Computer Vision
DuSCN-FusionNet: An Interpretable Dual-Channel Structural Covariance Fusion Framework for ADHD Classification Using Structural MRI
Qurat Ul Ain, Alptekin Temizel and Soyiba Jawed
Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent neurodevelopmental condition; however, its neurobiological diagnosis remains challenging due to the lack of reliable imaging-based biomarkers, particularly anatomical markers. Structural MRI (sMRI) provides a non-invasive modality for investigating
brain alterations associated with ADHD; nevertheless, most deep learning approaches function as black-box systems, limiting clinical trust and interpretability. In this work, we propose DuSCN-FusionNet, an interpretable sMRI-based framework for ADHD classification that leverages dual-channel Structural Covariance Networks (SCNs) to capture inter-regional morphological relationships. ROI-wise mean intensity and intra-regional variability descriptors are used to construct intensity-based and heterogeneity-based SCNs, which are processed through an SCN-CNN encoder. In parallel, auxiliary ROI-wise variability features and global statistical descriptors are integrated via late-stage fusion to enhance performance. The model is evaluated using stratified 10-fold cross-validation with a 5-seed ensemble strategy, achieving a mean balanced accuracy of 80.59% and an AUC of 0.778 on the Peking University site of the ADHD-200
dataset. DuSCN-FusionNet further achieves precision, recall, and F1-scores of 81.66%, 80.59%, and 80.27%, respectively. Moreover, Grad-CAM is adapted to the SCN domain to derive ROI-level importance scores, enabling the identification of structurally relevant
brain regions as potential biomarkers.
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Computer Vision
Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification
Shuai Lv, Chang Liu, Feng Tang et al. (+5 more)
Multimodal Large
Language Models (MLLMs) achieve strong
multimodal reasoning performance, yet we identify a recurring failure mode in long-form generation: as outputs grow longer, models progressively drift away from image evidence and fall back on textual priors, resulting in ungrounded
reasoning and
hallucinations. Interestingly, Based on
attention analysis, we find that MLLMs have a latent capability for late-stage visual verification that is present but not consistently activated. Motivated by this observation, we propose Visual Re-Examination (VRE), a self-evolving training framework that enables MLLMs to autonomously perform visual introspection during
reasoning without additional visual inputs. Rather than distilling visual capabilities from a stronger teacher, VRE promotes iterative self-improvement by leveraging the model itself to generate reflection traces, making visual information actionable through information gain. Extensive experiments across diverse
multimodal benchmarks demonstrate that VRE consistently improves
reasoning accuracy and perceptual reliability, while substantially reducing
hallucinations, especially in long-chain settings. Code is available at https://github.com/Xiaobu-USTC/VRE.
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All authors Shuai Lv, Chang Liu, Feng Tang, Yujie Yuan, Aojun Zhou, Kui Zhang, Xi Yang, Yangqiu Song
Computer Vision
HINT: Composed Image Retrieval with Dual-path Compositional Contextualized Network
Mingyu Zhang, Zixu Li, Zhiwei Chen et al. (+5 more)
Composed Image
Retrieval (CIR) is a challenging image
retrieval paradigm. It aims to retrieve target images from large-scale image databases that are consistent with the modification semantics, based on a
multimodal query composed of a reference image and modification text. Although existing methods have made significant progress in cross-modal
alignment and feature fusion, a key flaw remains: the neglect of contextual information in discriminating matching samples. However, addressing this limitation is not an easy task due to two challenges: 1) implicit dependencies and 2) the lack of a differential amplification mechanism. To address these challenges, we propose a dual-patH composItional coNtextualized neTwork (HINT), which can perform contextualized encoding and amplify the similarity differences between matching and non-matching samples, thus improving the upper performance of CIR models in complex scenarios. Our HINT model achieves optimal performance on all metrics across two CIR
benchmark datasets, demonstrating the superiority of our HINT model. Codes are available at https://github.com/zh-mingyu/HINT.
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All authors Mingyu Zhang, Zixu Li, Zhiwei Chen, Zhiheng Fu, Xiaowei Zhu, Jiajia Nie, Yinwei Wei, Yupeng Hu
Computer Vision
From Pixels to Privacy: Temporally Consistent Video Anonymization via Token Pruning for Privacy Preserving Action Recognition
Nazia Aslam, Abhisek Ray, Joakim Bruslund Haurum et al. (+2 more)
Recent advances in large-scale
video models have significantly improved
video understanding across domains such as surveillance, healthcare, and entertainment. However, these models also amplify
privacy risks by encoding sensitive attributes, including facial identity, race, and gender. While image anonymization has been extensively studied,
video anonymization remains relatively underexplored, even though modern
video models can leverage spatiotemporal motion patterns as biometric identifiers. To address this challenge, we propose a novel
attention-driven spatiotemporal
video anonymization framework based on systematic disentanglement of utility and
privacy features. Our key insight is that
attention mechanisms in
Vision Transformers (ViTs) can be explicitly structured to separate action-relevant information from
privacy-sensitive content. Building on this insight, we introduce two task-specific classification
tokens, an action CLS
token and a
privacy CLS
token, that learn complementary representations within a shared
Transformer backbone. We contrast their
attention distributions to compute a utility-
privacy score for each spatiotemporal tubelet, and keep the top-k tubelets with the highest scores. This selectively prunes tubelets dominated by
privacy cues while preserving those most critical for action recognition. Extensive experiments demonstrate that our approach maintains action recognition performance comparable to models trained on raw
videos, while substantially reducing
privacy leakage. These results indicate that
attention-driven spatiotemporal
pruning offers an effective and principled solution for
privacy-preserving
video analytics.
