen, Ariel Shamir
Computer Vision
Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable
Tianyuan Zou, Liang Yue, Yang Liu et al. (+2 more)
With the growing prevalence of always-on hardware such as smart glasses, body cameras, and home security systems, life-logging visual sensing is becoming inevitable, forming the backbone of persistent, always-on AI systems. Meanwhile, recent advances in proactive
agents and world models signal a fundamental shift from episodic,
prompt-driven tools to next-generation AI systems that continuously perceive and react to the physical world. Although life-logging
video streams can substantially improve utility of these promising systems, they also introduce significant
privacy risks by revealing sensitive information, such as behavioral patterns, emotional states, and social interactions, beyond what isolated images expose. If unresolved, these risks may undermine public trust and hinder the sustainable development of always-on AI technologies. Existing
privacy protections are either attack-specific or incur substantial utility loss, and fail to consider the entire data exploitation pipeline. We therefore posit that the
privacy-utility trade-off in life-logging
video streams is a foundational challenge for next-generation AI systems that demands further investigation. We call for novel pipeline-aware
privacy-preserving designs that jointly optimize utility and
privacy for long-horizon life-logging visual data. In parallel, formal
privacy leakage metrics and standardized
benchmarks remain important open directions for future research.
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All authors Tianyuan Zou, Liang Yue, Yang Liu, Ya-Qin Zhang, Sijie Cheng
Computer Vision
AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
Xi Jiang, Yinjie Zhao, Zesheng Yang et al. (+1 more)
Visual anomaly detection (VAD) is crucial in many real-world fields, such as industrial inspection, medical imaging, infrastructure monitoring, and remote sensing. However, the specific anomaly definitions, data modalities, and annotation standards across different domains make it difficult to transfer single-domain trained VAD models.
Vision-
language models (VLMs), pre-trained on large-scale cross-domain data, can perform visual perception under task instructions, offering a promising solution for cross-domain VAD. However, single-inference VLM judgments are unreliable, since they rely more on prior knowledge than on normal-sample references or fine-grained feature evidence. We therefore present AnomalyClaw, a training-free VAD
agent that turns anomaly judgment into a multi-round refutation process. In each round, the
agent proposes candidate anomalies and refutes each against normal-sample references, drawing on a 13-tool library for visual verification, reference parsing, and frozen expert probing. On the CrossDomainVAD-12
benchmark (12
datasets), AnomalyClaw achieves consistent macro-AUROC improvements over single-step direct inference with +6.23 pp on GPT-5.5, +7.93 pp on Seed2.0-lite, and +3.52 pp on Qwen3.5-VL-27B. We further introduce an optional verbalized self-
evolution extension. It builds an online rulebook from internal-branch disagreement without oracle labels. On Qwen3.5-VL-27B, it delivers a +2.09 pp mean gain, comparable to a K = 10 oracle-label supervised baseline (+1.99 pp). These results show that agentic refutation improve anomaly understanding and
reasoning of VLMs, rather than merely aggregating tool outputs.
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All authors Xi Jiang, Yinjie Zhao, Zesheng Yang, Feng Zheng
Computer Vision
Sens-VisualNews: A Benchmark Dataset for Sensational Image Detection
Andreas Goulas, Damianos Galanopoulos, Evlampios Apostolidis et al. (+1 more)
The detection of sensational content in media items can be a critical filtering mechanism for identifying check-worthy content and flagging potential disinformation, since such content triggers physiological arousal that often bypasses critical evaluation and accelerates viral sharing. In this paper we introduce the task of sensational image detection, which aims to determine whether an image contains shocking, provocative, or emotionally charged features to grab
attention and trigger strong emotional responses. To support research on this task, we create a new
benchmark dataset (called Sens-VisualNews) that contains 9,576 images from news items, annotated based on the (in-)existence of various sensational concepts and events in their visual content. Finally, using Sens-VisualNews, we study the
prompt sensitivity, performance and robustness of a wide range of open SotA
Multimodal LLMs, across both zero-shot and fine-tuned settings.
