Federated Learning (FL), a distributed learning paradigm that enables local training on user-held data across decentralized devices, is vulnerable to backdoor attacks due to limited visibility into client updates. Exploiting this opacity, adversaries induce targeted misbehavior on trigger inputs without affecting overall performance, thereby compromising the trust and integrity of collaborative training in federated learning systems. Existing federated backdoor attacks mainly concentrate on benign knowledge alignment on trigger-surface design or representation guidance to evade defense mechanisms. However, trigger-surface attacks suffer from insufficient alignment, leaving malicious knowledge distinguishable from benign updates. In contrast, representation-guided attacks attempt to obscure the boundary between benign and malicious behaviors. Nevertheless, excessive incorporation of benign knowledge within a shared parameter space leads to over-alignment, ultimately degrading attack effectiveness. To overcome shared parameter space dilemma in backdoor attack, we propose Batman, a novel backdoor attack that aligns benign knowledge within the malicious null space, which effectively decouples malicious space from shared parameter space and enables benign alignment in an orthogonal direction of this space that does not interfere with the attack effectiveness. To further enhance stealthiness, we combine both clean and global models to guide the alignment perturbation within this null space to evade detection. Experiments on four benchmark datasets demonstrate that Batman consistently achieves strong backdoor performance while remaining stealthy under various defenses.
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Vision-Language Models (VLMs), leveraging their powerful visual perception and reasoning capabilities, have been widely applied in Unmanned Aerial Vehicle (UAV) tasks.However, the spatial intelligence capabilities of existing VLMs in UAV scenarios remain largely unexplored, raising concerns about their effectiveness in navigating and interpreting dynamic environments.To bridge this gap, we introduce SpatialSky-Bench, a comprehensive benchmark specifically designed to evaluate the spatial intelligence capabilities of VLMs in UAV navigation. Our benchmark comprises two categories--Environmental Perception and Scene Understanding--divided into 13 subcategories, including bounding boxes, color, distance, height, and landing safety analysis, among others.Extensive evaluations of various mainstream open-source and closed-source VLMs reveal unsatisfactory performance in complex UAV navigation scenarios, highlighting significant gaps in their spatial capabilities.To address this challenge, we developed the SpatialSky-Dataset, a comprehensive dataset containing 1 M samples with diverse annotations across various scenarios. Leveraging this dataset, we introduce Sky-VLM, a specialized VLM designed for UAV spatial reasoning across multiple granularities and contexts.Extensive experimental results demonstrate that Sky-VLM achieves state-of-the-art performance across all benchmark tasks, paving the way for the development of VLMs suitable for drone scenarios.The source code is available at https://github.com/linglingxiansen/SpatialSKy .
Fighting Hallucinations with Counterfactuals: Diffusion-Guided Perturbations for LVLM Hallucination Suppression
PDF ↗While large vision-language models (LVLMs) achieve strong performance on multimodal tasks, they frequently generate hallucinations--unfaithful outputs misaligned with the visual input. To address this issue, we introduce CIPHER (Counterfactual Image Perturbations for Hallucination Extraction and Removal), a training-free method that suppresses vision-induced hallucinations via lightweight feature-level correction. Unlike prior training-free approaches that primarily focus on text-induced hallucinations, CIPHER explicitly targets hallucinations arising from the visual modality. CIPHER operates in two phases. In the offline phase, we construct OHC-25K (Object-Hallucinated Counterfactuals, 25,000 samples), a counterfactual dataset consisting of diffusion-edited images that intentionally contradict the original ground-truth captions. We pair these edited images with the unchanged ground-truth captions and process them through an LVLM to extract hallucination-related representations. Contrasting these representations with those from authentic (image, caption) pairs reveals structured, systematic shifts spanning a low-rank subspace characterizing vision-induced hallucination. In the inference phase, CIPHER suppresses hallucinations by projecting intermediate hidden states away from this subspace. Experiments across multiple benchmarks show that CIPHER significantly reduces hallucination rates while preserving task performance, demonstrating the effectiveness of counterfactual visual perturbations for improving LVLM faithfulness.