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All authors Nazia Aslam, Abhisek Ray, Joakim Bruslund Haurum, Lukas Esterle, Kamal Nasrollahi
Computer Vision
Mitigating the Reasoning Tax in Vision-Language Fine-Tuning with Input-Adaptive Depth Aggregation
Yiming Ren, Yujiu Yang and Junjie Wang
Supervised fine-tuning (SFT) on visual instruction data often improves perceptual capabilities in
vision-
language models (VLMs) while degrading
reasoning performance, creating a persistent
reasoning tax during post-training. We investigate whether this degradation is related to disrupted access to depth-wise representations, and find that even fixed cross-depth aggregation substantially restores
reasoning, suggesting that preserved cross-depth access is an important missing factor in VLM fine-tuning. Building on this observation, we propose Input-Adaptive Depth Aggregation (IADA), a lightweight mechanism that makes cross-depth
retrieval input-adaptive, modality-aware, and efficiently parameterized through a low-rank bottleneck. On Qwen3-VL-2B, IADA improves the average
reasoning score by 9.5 points and the average perception score by $3.3$ points over LoRA-only fine-tuning with only 0.14M additional parameters, with the strongest gains appearing in parameter-efficient low-rank settings.
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Computer Vision
Verify Claimed Text-to-Image Models via Boundary-Aware Prompt Optimization
Zidong Zhao, Yihao Huang, Qing Guo et al. (+5 more)
As Text-to-Image (T2I) generation becomes widespread, third-party platforms increasingly integrate multiple model APIs for convenient image creation. However, false claims of using official models can mislead users and harm model owners' reputations, making model verification essential to confirm whether an API's underlying model matches its claim. Existing methods address this by using verification
prompts generated by official model owners, but the generation relies on multiple reference models for optimization, leading to high computational cost and sensitivity to model selection. To address this problem, we propose a reference-free T2I model verification method called Boundary-aware
Prompt Optimization (BPO). It directly explores the intrinsic characteristics of the target model. The key insight is that although different T2I models produce similar outputs for normal
prompts, their semantic boundaries in the
embedding space (transition zones between two concepts such as "corgi" and "bagel") are distinct.
Prompts near these boundaries generate unstable outputs (e.g., sometimes a corgi and sometimes a bagel) on the target model but remain stable on other models. By identifying such boundary-adjacent
prompts, BPO captures model-specific behaviors that serve as reliable verification cues for distinguishing T2I models. Experiments on five T2I models and four baselines demonstrate that BPO achieves superior verification accuracy.
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All authors Zidong Zhao, Yihao Huang, Qing Guo, Tianlin Li, Anran Li, Kailong Wang, Jin Song Dong, Geguang Pu
Computer Vision · Cross-listed from cs.RO
DFM-VLA: Iterative Action Refinement for Robot Manipulation via Discrete Flow Matching
Jiayi Chen, Wenxuan Song, Jiaxin Fang et al. (+12 more)
Vision-
Language-Action (VLA) models that encode actions using a discrete tokenization scheme have been widely adopted for robotic manipulation, but existing decoding paradigms remain fundamentally limited. Whether actions are decoded sequentially by autoregressive VLAs or in parallel by discrete
diffusion VLAs, once a
token is generated, it is typically fixed and cannot be revised in subsequent iterations. Consequently, early
token errors cannot be effectively corrected later. We propose DFM-VLA, a discrete flow matching VLA that iteratively refines action
tokens. DFM-VLA models a
token-level probability velocity field that dynamically updates the full action sequence across refinement iterations. We investigate two approaches to constructing the velocity field: an auxiliary velocity-head formulation and an
embedding-guided formulation. To further improve prediction accuracy, we introduce a metric-aligned action tokenizer (MAAT) tailored to the coarse-to-fine nature of DFM, together with a two-stage decoding strategy. Extensive experiments on CALVIN, LIBERO, LIBERO-Plus, and real-world manipulation tasks demonstrate the effectiveness of our approach. Our project is available at https://chris1220313648.github.io/DFM-VLA/.