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All authors Andreas Goulas, Damianos Galanopoulos, Evlampios Apostolidis, Vasileios Mezaris
Computer Vision · Cross-listed from cs.CL
Phoenix-VL 1.5 Medium Technical Report
Team Phoenix: Arka Ray, Askar Ali Mohamed Jawad, Biondi Lee et al. (+26 more)
We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively
multimodal and
multilingual foundation model, adapted to regional
languages and the Singapore context. Developed as a sovereign AI asset, it demonstrates that deep domain adaptation can be achieved with minimal degradation to broad-spectrum intelligence and
alignment. Continued pretraining was performed on Mistral Medium 3.1 using a localized 1-trillion
tokens multimodal corpus, followed by a 250-billion
tokens long-context extension phase. Subsequent post-training incorporated a novel human-annotated Singapore
multimodal dataset and curated textual corpus on Singapore culture, knowledge, and legislation, totaling 22-billion
tokens. An additional 5 billion
tokens of model
alignment was performed through Online Direct Preference Optimization. Phoenix-VL 1.5 Medium achieves state-of-the-art performance for its size on Singapore
multimodal, legal, and government policy
benchmarks while remaining globally competitive on general
multimodal intelligence,
multilingual, and STEM
benchmarks. We also introduce a novel evaluation suite encompassing localized knowledge
benchmarks and an institutionally aligned model behavior and safety framework. We report the data curation principles, training methodology, and highlight
benchmark and inference performance.
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All authors Team Phoenix: Arka Ray, Askar Ali Mohamed Jawad, Biondi Lee, Elijah Seah, Eva Lim, Fiona Teo, Grace Toh, Guang Xiang Teo, Jun En Tan, Jia Hui Bong, Jiale Wang, Jonathan Ng, Justin Tan, Kai Zhe Yew, Matthew Ong, Shun Yi Yeo, Wen Jett Lam, Wen Xiu Tan, Ze Yu Zhang, Gee Wah Ng, Chee Wee Ang, Mistral AI: Adrien Sadé, Guillaume Kunsch, Jia Sin Loh, Nicolas Schuhl, Rupert Menneer, Umar Jamil, Vincent Maladière, Yimu Pan
Computer Vision
Temporal Sampling Frequency Matters: A Capacity-Aware Study of End-to-End Driving Trajectory Prediction
Yumao Liu, Tao Liu, Xiangyu Li et al. (+2 more)
End to end (E2E) autonomous driving trajectory prediction is often trained with camera frames sampled at the highest available temporal frequency, assuming that denser sampling improves performance. We question this assumption by treating temporal sampling frequency as an explicit training set design variable. Starting from high frequency E2E driving
datasets, we construct frequency sweep training sets by temporally subsampling camera frames along each trajectory. For each model
dataset pair, we train and evaluate the same model under a fixed protocol, so the frequency response reflects how prediction performance changes with sampling frequency. We analyze this response from a capacity aware perspective. Sparse sampling may miss driving relevant cues, while dense sampling may add redundant visual content and off
manifold noise. For finite capacity models, this can create a driving irrelevant capacity burden. We evaluate three smaller E2E models and a larger VLA style AutoVLA model on Waymo, nuScenes, and PAVE. Results show model and
dataset dependent frequency responses. Smaller E2E models often show non monotonic or near plateau trends and achieve their best 3 second ADE at lower or intermediate frequencies. In contrast, AutoVLA achieves its best 3 second ADE and FDE at the highest evaluated frequency on all three
datasets. Iteration matched controls suggest that the advantage of lower or intermediate frequencies for smaller models is not explained only by unequal training update counts. These findings show that temporal sampling frequency should be reported and tuned, rather than fixed to the highest available value.