Successful robotic grasping in cluttered environments not only requires a model to visually ground a target object but also to reason about obstructions that must be cleared beforehand. While current vision-language embodied reasoning models show emergent spatial understanding, they remain limited in terms of obstruction reasoning and accessibility planning. To bridge this gap, we present UNOGrasp, a learning-based vision-language model capable of performing visually-grounded obstruction reasoning to infer the sequence of actions needed to unobstruct the path and grasp the target object. We devise a novel multi-step reasoning process based on obstruction paths originated by the target object. We anchor each reasoning step with obstruction-aware visual cues to incentivize reasoning capability. UNOGrasp combines supervised and reinforcement finetuning through verifiable reasoning rewards. Moreover, we construct UNOBench, a large-scale dataset for both training and benchmarking, based on MetaGraspNetV2, with over 100k obstruction paths annotated by humans with obstruction ratios, contact points, and natural-language instructions. Extensive experiments and real-robot evaluations show that UNOGrasp significantly improves obstruction reasoning and grasp success across both synthetic and real-world environments, outperforming generalist and proprietary alternatives.
This paper presents a generalizable CoSOD framework via mixed content-style modulation, termed CoMCS, to enhance the robustness of the model to unseen domains. The CoMCS, consisting of a mixed content modulator (MCM), a mixed style modulator (MSM), and a collaborative semantic contrast module (SCM), effectively extracts scene structure priors as well as augments the source domain styles to bridge the domain gap between the source and the unseen domains. Specifically, the CoMCS first utilizes the CLIP model to extract conceptual knowledge associated with the semantic classes in the whole scene, resulting in multi-class semantic embeddings that are domain-invariant. Subsequently, the MCM models the semantic relationships between the prototypes of co-salient objects and the multi-class semantic embeddings through the cross-attention mechanism, effectively capturing domain-invariant scene structure priors that aid in reducing scene distribution shift in unseen domains. Meanwhile, to alleviate domain perturbations encountered during testing, the MSM addresses the uncertainty associated with domain shifts by synthesizing feature statistics, such as mean and standard deviation, during training to simulate new stylistic characteristics, thus achieving data augmentation within the source domain. Finally, to reduce the ambiguity of the co-salient object representations within test data from unseen domains, the SCM employs a uniform loss function to ensure that the learned prototypes are uniformly distributed within the hyperspherical space, further enhancing the domain generalization capabilities of the framework. Besides, to further verify the generalization ability of the CoMCS to unseen domains, we construct an unseen-domain benchmark dataset (UND) that selects a variety of image groups with unseen classes from CoCA, CoSOD3k, CoSal2015. Extensive evaluations on the four benchmark datasets demonstrate favorable performance of our CoMCS to a variety of state-of-the-art methods.
Multi-period image collections are common in real-world applications. Cities are re-scanned for mapping, construction sites are revisited for progress tracking, and natural regions are monitored for environmental change. Such data form multi-period scenes, where geometry and appearance evolve. Reconstructing such scenes is an important yet underexplored problem. Existing pipelines rely on incompatible assumptions: static and in-the-wild methods enforce a single geometry, while dynamic ones assume smooth motion, both failing under long-term, discontinuous changes. To solve this problem, we introduce ChronoGS, a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold. It's also designed to disentangle stable and evolving components, achieving temporally consistent reconstruction of multi-period scenes. To catalyze relevant research, we release ChronoScene dataset, a benchmark of real and synthetic multi-period scenes, capturing geometric and appearance variation. Experiments demonstrate that ChronoGS consistently outperforms baselines in reconstruction quality and temporal consistency. Our code and the ChronoScene dataset will be made publicly available.