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All authors Jiayi Chen, Wenxuan Song, Jiaxin Fang, Ruiqing Yin, Jingbo Wang, Shuai Chen, Jieyuan Pei, Yikai Qin, Feifan Chen, Haodong Yan, Zhide Zhong, Wen Chen, Yan Wang, Yuxiang Gao, Haoang Li
Computer Vision
Label-Free Cross-Task LoRA Merging with Null-Space Compression
Wonyoung Lee, Wooseong Jeong and Kuk-Jin Yoon
Model merging combines independently fine-tuned checkpoints without joint multi-task training. In the era of foundation-model, fine-tuning with Low-Rank Adaptation (LoRA) is prevalent, making LoRA merging a promising target. Existing approaches can work in homogeneous settings where all target tasks are classification but often fail when tasks span classification and regression. Approaches using
entropy-based surrogates do not apply to regression and are costly for large
language models due to long
token sequences. We introduce Null-Space Compression (NSC) Merging, a label-free, output-agnostic method that sets merge weights from adapter geometry. Our key observation is that during LoRA finetuning the down-projection factor $A$ in $\Delta W = BA$ compresses its null space, and the compression correlates with performance. NSC uses this as an optimization signal for merging that can generalize across classification, regression, and sequence generation. NSC achieves state-of-the-art performance across twenty heterogeneous
vision tasks with balanced gains where prior methods overfit subsets of tasks. It also outperforms baselines on six NLI
benchmarks and on
vision-
language evaluations for VQA and image captioning, demonstrating scalability and effectiveness.
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Computer Vision
SALMUBench: A Benchmark for Sensitive Association-Level Multimodal Unlearning
Cai Selvas-Sala, Lei Kang and Lluis Gomez
As
multimodal models like CLIP become integral to downstream systems, the need to remove sensitive information is critical. However, machine unlearning for contrastively-trained encoders remains underexplored, and existing evaluations fail to diagnose fine-grained, association-level forgetting. We introduce SALMUBench (Sensitive Association-Level
Multimodal Unlearning), a
benchmark built upon a synthetic
dataset of 60K persona-attribute associations and two foundational models: a Compromised model polluted with this data, and a Clean model without it. To isolate unlearning effects, both are trained from scratch on the same 400M-pair retain base, with the Compromised model additionally trained on the sensitive set. We propose a novel evaluation protocol with structured holdout sets (holdout identity, holdout association) to precisely measure unlearning efficacy and collateral damage. Our
benchmark reveals that while utility-efficient deletion is feasible, current methods exhibit distinct failure modes: they either fail to forget effectively or over-generalize by erasing more than intended. SALMUBench sets a new standard for comprehensive unlearning evaluation, and we publicly release our
dataset, models, evaluation scripts, and leaderboards to foster future research.
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Computer Vision
Preference-Aligned LoRA Merging: Preserving Subspace Coverage and Addressing Directional Anisotropy
Wooseong Jeong, Wonyoung Lee and Kuk-Jin Yoon
Merging multiple Low-Rank Adaptation (LoRA) modules is promising for constructing general-purpose systems, yet challenging because LoRA update directions span different subspaces and contribute unevenly. When merged naively, such mismatches can weaken the directions most critical to certain task losses while overemphasizing relatively less important ones, ultimately reducing the model's ability to represent all tasks faithfully. We revisit this problem through two perspectives: subspace coverage, which captures how broadly LoRA directions cover diverse representational directions, and anisotropy, which reflects the imbalance of influence across those directions. We propose TARA-Merging (Task-Rank Anisotropy
Alignment), which aligns merging weights using a preference-weighted cross-
entropy pseudo-loss while preserving task-relevant LoRA subspaces. This ensures broad subspace coverage and mitigates anisotropy via direction-wise reweighting. Across eight
vision and six NLI
benchmarks, TARA-Merging consistently outperforms vanilla and LoRA-aware baselines, demonstrating strong robustness and generalization, and highlighting the importance of addressing both subspace coverage and anisotropy in LoRA merging.
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Computer Vision
PhysVid: Physics Aware Local Conditioning for Generative Video Models
Saurabh Pathak, Elahe Arani, Mykola Pechenizkiy et al. (+1 more)
Generative
video models achieve high visual fidelity but often violate basic physical principles, limiting reliability in real-world settings. Prior attempts to inject physics rely on conditioning: frame-level signals are domain-specific and short-horizon, while global text
prompts are coarse and noisy, missing fine-grained dynamics. We present PhysVid, a physics-aware local conditioning scheme that operates over temporally contiguous chunks of frames. Each chunk is annotated with physics-grounded descriptions of states, interactions, and constraints, which are fused with the global
prompt via chunk-aware cross-
attention during training. At inference, we introduce negative physics
prompts (descriptions of locally relevant law violations) to steer generation away from implausible trajectories. On VideoPhy, PhysVid improves physical commonsense scores by $\approx 33\%$ over baseline
video generators, and by up to $\approx 8\%$ on VideoPhy2. These results show that local, physics-aware guidance substantially increases physical plausibility in generative
video and marks a step toward physics-grounded
video models.