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All authors Yumao Liu, Tao Liu, Xiangyu Li, Jiaxiang Li, Ke Ma
Computer Vision
Niyati Rawal, Sushant Ravva, Shah Alam Abir et al. (+5 more)
Vision-
Language Models (VLMs) have advanced rapidly in
multimodal perception and
language understanding, yet it remains unclear whether they can reliably ground
language into spatially coherent, plausibly executable actions in 3D digital environments. We introduce SleepWalk, a
benchmark for evaluating instruction-grounded trajectory prediction in single-scene 3D worlds generated from textual scene descriptions and filtered for navigability. Unlike prior
navigation benchmarks centered on long-range exploration across rooms, SleepWalk targets localized, interaction-centric
embodied reasoning: given rendered visual observations and a natural-
language instruction, a model must predict a trajectory that respects scene geometry, avoids collisions, and terminates at an action-compatible location. The
benchmark covers diverse indoor and outdoor environments and organizes tasks into three tiers of spatial and temporal difficulty, enabling fine-grained analysis of grounding under increasing compositional complexity. Using a standardized pointwise judge-based evaluation protocol, we evaluate three frontier VLMs on 2,472 curated 3D environments with nine instructions per scene. Results reveal systematic failures in grounded spatial
reasoning, especially under occlusion, interaction constraints, and multi-step instructions: performance drops as the difficulty level of the tasks increase. In general, current VLMs can somewhat produce trajectories that are simultaneously spatially coherent, plausibly executable, and aligned with intended actions. By exposing failures in a controlled yet scalable setting, SleepWalk provides a critical
benchmark for advancing grounded
multimodal reasoning,
embodied planning,
vision-
language navigation, and action-capable
agents in 3D environments.
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All authors Niyati Rawal, Sushant Ravva, Shah Alam Abir, Saksham Jain, Aman Chadha, Vinija Jain, Suranjana Trivedy, Amitava Das
Computer Vision
Halo Separation-guided Underwater Multi-scale Image Restoration
Jiaxin Yang, Honglin Liu, Yongli Wang et al. (+3 more)
Underwater images captured by Autonomous
Underwater Vehicles (AUVs) are inevitably affected by artificial light sources, which often produce
halos in the foreground of the camera and seriously interfere with the quality of the image. The existing
underwater image enhancement methods fail to fully consider this key problem, and the robustness of processing images under artificial light scenes is poor. In practical applications, since
underwater image enhancement itself is a very challenging task, the influence of artificial light sources will lead to serious degradation of image performance and affect subsequent
vision tasks. In order to effectively deal with this problem, this paper designs a single
halo image correction method based on an iterative structure. The network is mainly divided into two sub-networks, one is the
halo layer separation sub-network which aims to separate the
halo by gradient minimization, and the other is the multi-scale recovery sub-network which aims to recover the image information masked by
halo. The UIEB and EUVP synthetic
datasets are used for training to ensure that the network can fully learn the characteristics and laws of
underwater halo images. Then a large number of
halo images taken in an
underwater environment with real artificial light are collected for testing. In addition, the brightness distribution characteristics of
underwater halo images are analyzed and the radial gradient is introduced to constraint eliminate
halo to improve the effect of
underwater image restoration.
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All authors Jiaxin Yang, Honglin Liu, Yongli Wang, Shuyi Cao, Chengcheng Jiang, Jiale Wang
Computer Vision
CellDX AI Autopilot: Agent-Guided Training and Deployment of Pathology Classifiers
Alexey Pchelnikov and Aleksei Pchelnikov
Training AI models for computational pathology currently requires access to expensive whole-slide-image
datasets, GPU infrastructure, deep expertise in machine learning, and substantial engineering effort. We present CellDX AI Autopilot, a platform that lets users – from pathologists with no ML background to ML practitioners running many parallel experiments – train, evaluate, and deploy whole-slide image classifiers through natural
language interaction with an AI
agent. The platform provides a structured set of
agent skills that guide the user through
dataset curation, automated hyperparameter tuning, multi-strategy model comparison, and human-in-the-loop deployment, all on a pre-built
dataset of over 32,000 cases and 66,000 H&E-stained whole-slide images with pre-extracted features. We describe the
agent skill architecture, the underlying Multiple Instance Learning (MIL) training framework supporting four classification strategies, and an iterative pairwise hyperparameter search (grid or seeded random) that reduces tuning cost by over 30x compared to exhaustive search. CellDX AI Autopilot is, to our knowledge, the first system to expose pathology-specialized
agent skills and a pathology-specialized training platform to general-purpose AI
agents (e.g. any LLM-based
agent runtime), delivering end-to-end automated model training without requiring the
agent itself to be domain-specific. The platform addresses both the ML-expertise bottleneck that limits adoption in diagnostic pathology and the engineering bottleneck that limits how many experiments a researcher can run cost-effectively.