SaPaVe: Towards Active Perception and Manipulation in Vision-Language Action Models for Robotics
PDF ↗Active perception and manipulation are crucial for robots to interact with complex scenes. Existing methods struggle to unify semantic-driven perception actively with robust, viewpoint-invariant execution accordingly. To this end, we propose SaPaVe, an end-to-end framework that jointly learns these capabilities in a data-efficient manner. Central to our approach is a decoupling of camera and manipulation actions, contrary to shared-action-space, and learning in a bottom-up strategy: we first train semantic camera control on our proposed large-scale dataset, then jointly optimizes both action types via hybrid data. To support this, we introduce ActiveViewPose-200K, comprising 200k image-language-camera movement pairs for semantic camera movement learning, and a 3D geometry-aware module that improves execution robustness under dynamic viewpoints. We further present ActiveManip-Bench, the first benchmark filling the gap to evaluate active manipulation. Extensive experiments in both simulation and real-world settings show that SaPaVe outperforms recent VLA models such as GR00T N1 and pi0, achieving up to 31.25% higher success rates in real-world tasks. Our results show that tightly coupled perception and execution, when trained with decoupled yet coordinated strategies, enable efficient and generalizable active manipulation.
Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge for practical automated inspection systems operating in data-scarce environments. While existing approaches predominantly focus on obtaining prototypes from limited normal images, they neglect to systematically incorporate statistics of query image to enhance prototype representativeness. To address this issue, we propose FastRef, a novel and efficient prototype refinement framework for FS-IAD. Our method operates through an iterative two-stage process during inference: (1) characteristic transfer from query features to the enhanced prototypes, and (2) anomaly suppression by aligning the enhanced prototypes with their normal counterparts. The characteristic transfer is achieved through linear reconstruction of query features from prototypes with an optimizable transport matrix, while the anomaly suppression addresses a key observation in FS-IAD that unlike conventional IAD with abundant normal prototypes, the limited-sample setting makes anomaly reconstruction more probable in characteristic transfer. Therefore, we employ optimal transport to measure while minimize the gap between prototypes and their enhanced counterparts for anomaly suppression. For comprehensive evaluation, we integrate FastRef with three competitive prototype-based FS-IAD methods: PatchCore, WinCLIP, and AnomalyDINO. Extensive experiments across four benchmark datasets of MVTec, ViSA, MPDD and RealIAD demonstrate both the effectiveness and efficiency of our approach under 1/2/4-shots.
Medical Visual Question Answering (Med-VQA) holds significant promise for clinical decision support, yet faces challenges due to limited annotated data and the high computational demands of existing large vision-language models. We propose MedFG-VQA, a lightweight framework that leverages a memory bank to augment DCT-based low-frequency features and employs graph-enhanced cross-attention for effective visual-textual alignment. Specifically, our approach features two key components: Frequency-Memory Fusion (FMF), which enhances low-frequency features by retrieving from a learnable memory bank built on DCT decomposition, and Graph-Aware Cross-Attention (GACA), which aligns visual-textual features via cross-attention and refines them through graph-convolutional aggregation. To address data scarcity, we construct SynMed-VQA, a large-scale synthetic dataset comprising over 2 million question-answer pairs across 9 imaging modalities and 10 major organs, generated with GPT-4o. Extensive experiments on SynMed-VQA and three other standard biomedical VQA benchmarks demonstrate that MedFG-VQA achieves competitive or superior performance compared to much larger models while maintaining significantly lower computational costs, highlighting its efficiency and potential for clinical deployment.
Gait recognition enables non-intrusive, privacy-preserving identification but suffers in uncontrolled environments due to illumination and motion sensitivity in conventional cameras. In this work, we explore gait recognition using event cameras, which offer microsecond temporal resolution and high dynamic range, naturally capturing robust dynamic cues and suppressing static noise. Existing event-based approaches typically aggregate event streams into event images over long time windows, thereby discarding fine-grained motion dynamics critical for gait recognition. Therefore, we propose EventGait, an end-to-end dual-stream framework that separately models motion and shape while preserving the advantages of events. Our dynamic stream leverages a Mixture of Spiking Experts (MoSE) with diverse neuron constants for robust dynamic perception across complex motion and illumination scenes, while the static stream learns dense shape representations via Cross-modal Structural Alignment (CroSA) with large vision foundation models. To address the absence of large-scale event-based gait datasets, we introduce a synthesis pipeline and release two new benchmarks: SUSTech1K-E and CCGR-Mini-E. Extensive experiments have shown that event-based gait recognition not only achieves results comparable to camera-based gait recognition under normal conditions but also significantly outperforms it in low-light scenarios. Our approach sets a new state of the art on both synthesized and real-world event-based gait benchmarks, highlighting the robustness and potential of event-driven gait analysis. The code and datasets are released at https://github.com/QUEAHREN/EventGait.