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All authors Saurabh Pathak, Elahe Arani, Mykola Pechenizkiy, Bahram Zonooz
Computer Vision · Cross-listed from cs.AI
GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation
Rui Xie, Zhi Gao, Chenrui Shi et al. (+3 more)
Large
vision-
language models have endowed GUI
agents with strong general capabilities for interface understanding and interaction. However, due to insufficient exposure to domain-specific software operation data during training, these
agents exhibit significant domain bias - they lack familiarity with the specific operation workflows (planning) and UI element layouts (grounding) of particular applications, limiting their real-world task performance. In this paper, we present GUIDE (GUI Unbiasing via Instructional-
Video Driven Expertise), a training-free, plug-and-play framework that resolves GUI
agent domain bias by autonomously acquiring domain-specific expertise from web tutorial
videos through a
retrieval-augmented automated annotation pipeline. GUIDE introduces two key innovations. First, a subtitle-driven
Video-RAG pipeline unlocks
video semantics through subtitle analysis, performing progressive three-stage
retrieval - domain classification, topic extraction, and relevance matching - to identify task-relevant tutorial
videos. Second, a fully automated annotation pipeline built on an inverse dynamics paradigm feeds consecutive keyframes enhanced with UI element detection into VLMs, inferring the required planning and grounding knowledge that are injected into the
agent's corresponding modules to address both manifestations of domain bias. Extensive experiments on OSWorld demonstrate GUIDE's generality as a plug-and-play component for both multi-
agent systems and single-model
agents. It consistently yields over 5% improvements and reduces execution steps - without modifying any model parameters or architecture - validating GUIDE as an architecture-agnostic enhancement to bridge GUI
agent domain bias.
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All authors Rui Xie, Zhi Gao, Chenrui Shi, Zirui Shang, Lu Chen, Qing Li
Computer Vision
DRUM: Diffusion-based Raydrop-aware Unpaired Mapping for Sim2Real LiDAR Segmentation
Tomoya Miyawaki, Kazuto Nakashima, Yumi Iwashita et al. (+1 more)
LiDAR-based semantic
segmentation is a key component for autonomous mobile
robots, yet large-scale annotation of
LiDAR point clouds is prohibitively expensive and time-consuming. Although simulators can provide labeled synthetic data, models trained on synthetic data often underperform on real-world data due to a data-level domain gap. To address this issue, we propose DRUM, a novel Sim2Real translation framework. We leverage a
diffusion model pre-trained on unlabeled real-world data as a generative prior and translate synthetic data by reproducing two key measurement characteristics: reflectance intensity and raydrop noise. To improve sample fidelity, we introduce a raydrop-aware masked guidance mechanism that selectively enforces consistency with the input synthetic data while preserving realistic raydrop noise induced by the
diffusion prior. Experimental results demonstrate that DRUM consistently improves Sim2Real performance across multiple representations of
LiDAR data. The project page is available at https://miya-tomoya.github.io/drum.
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All authors Tomoya Miyawaki, Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume
Computer Vision
GLASS: Geometry-aware Local Alignment and Structure Synchronization Network for 2D-3D Registration
Zhixin Cheng, Jiacheng Deng, Xinjun Li et al. (+5 more)
Image-to-point cloud registration methods typically follow a coarse-to-fine pipeline, extracting patch-level correspondences and refining them into dense pixel-to-point matches. However, in scenes with repetitive patterns, images often lack sufficient 3D structural cues and
alignment with point clouds, leading to incorrect matches. Moreover, prior methods usually overlook structural consistency, limiting the full exploitation of correspondences. To address these issues, we propose two novel modules: the Local Geometry Enhancement (LGE) module and the Graph Distribution Consistency (GDC) module. LGE enhances both image and point cloud features with normal vectors, injecting
geometric structure into image features to reduce mismatches. GDC constructs a graph from matched points to update features and explicitly constrain similarity distributions. Extensive experiments and ablations on two
benchmarks, RGB-D Scenes v2 and 7-Scenes, demonstrate that our approach achieves state-of-the-art performance in image-to-point cloud registration.