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Computer Vision
DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions
Minje Kim, Younghyun Noh, Jaesoon Kim et al. (+1 more)
While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent
geometric surfaces remains a challenge. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) offer powerful dynamic scene rendering capabilities; however, relying solely on photometric optimization often leads to
geometric ambiguities. This results in discontinuous surfaces, severe artifacts, and broken surfaces over time. To address these limitations, we present DySurface, a novel framework that bridges the effectiveness of explicit Gaussians with the
geometric fidelity of implicit Signed Distance Functions (SDFs) in dynamic scenes. Our approach tackles the structural discrepancy between the forward deformation of 3DGS ($canonical \rightarrow dynamic$) and the backward deformation required for volumetric SDF rendering ($dynamic \rightarrow canonical$). Specifically, we propose the VoxGS-DSDF branch that leverages deformed Gaussians to construct a dynamic sparse voxel grid, providing explicit
geometric guidance to the implicit SDF field. This explicit anchoring effectively regularizes the volumetric rendering process, significantly improving surface reconstruction quality, with watertight boundaries and detailed representations. Quantitative and qualitative experiments demonstrate that DySurface significantly outperforms state-of-the-art baselines in
geometric accuracy while maintaining competitive rendering performance.
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All authors Minje Kim, Younghyun Noh, Jaesoon Kim, Tae-Kyun Kim
Computer Vision
Portable Active Learning for Object Detection
Rashi Sharma, Justin Timothy C. Bersamin and Karthikk Subramanian
Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-world applications. Prior active learning methods often depend on model features or modify detector internals and training schedules, increasing integration overhead. Moreover, they rarely jointly exploit the benefits of image-level signals, class-imbalance cues, and instance-level uncertainty for comprehensive selection. We present Portable Active Learning (PAL), a detector-agnostic, easily portable framework that operates solely on inference outputs. PAL combines class-wise instance uncertainty with image-level diversity to guide data selection. At each round, PAL trains lightweight class-specific logistic classifiers to distinguish true from false positives, producing
entropy-based uncertainty scores for proposals. Candidate images are then refined using global image
entropy, class diversity, and image similarity, yielding batches that are both informative and diverse. PAL requires no changes to model internals or training pipelines, ensuring broad compatibility across detectors. Extensive experiments on COCO, PASCAL VOC, and BDD100K demonstrate that PAL consistently improves label efficiency and detection accuracy compared to existing active learning baselines, making it a practical solution for scalable and cost-effective deployment of object detection in real-world settings.
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Computer Vision
BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization
Wei Wang, Dou Quan, Ning Huyan et al. (+4 more)
Geometric differences between cross-view images, such as drone and satellite views, significantly increase the challenge of Cross-View Geo-Localization (CVGL), which aims to acquire the geolocation of images by image
retrieval. To further enhance the CVGL performance, this paper proposes a parameter-efficient adaptation framework for bridging the
geometric gap across images based on the
vision foundation model (VFM) (e.g., DINOv3), termed BGG. BGG not only effectively leverages the general visual representations of VFM and captures the robust and consistent features from cross-view images, but also utilizes the generalization capabilities of the VFM, significantly improving the CVGL performance. It mainly contains a Multi-granularity Feature Enhancement Adapter (MFEA) and a Frequency-Aware Structural Aggregation (FASA) module. Specifically, MFEA enhances the scale adaptability and viewpoint robustness of features by multi-level dilated convolutions, effectively bridging the cross-view
geometric gap with small training costs. Additionally, considering the [CLS]
token lacks spatial details for precise image
retrieval and localization, the FASA module modulates patch
tokens in the frequency domain and performs adaptive aggregation for local structural feature enhancement. Finally, BGG fuses the enhanced local features with the [CLS]
token for more accurate CVGL. Extensive experiments on University-1652 and SUES-200
datasets demonstrate that BGG has significant advantages over other methods and achieves state-of-the-art localization performance with low training costs.