Human driving behavior is inherently personal, which is shaped by long-term habits and influenced by short-term intentions. Individuals differ in how they accelerate, brake, merge, yield, and overtake across diverse situations. However, existing end-to-end autonomous driving systems either optimize for generic objectives or rely on fixed driving modes, lacking the ability to adapt to individual preferences or interpret natural language intent. To address this gap, we propose Drive My Way (DMW), a personalized Vision-Language-Action (VLA) driving framework that aligns with users' long-term driving habits and adapts to real-time user instructions. DMW learns a user embedding from our personalized driving dataset collected across multiple real drivers and conditions the policy on this embedding during planning, while natural language instructions provide additional short-term guidance. Closed-loop evaluation on the Bench2Drive benchmark demonstrates that DMW improves style instruction adaptation, and user studies show that its generated behaviors are recognizable as each driver's own style, highlighting personalization as a key capability for human-centered autonomous driving.
We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved trajectories due to random or batch-wise couplings, which increase discretization error and reduce sample quality. COT-FM fixes this by clustering target samples and assigning each cluster a dedicated source distribution obtained by reversing pretrained FM models. This divide-and-conquer strategy yields more accurate local transport and significantly straighter vector fields, all without changing the model architecture. As a plug-and-play approach, COT-FM consistently accelerates sampling and improves generation quality across 2D datasets, image benchmarks, and robotic manipulation tasks.
Few-shot Font Generation (FFG) aims to create a complete font from a limited number of references, offering significant practical value. However, existing methods neglect glyph spatial information, which leads to two critical limitations. At the pipeline level, distorted rendering introduces spatial bias, impairing vectorization and dataset quality, and this problem is compounded by the lack of unified standards, which undermines a unified benchmark. At the model level, the implicit coupling of shape and position hinders fine-grained optimization and generalization. We address these challenges in the context of Chinese font generation, where glyph complexity demands superior model capability. Consequently, we first propose a Spatial-Preserving Rendering (SPR) scheme, which eliminates spatial bias and enables accurate vectorization. Alongside, we release an OFL-licensed Chinese font dataset to establish a unified benchmark. Then, technically, we propose GlyphSpatialNet, a two-stage framework to explicitly model glyph spatial information in pixel space. In first stage, we design a Shape-Position Decoupling (SPD) architecture and a Gradient Broadcasting Module (GBM) to achieve font style transfer in low resolution. In second stage, we design Style Detail Enhancement (SDE), which refines the style details for high resolution outputs. Extensive experiments demonstrate the effectiveness of our approach. Code and dataset are available at https://github.com/sp777g/GlyphSpatialNet.
Large-scale vision-language models such as CLIP have achieved remarkable success in zero-shot image recognition, yet their predictions remain largely opaque to human understanding. In contrast, Concept Bottleneck Models provide interpretable intermediate representations by reasoning through human-defined concepts, but they rely on concept supervision and lack the ability to generalize to unseen classes. We introduce EZPC that bridges these two paradigms by explaining CLIP's zero-shot predictions through human-understandable concepts. Our method projects CLIP's joint image-text embeddings into a concept space learned from language descriptions, enabling faithful and transparent explanations without additional supervision. The model learns this projection via a combination of alignment and reconstruction objectives, ensuring that concept activations preserve CLIP's semantic structure while remaining interpretable. Extensive experiments on five benchmark datasets, CIFAR-100, CUB-200-2011, Places365, ImageNet-100, and ImageNet-1k, demonstrate that our approach maintains CLIP's strong zero-shot classification accuracy while providing meaningful concept-level explanations. By grounding open-vocabulary predictions in explicit semantic concepts, our method offers a principled step toward interpretable and trustworthy vision-language models. Code is available at https://github.com/oonat/ezpc.