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All authors Zhixin Cheng, Jiacheng Deng, Xinjun Li, Bohao Liao, Li Liu, Xiaotian Yin, Baoqun Yin, Tianzhu Zhang
Computer Vision
GeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation
Xujing Tao, Chuxin Wang, Yubo Ai et al. (+8 more)
Open-vocabulary 3D semantic
segmentation aims to segment arbitrary categories beyond the training set. Existing methods predominantly rely on distilling knowledge from 2D open-vocabulary models. However, aligning 3D features to the 2D representation space restricts intrinsic 3D
geometric learning and inherits errors from 2D predictions. To address these limitations, we propose GeoGuide, a novel framework that leverages pretrained 3D models to integrate hierarchical geometry-semantic consistency for open-vocabulary 3D
segmentation. Specifically, we introduce an Uncertainty-based Superpoint
Distillation module to fuse
geometric and semantic features for estimating per-point uncertainty, adaptively weighting 2D features within superpoints to suppress noise while preserving discriminative information to enhance local semantic consistency. Furthermore, our Instance-level Mask Reconstruction module leverages
geometric priors to enforce semantic consistency within instances by reconstructing complete instance masks. Additionally, our Inter-Instance Relation Consistency module aligns
geometric and semantic similarity
matrices to calibrate cross-instance consistency for same-category objects, mitigating viewpoint-induced semantic drift. Extensive experiments on ScanNet v2, Matterport3D, and nuScenes demonstrate the superior performance of GeoGuide.
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All authors Xujing Tao, Chuxin Wang, Yubo Ai, Zhixin Cheng, Zhuoyuan Li, Liangsheng Liu, Yujia Chen, Xinjun Li, Qiao Li, Wenfei Yang, Tianzhu Zhang
Computer Vision
ARTA: Adaptive Mixed-Resolution Token Allocation for Efficient Dense Feature Extraction
David Hagerman, Roman Naeem, Erik Brorsson et al. (+2 more)
We present ARTA, a mixed-resolution coarse-to-fine
vision transformer for efficient dense feature extraction. Unlike models that begin with dense high-resolution (fine)
tokens, ARTA starts with low-resolution (coarse)
tokens and uses a lightweight allocator to predict which regions require more fine
tokens. The allocator iteratively predicts a semantic (class) boundary score and allocates additional
tokens to patches above a low threshold, concentrating
token density near boundaries while maintaining high sensitivity to weak boundary evidence. This targeted allocation encourages
tokens to represent a single semantic class rather than a mixture of classes. Mixed-resolution
attention enables interaction between coarse and fine
tokens, focusing computation on semantically complex areas while avoiding redundant processing in homogeneous regions. Experiments demonstrate that ARTA achieves state-of-the-art results on ADE20K and COCO-Stuff with substantially fewer FLOPs, and delivers competitive performance on Cityscapes at markedly lower compute. For example, ARTA-Base attains 54.6 mIoU on ADE20K in the ~100M-parameter class while using fewer FLOPs and less
memory than comparable backbones.
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All authors David Hagerman, Roman Naeem, Erik Brorsson, Fredrik Kahl, Lennart Svensson
Computer Vision
Real-Time Branch-to-Tool Distance Estimation for Autonomous UAV Pruning: Benchmarking Five DEFOM-Stereo Variants from Simulation to Jetson Deployment
Yida Lin, Bing Xue, Mengjie Zhang et al. (+2 more)
Autonomous tree
pruning with unmanned aerial vehicles (
UAVs) is a safety-critical real-world task: the onboard perception system must estimate the metric distance from a cutting tool to thin tree branches in real time so that the
UAV can approach, align, and actuate the pruner without collision. We address this problem by training five variants of DEFOM-Stereo - a recent foundation-model-based stereo matcher - on a task-specific synthetic
dataset and deploying the checkpoints on an NVIDIA Jetson Orin Super 16 GB. The training corpus is built in Unreal Engine 5 with a simulated ZED Mini stereo camera capturing 5,520 stereo pairs across 115 tree instances from three viewpoints at 2m distance; dense EXR depth maps provide exact, spatially complete supervision for thin branches. On the synthetic test set, DEFOM-Stereo ViT-S achieves the best depth-domain accuracy (EPE 1.74 px, D1-all 5.81%, delta-1 95.90%, depth MAE 23.40 cm) but its Jetson inference speed of ~2.2 FPS (~450 ms per frame) remains too slow for responsive closed-loop tool control. A newly introduced balanced variant, DEFOM-PrunePlus (~21M backbone, ~3.3 FPS on Jetson), offers the best deployable accuracy-speed trade-off (EPE 5.87 px, depth MAE 64.26 cm, delta-1 87.59%): its frame rate is sufficient for real-time guidance and its depth accuracy supports safe branch approach planning at the 2m operating range. The lightweight DEFOM-PruneStereo (~6.9 FPS) and DEFOM-PruneNano (~8.5 FPS) run fast but sacrifice substantial accuracy (depth MAE > 57 cm), making estimates too unreliable for safe actuation. Zero-shot inference on real photographs confirms that full-capacity models preserve branch geometry, validating the sim-to-real transfer. We conclude that DEFOM-PrunePlus provides the most practical accuracy-latency balance for onboard distance estimation, while ViT-S serves as the reference for future hardware.