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All authors Wei Wang, Dou Quan, Ning Huyan, Shuang Wang, Yi Li, Pei He, Licheng Jiao
Computer Vision
EvoStreaming: Your Offline Video Model Is a Natively Streaming Assistant
Zichen Wen, Boxue Yang, Junlong Ke et al. (+5 more)
Streaming
video understanding demands more than watching longer
videos: assistants must decide when to speak in real time, balancing responsiveness against verbosity. Yet most
video-
language models (VideoLLMs) are trained for offline inference, and existing streaming
benchmarks externalize this timing decision to the evaluator. We address this gap with RealStreamEval, a frame-level multi-turn evaluation protocol that exposes models to sequential observations and penalizes unnecessary responses. Under this protocol, we observed that strong offline VideoLLMs retain useful visual understanding but lack an interaction policy for deciding when to respond. Motivated by this observation, we propose EvoStreaming, a self-evolved streaming adaptation framework in which the base model itself acts as data generator, relevance annotator, and roll-out policy to synthesize streaming trajectories without external supervision. With only $1{,}000$ self-generated samples ($139\times$ less than the leading streaming instruction-tuning approach) and no architectural changes, EvoStreaming consistently improves the overall RealStreamEval score by up to $10.8$ points across five open VideoLLM backbones (Qwen2/2.5/3-VL, InternVL-3.5, MiniCPM-V4.5) while largely preserving offline
video performance. These results suggest that data-efficient interaction tuning is a practical path for adapting existing VideoLLMs to streaming assistants.
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All authors Zichen Wen, Boxue Yang, Junlong Ke, Jiajie Huang, Chenfei Liao, Junxi Wang, Xuyang Liu, Linfeng Zhang
Computer Vision
The Alpha Blending Hypothesis: Compositing Shortcut in Deepfake Detection
Andrii Yermakov, Jan Cech, Mario Fritz et al. (+1 more)
Recent deepfake detection methods demonstrate improved cross-
dataset generalization, yet the underlying mechanisms remain underexplored. We introduce the Alpha Blending Hypothesis, positing that state-of-the-art frame-based detectors primarily function as alpha blending searchers; rather than learning semantic anomalies or specific generative neural fingerprints, they localize low-level compositing artifacts introduced during the integration of manipulated faces into target frames. We experimentally validate the hypothesis, demonstrating that deepfake detectors exhibit high sensitivity to the so-called self-blended images (SBI) and non-generative manipulations. We propose the method BlenD that leverages a large-scale, diverse
dataset of real-only facial images augmented with SBI. This approach achieves the best average cross-
dataset generalization on 15 compositional deepfake
datasets released between 2019 and 2025 without utilizing explicitly generated deepfakes during training. Furthermore, we show that predictions from explicit blending searchers and models resilient to blending shortcuts are highly complementary, yielding a state-of-the-art AUROC of 94.0% in an ensemble configuration. The code with experiments and the trained model will be publicly released.
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All authors Andrii Yermakov, Jan Cech, Mario Fritz, Jiri Matas
Computer Vision
LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency
Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser et al. (+4 more)
Layered image assets are widely used in real-world creative workflows, enabling non-destructive iteration and flexible re-composition. Recent advances in layered image generation and decomposition synthesize or recover layered representations, yet controllable editing of layered images remains challenging. Manual editing requires careful coordination across layers to maintain consistent illumination and contact, while AI-based pipelines collapse layers into a flattened image for editing, then decompose them again, introducing background-to-foreground leakage and unstable transparency. To address these limitations, we propose LimeCross, a training-free context-conditioned layered image editing framework that edits user-selected RGBA layers according to text while keeping the remaining layers unchanged. It leverages contextual cues from other layers using a bi-stream
attention mechanism to preserve cross-layer consistency, while explicitly maintaining layer integrity to prevent the contamination of edited layers. To evaluate our approach, we introduce LayerEditBench, a
benchmark of 1500 layered scenes with paired source/target
prompts, along with evaluation protocols that assess both edit fidelity and alpha channel stability. Extensive experiments demonstrate that LimeCross improves layer purity and composite realism over strong editing baselines, establishing context-conditioned layered editing as a principled framework for controllable generative creation.