Harmonious Parameter Adaptation in Continual Visual Instruction Tuning for Safety-Aligned MLLMs
PDF ↗While continual visual instruction tuning (CVIT) has shown promise in adapting multimodal large language models (MLLMs), existing studies predominantly focus on models without safety alignment. This critical oversight ignores the fact that real-world MLLMs inherently require such mechanisms to mitigate potential risks. In this work, we shift our focus to CVIT for safety-aligned MLLMs and observe that during continual adaptation, the model not only suffers from task forgetting but also exhibits degradation in its safety. Achieving a harmonious balance between safety and task performance remains a crucial challenge. To address this, we propose Harmonious Parameter Adaptation (HPA), a post-training framework composed of focusing-based parameter partition, harmoniously balanced parameter selection, and orthogonal parameter adjustment. Specifically, HPA partitions parameters into two types based on their focus on safety or task performance, and selects the focused ones to preserve from a balanced perspective. In addition, HPA imposes orthogonality constraints on parameter updates to further alleviate catastrophic forgetting. Extensive experiments on the CVIT benchmark and safety evaluation datasets demonstrate that HPA better maintains high safety and mitigates forgetting than existing baselines. Code is available at https://github.com/Minato-Zackie/HPA.
HandVQA: Diagnosing and Improving Fine-Grained Spatial Reasoning about Hands in Vision-Language Models
PDF ↗Understanding the fine-grained articulation of human hands is critical in high-stakes settings such as robot-assisted surgery, chip manufacturing, and AR/VR-based human-AI interaction. Despite achieving near-human performance on general vision-language benchmarks, current vision-language models (VLMs) struggle with fine-grained spatial reasoning, especially in interpreting complex, articulated hand poses. We introduce HandVQA, a large-scale diagnostic benchmark designed to evaluate VLMs' understanding of detailed hand anatomy through visual question answering. Built upon high-quality 3D hand datasets (FreiHAND, InterHand2.6M, FPHA), our benchmark includes over 1.6M controlled multiple-choice questions that probe spatial relationships between hand joints, such as angles, distances, and relative positions. We evaluate several state-of-the-art VLMs (LLaVA, DeepSeek and Qwen-VL) in both base and fine-tuned settings, using lightweight fine-tuning via LoRA. Our findings reveal systematic limitations in current models, including hallucinated finger parts, incorrect geometric interpretations, and poor generalization. HandVQA not only exposes these critical reasoning gaps but provides a validated path to improvement. We demonstrate that the 3D-grounded spatial knowledge learned from our benchmark transfers in a zero-shot setting, significantly improving accuracy of model on novel downstream tasks like hand gesture recognition (+10.33%) and hand-object interaction (+2.63%).
Recent advances in feed-forward 3D Gaussian splatting have demonstrated remarkable efficiency by reconstructing scenes in a single pass. However, the reconstruction fidelity of these methods lags behind that of traditional optimization-based approaches, which gradually correct reconstruction flaws through a lengthy iterative process. In this paper, we leverage the strengths of both paradigms and introduce iSplat, a novel framework that reformulates reconstruction as an iterative feed-forward process involving multiple (typically three) passes. Central to iSplat is a recurrent GRU-based optimizer that refines both geometry and appearance in a synergistic loop. To address geometric inaccuracies, we propose an uncertainty-driven depth refinement strategy that progressively narrows the search space for each Gaussian based on its estimated uncertainty from the previous step. To further improve appearance details, we design a region-aware enhancement mechanism that applies targeted multi-view and monocular feature aggregation to resolve ambiguities in both overlapping and non-overlapping areas. We validate iSplat's robustness and generalization on in-domain (RealEstate10K, ACID) and cross-dataset (DTU, ACID) benchmarks. With only 42.6M parameters, iSplat surpasses DepthSplat (354M) on RealEstate10K (PSNR: 27.67 vs. 27.47 dB). Crucially, on the cross-dataset DTU benchmark, it further boosts the PSNR by 2.88 dB (18.26 vs. 15.38 dB), showcasing exceptional generalization. These results highlight the significant potential of iterative refinement to overcome the inherent limitations of one-shot approaches. Our code is available at https://github.com/haifengwu205/iSplat.
Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machine-readable and unusable for training machine learning models. In response to this challenge, we propose the RxnCaption framework for the task of chemical Reaction Diagram Parsing (RxnDP). Our framework reformulates the traditional coordinate prediction driven parsing process into an image captioning problem, which Large Vision Language Models (LVLMs) handle naturally. We introduce a strategy termed "BBox and Index as Visual Prompt" (BIVP), which uses our state-of-the-art molecular detector, MolYOLO, to pre-draw molecular bounding boxes and indices directly onto the input image. This turns the downstream parsing into a natural-language description problem. Extensive experiments show that the BIVP strategy significantly improves structural extraction quality while simplifying model design. We further construct the U-RxnDiagram-15k dataset, an order of magnitude larger than prior real-world literature benchmarks, with a balanced test subset across four layout archetypes. Experiments demonstrate that RxnCaption-VL achieves state-of-the-art performance on multiple metrics. We believe our method, dataset, and models will advance structured information extraction from chemical literature and catalyze broader AI applications in chemistry.
Recent advances in vision-language models (VLMs) have significantly enhanced the visual grounding task, which involves locating objects in an image based on natural language queries. Despite these advancements, the security of VLM-based grounding systems has not been thoroughly investigated. This paper reveals a novel and realistic vulnerability: the first multi-target backdoor attack on VLM-based visual grounding. Unlike prior attacks that rely on static triggers or fixed targets, we propose IAG, a method that dynamically generates input-aware, text-guided triggers conditioned on any specified target object description to execute the attack. This is achieved through a text-conditioned UNet that embeds imperceptible target semantic cues into visual inputs while preserving normal grounding performance on benign samples. We further develop a joint training objective that balances language capability with perceptual reconstruction to ensure imperceptibility, effectiveness, and stealth. Extensive experiments on multiple VLMs (e.g., LLaVA, InternVL, Ferret) and benchmarks (RefCOCO, RefCOCO+, RefCOCOg, Flickr30k Entities, and ShowUI) demonstrate that IAG achieves the best ASRs compared with other baselines on almost all settings without compromising clean accuracy, maintaining robustness against existing defenses, and exhibiting transferability across datasets and models. These findings underscore critical security risks in grounding-capable VLMs and highlight the need for further research on trustworthy multimodal understanding. Code is in the supplementary material.
HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation
PDF ↗Lane detection is a crucial task in autonomous driving, which is conducive to ensuring the safe operation of vehicles. However, current datasets like CULane and TuSimple have relatively limited data under extreme weather conditions, such as rain, snow and fog, which makes detection models unreliable in extreme conditions, potentially leading to serious safety-critical failures on the road. In this direction, we propose HG-Lane, a High-fidelity Generation framework for Lane Scenes under adverse weather and lighting conditions, without the need for re-annotation. Based on our framework, we further propose a benchmark that includes adverse weather and lighting conditions, with 30,000 images. Experiment results demonstrate that our method constantly and significantly improves the performance of all the related lane detection networks. Taking the state-of-the-art CLRNet as an example, the overall mF1 on our benchmark increases by 20.87%. The F1@50 for the overall, normal, snow, rain, fog, night, and dusk categories increases by 19.75%, 8.63%, 38.8%, 14.96%, 26.84%, 21.5%, and 12.04%, respectively. The Code and dataset are available at https://github.com/zdc233/HG-Lane.