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All authors Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield, Richard Green
Computer Vision
Shrinidhi Kumbhar, Haofu Liao, Srikar Appalaraju et al. (+1 more)
Autoregressive (AR)
vision-
language models (VLMs) have long dominated
multimodal understanding,
reasoning, and graphical user interface (GUI) grounding. Recently, discrete
diffusion vision-
language models (DVLMs) have shown strong performance in
multimodal reasoning, offering bidirectional
attention, parallel
token generation, and iterative refinement. However, their potential for GUI grounding remains unexplored. In this work, we evaluate whether discrete DVLMs can serve as a viable alternative to AR models for GUI grounding. We adapt LLaDA-V for single-turn action and bounding-box prediction, framing the task as text generation from
multimodal input. To better capture the hierarchical structure of bounding-box geometry, we propose a hybrid masking schedule that combines linear and deterministic masking, improving grounding accuracy by up to 6.1 points in Step Success Rate (SSR) over the GUI-adapted LLaDA-V trained with linear masking. Evaluations on four
datasets spanning web, desktop, and mobile interfaces show that the adapted
diffusion model with hybrid masking consistently outperforms the linear-masked variant and performs competitively with autoregressive counterparts despite limited pretraining. Systematic ablations reveal that increasing
diffusion steps, generation length, and block length improves accuracy but also increases latency, with accuracy plateauing beyond a certain number of
diffusion steps. Expanding the training data with diverse GUI domains further reduces latency by about 1.3 seconds and improves grounding accuracy by an average of 20 points across
benchmarks. These results demonstrate that discrete DVLMs are a promising modeling framework for GUI grounding and represent an important step toward
diffusion-based GUI
agents.
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All authors Shrinidhi Kumbhar, Haofu Liao, Srikar Appalaraju, Kunwar Yashraj Singh
Computer Vision
4DRaL: Bridging 4D Radar with LiDAR for Place Recognition using Knowledge Distillation
Ningyuan Huang, Zhiheng Li and Zheng Fang
Place recognition is crucial for loop closure detection and global localization in robotics. Although mainstream
algorithms typically rely on cameras and
LiDAR, these sensors are susceptible to adverse weather conditions. Fortunately, the recently developed 4D millimeter-wave radar (4D radar) offers a promising solution for all-weather place recognition. However, the inherent noise and sparsity in 4D radar data significantly limit its performance. Thus, in this paper, we propose a novel framework called 4DRaL that leverages knowledge
distillation (KD) to enhance the place recognition performance of 4D radar. Its core is to adopt a high-performance
LiDAR-to-
LiDAR (L2L) place recognition model as a teacher to guide the training of a 4D radar-to-4D radar (R2R) place recognition model. 4DRaL comprises three key KD modules: a local image enhancement module to handle the sparsity of raw 4D radar points, a feature distribution
distillation module that ensures the student model generates more discriminative features, and a response
distillation module to maintain consistency in feature space between the teacher and student models. More importantly, 4DRaL can also be trained for 4D radar-to-
LiDAR (R2L) place recognition through different module configurations. Experimental results prove that 4DRaL achieves state-of-the-art performance in both R2R and R2L tasks regardless of normal or adverse weather.
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Computer Vision · Cross-listed from cs.IT
SAFT: Sensitivity-Aware Filtering and Transmission for Adaptive 3D Point Cloud Communication over Wireless Channels
Huda Adam Sirag Mekki, Hui Yuan, Mohanad M. G. Hassan et al. (+2 more)
Reliable transmission of 3D point clouds over wireless channels is challenging due to time-varying signal-to-noise ratio (SNR) and limited bandwidth. This paper introduces sensitivity-aware filtering and transmission (SAFT), a learned transmission framework that integrates a Point-BERT-inspired encoder, a sensitivity-guided
token filtering (STF) unit, a quantization block, and an SNR-aware decoder for adaptive reconstruction. Specifically, the STF module assigns
token-wise importance scores based on the reconstruction sensitivity of each
token under channel perturbation. We further employ a training-only symbol-usage penalty to stabilize the discrete representation, without affecting the transmitted payload. Experiments on ShapeNet, ModelNet40, and 8iVFB show that SAFT improves
geometric fidelity (D1/D2 PSNR) compared with a separate source–channel coding pipeline (G-PCC combined with LDPC and QAM) and existing learned baselines, with the largest gains observed in low-SNR regimes, highlighting improved robustness under limited bandwidth.