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All authors Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser, Ko Watanabe, Riku Takahashi, Issey Sukeda, Andreas Dengel
Computer Vision
PaMoSplat: Part-Aware Motion-Guided Gaussian Splatting for Dynamic Scene Reconstruction
Yinan Deng, Jianyu Dou, Jiahui Wang et al. (+3 more)
Dynamic scene reconstruction represents a fundamental yet demanding challenge in computer
vision and robotics. While recent progress in 3DGS-based methods has advanced dynamic scene modeling, obtaining high-fidelity rendering and accurate tracking in scenarios with substantial, intricate motions remains significantly challenging. To address these challenges, we propose PaMoSplat, a novel dynamic Gaussian splatting framework incorporating part awareness and motion priors. Our approach is grounded in two key observations: 1) Parts serve as primitives for scene deformation, and 2) Motion cues from optical flow can effectively guide part motion. Specifically, PaMoSplat initializes by lifting multi-view
segmentation masks into 3D space via graph clustering, establishing coherent Gaussian parts. For subsequent timestamps, we leverage a differential evolutionary
algorithm to estimate the rigid motion of these parts using multi-view optical flow cues, providing a robust warm-start for further optimization. Additionally, PaMoSplat introduces an adaptive iteration count mechanism, internal learnable
rigidity, and flow-supervised rendering loss to accelerate and optimize the training process. Comprehensive evaluations across diverse scenes, including real-world environments, demonstrate that PaMoSplat delivers superior rendering quality, improved tracking precision, and faster convergence compared to existing methods. Furthermore, it enables multiple part-level downstream applications, such as 4D scene editing.
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All authors Yinan Deng, Jianyu Dou, Jiahui Wang, Jingyu Zhao, Yi Yang, Yufeng Yue
Computer Vision
PolarVSR: A Unified Framework and Benchmark for Continuous Space-Time Polarization Video Reconstruction
Chenggong Li, Yidong Luo, Junchao Zhang et al. (+2 more)
Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP). In mainstream Division of-Focal-Plane (DoFP) color polarization imaging, recovering polarization parameters from captured mosaic arrays remains a challenging inverse problem. Existing DoFP cameras also face hardware bottlenecks and often cannot support high-frame-rate acquisition, limiting polarimetric imaging in dynamic
video tasks. These limitations motivate joint spatial and temporal enhancement. To this end, we propose the first space-time polarization
video reconstruction architecture. The method jointly models polarization directions in space and time and uses a polarization-aware implicit neural representation for continuous, high-fidelity upsampling. By analyzing temporal variations in polarization parameters, we further introduce a flow-guided polarization variation loss to supervise polarization dynamics. We also establish the first large-scale color DoFP polarization
video benchmark to support this research direction. Extensive experiments on this
benchmark demonstrate the effectiveness of the method.
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All authors Chenggong Li, Yidong Luo, Junchao Zhang, Boxin Shi, Degui Yang
Computer Vision
Increasing the Efficiency of DETR for Maritime High-Resolution Images
Tinsae Yehuala, Hao Cheng and Ville Lehtola
Maritime object detection is critical for the safe
navigation of unmanned surface vessels (USVs), requiring accurate recognition of obstacles from small buoys to large vessels. Real-time detection is challenging due to long distances, small object sizes, large-scale variations, edge computing limitations, and the high
memory demands of high-resolution imagery. Existing solutions, such as downsampling or image splitting, often reduce accuracy or require additional processing, while
memory-efficient models typically handle only limited resolutions. To overcome these limitations, we leverage
Vision Mamba (ViM) backbones, which build on State Space Models (SSMs) to capture long-range dependencies while scaling linearly with sequence length. Images are tokenized into sequences for efficient high-resolution processing. For further computational efficiency, we design a tailored Feature Pyramid Network with successive downsampling and SSM layers, as well as
token pruning to reduce unnecessary computation on background regions. Compared to state-of-the-art methods like RT-DETR with ResNet50 backbone, our approach achieves a better balance between performance and computational efficiency in maritime object detection.
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Computer Vision
Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation
Ishan Narayan
We present GraphDepth, a
monocular depth estimation architecture that synergistically integrates Graph Neural Networks (GNNs) within a convolutional encoder-decoder framework. Our approach embeds efficient GraphSAGE layers at multiple scales of a ResNet-101 U-Net backbone, enabling explicit modeling of long-range spatial relationships that lie beyond the receptive field of local convolutions. Key technical contributions include: (1) batch-parallelized graph construction with configurable k-NN and grid-based adjacency for scalable training; (2) multi-scale GraphSAGE integration at bottleneck and decoder stages (1/32, 1/16, 1/8 resolution) to propagate global context throughout the feature hierarchy; (3) channel-
attention gated skip connections that adaptively weight encoder features before fusion; and (4) heteroscedastic uncertainty estimation via a dedicated aleatoric uncertainty head, enabling confidence-aware loss weighting during optimization. Unlike
transformer-based hybrids, which suffer from quadratic complexity in sequence length, GraphDepth scales linearly with spatial resolution while achieving comparable global receptive fields through iterative message passing. Experiments on NYU Depth V2, WHU Aerial, ETH3D, and Mid-Air
benchmarks demonstrate competitive accuracy within 4.6% of state-of-the-art
transformers on indoor scenes with substantially lower computational cost (25 FPS vs 9 FPS, 3.8 GB vs 8.8 GB VRAM). GraphDepth achieves the best reported result on WHU Aerial (RMSE 8.24 m) and exhibits superior zero-shot cross-domain transfer to the Mid-Air synthetic aerial
dataset, validating the generalization power of explicit relational
reasoning for depth estimation.
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Computer Vision
AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting
Mingwei Xing, Xinliang Wang and Yifeng Shi
This work explores a simple yet powerful lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). Existing methods typically apply complex, architecture-specific designs on top of the generic pipeline of image feature extraction $\rightarrow$ multi-view interaction $\rightarrow$ feature decoding. However, constrained by the scale bottleneck of 3D training data and the low-pass filtering effect of deep networks, these methods still fall short in cross-domain generalization and high-frequency
geometric fidelity. To address these problems, we propose AdaptSplat, which demonstrates that without complex component engineering, introducing a single adapter of only 1.5M parameters into the generic architecture is sufficient to achieve superior performance. Specifically, we design a lightweight Frequency-Preserving Adapter (FPA) that extracts direction-aware high-frequency structural priors from the shallow features of a powerful
vision foundation model backbone, and seamlessly integrates them into the generic pipeline via high-frequency positional encodings and adaptive residual modulation. This effectively compensates for the high-frequency attenuation caused by over-smoothing in deep features, improving the fitting accuracy of Gaussian primitives on complex surfaces and sharp boundaries. Extensive experiments demonstrate that AdaptSplat achieves state-of-the-art feed-forward reconstruction performance on multiple standard
benchmarks, with stable generalization across domains. Code available at: https://github.com/xmw666/AdaptSplat.
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Computer Vision
VPD-100K: Towards Generalizable and Fine-grained Visual Privacy Protection
Xiaobin Hu, Enpu Zuo, Lanping Hu et al. (+7 more)
Privacy protection has become a critical requirement in the era of ubiquitous visual data sharing, imposing higher demands on efficient and robust
privacy detection
algorithms. However, current robust detection models are severely hindered by the lack of comprehensive
datasets. Existing
privacy-oriented
datasets often suffer from limited scale, coarse-grained annotations, and narrow domain coverage, failing to capture the intricate details of sensitive information in realworld environments. To bridge this gap, we present a large-scale, fine-grained Visual
Privacy Dataset (VPD-100K), designed to facilitate generalized
privacy detection. We establish a holistic taxonomy comprising four primary domains: Human Presence, On-Screen Personally Identifiable Information (PII), Physical Identifiers, and Location Indicators, containing 100,000 images annotated with 33 fine-grained classes and over 190,000 object instances. Statistical analysis reveals that our
dataset features long-tailed distributions, small object scales, and high visual complexity. These characteristics make the
dataset particularly valuable for demanding, unconstrained applications such as live streaming, where actors frequently face unintentional, realtime information leakage. Furthermore, we design an effective frequency-enhanced lightweight module consisting of frequency-domain
attention fusion and adaptive spectral gating mechanism that breaks the limitations of spatial pixel intensity to better capture the subtle details of sensitive information. Extensive experiments conducted on both diverse image and streaming
videos benchmarks consistently demonstrate the effectiveness of our VPD-100K
dataset and the wellcurated frequency mechanism. The code and
dataset are available at https://vpd-100k.github.io/.
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All authors Xiaobin Hu, Enpu Zuo, Lanping Hu, Kaiwen Yang, Dianshu Liao, Tianyi Zhang, Bo Yin, Yinsi Zhou, Shidong Pan, Xiaoyu Sun