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All authors Huda Adam Sirag Mekki, Hui Yuan, Mohanad M. G. Hassan, Zejia Chen, Guanghui Zhang
Computer Vision
MemCam: Memory-Augmented Camera Control for Consistent Video Generation
Xinhang Gao, Junlin Guan, Shuhan Luo et al. (+3 more)
Interactive
video generation has significant potential for scene simulation and
video creation. However, existing methods often struggle with maintaining scene consistency during long
video generation under dynamic camera control due to limited contextual information. To address this challenge, we propose MemCam, a
memory-augmented interactive
video generation approach that treats previously generated frames as external
memory and leverages them as contextual conditioning to achieve controllable camera viewpoints with high scene consistency. To enable longer and more relevant context, we design a context compression module that encodes
memory frames into compact representations and employs co-visibility-based selection to dynamically retrieve the most relevant historical frames, thereby reducing computational overhead while enriching contextual information. Experiments on interactive
video generation tasks show that MemCam significantly outperforms existing baseline methods as well as open-source state-of-the-art approaches in terms of scene consistency, particularly in long
video scenarios with large camera rotations.
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All authors Xinhang Gao, Junlin Guan, Shuhan Luo, Wenzhuo Li, Guanghuan Tan, Jiacheng Wang
Computer Vision
HAD: Heterogeneity-Aware Distillation for Lifelong Heterogeneous Learning
Xuerui Zhang, Xuehao Wang, Zhan Zhuang et al. (+5 more)
Lifelong learning aims to preserve knowledge acquired from previous tasks while incorporating knowledge from a sequence of new tasks. However, most prior work explores only streams of homogeneous tasks (e.g., only classification tasks) and neglects the scenario of learning across heterogeneous tasks that possess different structures of outputs. In this work, we formalize this broader setting as lifelong heterogeneous learning (LHL). Departing from conventional lifelong learning, the task sequence of LHL spans different task types, and the learner needs to retain heterogeneous knowledge for different output space structures. To instantiate the LHL, we focus on LHL in the context of dense prediction (LHL4DP), a realistic and challenging scenario. To this end, we propose the Heterogeneity-Aware
Distillation (HAD) method, an exemplar-free approach that preserves previously gained heterogeneous knowledge by self-
distillation in each training phase. The proposed HAD comprises two complementary components, including a distribution-balanced heterogeneity-aware
distillation loss to alleviate the global imbalance of prediction distribution and a salience-guided heterogeneity-aware
distillation loss that concentrates learning on informative edge pixels extracted with the Sobel operator. Extensive experiments demonstrate that the proposed HAD method significantly outperforms existing methods in this new scenario.
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All authors Xuerui Zhang, Xuehao Wang, Zhan Zhuang, Linglan Zhao, Ziyue Li, Xinmin Zhang, Zhihuan Song, Yu Zhang
Computer Vision
Dual-Stage Invariant Continual Learning under Extreme Visual Sparsity
Rangya Zhang, Jiaping Xiao, Lu Bai et al. (+2 more)
Continual learning seeks to maintain stable adaptation under non-stationary environments, yet this problem becomes particularly challenging in object detection, where most existing methods implicitly assume relatively balanced visual conditions. In extreme-sparsity regimes, such as those observed in space-based resident space object (RSO) detection scenarios, foreground signals are overwhelmingly dominated by background observations. Under such conditions, we analytically demonstrate that background-driven gradients destabilize the feature backbone during sequential domain shifts, causing progressive representation drift. This exposes a structural limitation of continual learning approaches relying solely on output-level
distillation, as they fail to preserve intermediate representation stability. To address this, we propose a dual-stage invariant continual learning framework via joint
distillation, enforcing structural and semantic consistency on both backbone representations and detection predictions, respectively, thereby suppressing error propagation at its source while maintaining adaptability. Furthermore, to regulate gradient statistics under severe imbalance, we introduce a sparsity-aware data conditioning strategy combining patch-based sampling and distribution-aware augmentation. Experiments on a high-resolution space-based RSO detection
dataset show consistent improvement over established continual object detection methods, achieving an absolute gain of +4.0 mAP under sequential domain shifts.
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All authors Rangya Zhang, Jiaping Xiao, Lu Bai, Yuhang Zhang, Mir Feroskhan
Computer Vision
OSA: Echocardiography Video Segmentation via Orthogonalized State Update and Anatomical Prior-aware Feature Enhancement
Rui Wang, Huisi Wu and Jing Qin
Accurate and temporally consistent
segmentation of the left ventricle from echocardiography
videos is essential for estimating the ejection fraction and assessing cardiac function. However, modeling spatiotemporal dynamics remains difficult due to severe speckle noise and rapid non-rigid deformations. Existing linear recurrent models offer efficient in-context associative recall for temporal tracking, but rely on unconstrained state updates, which cause progressive singular value decay in the state
matrix, a phenomenon known as rank collapse, resulting in anatomical details being overwhelmed by noise. To address this, we propose OSA, a framework that constrains the state
evolution on the Stiefel
manifold. We introduce the Orthogonalized State Update (OSU) mechanism, which formulates the
memory evolution as Euclidean projected gradient descent on the Stiefel
manifold to prevent rank collapse and maintain stable temporal transitions. Furthermore, an Anatomical Prior-aware Feature Enhancement module explicitly separates anatomical structures from speckle noise through a physics-driven process, providing the temporal tracker with noise-resilient structural cues. Comprehensive experiments on the CAMUS and EchoNet-Dynamic
datasets show that OSA achieves state-of-the-art
segmentation accuracy and temporal stability, while maintaining real-time inference efficiency for clinical deployment. Codes are available at https://github.com/wangrui2025/OSA.
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Computer Vision
Progressive Learning with Anatomical Priors for Reliable Left Atrial Scar Segmentation from Late Gadolinium Enhancement MRI
Jing Zhang, Bastien Bergere, Emilie Bollache et al. (+4 more)
Cardiac MRI late gadolinium enhancement (LGE) enables non-invasive identification of left atrial (LA) scar, whose spatial distribution is strongly associated with atrial fibrillation (AF) severity and recurrence. However, automatic LA scar
segmentation remains challenging due to low contrast, annotation variability, and the lack of anatomical constraints, often leading to non-reliable predictions. Accordingly, our aim was to propose a progressive learning strategy to segment LA scar from LGE images inspired from a clinical workflow. A 3-stage framework based on SwinUNETR was implemented, comprising: 1) a first LA cavity pre-learning model, 2) dual-task model which further learns spatial relationship between LA geometry and scar patterns, and 3) fine-tuning on precise
segmentation of the scar. Furthermore, we introduced an anatomy-aware spatially weighted loss that incorporates prior clinical knowledge by constraining scar predictions to anatomically plausible LA wall regions while mitigating annotation bias. Our preliminary results obtained on validation LGE volumes from LASCARQS public
dataset after 5-fold cross validation, LA
segmentation had Dice score of 0.94, LA scar
segmentation achieved Dice score of 0.50, Hausdorff Distance of 11.84 mm, Average Surface Distance of 1.80 mm, outperforming only a one-stage scar
segmentation with 0.49, 13.02 mm, 1.96 mm, repectively. By explicitly
embedding clinical anatomical priors and diagnostic
reasoning into deep learning, the proposed approach improved the accuracy and reliability of LA scar
segmentation from LGE, revealing the importance of clinically informed model design.
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All authors Jing Zhang, Bastien Bergere, Emilie Bollache, Jonas Leite, Mikaël Laredo, Alban Redheuil, Nadjia Kachenoura
Computer Vision
DUGAE: Unified Geometry and Attribute Enhancement via Spatiotemporal Correlations for G-PCC Compressed Dynamic Point Clouds
Pan Zhao, Hui Yuan, Chang Sun et al. (+3 more)
Existing post-decoding quality enhancement methods for point clouds are designed for static data and typically process each frame independently. As a result, they cannot effectively exploit the spatiotemporal correlations present in point cloud sequences.We propose a unified geometry and attribute enhancement framework (DUGAE) for G-PCC compressed dynamic point clouds that explicitly exploits inter-frame spatiotemporal correlations in both geometry and attributes. First, a dynamic geometry enhancement network (DGE-Net) based on sparse convolution (SPConv) and feature-domain geometry motion compensation (GMC) aligns and aggregates spatiotemporal information. Then, a detail-aware k-nearest neighbors (DA-KNN) recoloring module maps the original attributes onto the enhanced geometry at the encoder side, improving mapping completeness and preserving attribute details. Finally, a dynamic attribute enhancement network (DAE-Net) with dedicated temporal feature extraction and feature-domain attribute motion compensation (AMC) refines attributes by modeling complex spatiotemporal correlations. On seven dynamic point clouds from the 8iVFB v2, Owlii, and MVUB
datasets, DUGAE significantly enhanced the performance of the latest G-PCC geometry-based solid content test model (GeS-TM v10). For geometry (D1), it achieved an average BD-PSNR gain of 11.03 dB and a 93.95% BD-bitrate reduction. For the luma component, it achieved a 4.23 dB BD-PSNR gain with a 66.61% BD-bitrate reduction. DUGAE also improved perceptual quality (as measured by PCQM) and outperformed V-PCC. Our source code will be released on GitHub at: https://github.com/yuanhui0325/DUGAE
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All authors Pan Zhao, Hui Yuan, Chang Sun, Chongzhen Tian, Raouf Hamzaoui, Sam Kwong
Computer Vision
GLINT: Modeling Scene-Scale Transparency via Gaussian Radiance Transport
Youngju Na, Jaeseong Yun, Soohyun Ryu et al. (+3 more)
While 3D Gaussian splatting has emerged as a powerful paradigm, it fundamentally fails to model transparency such as glass panels. The core challenge lies in decoupling the intertwined radiance contributions from transparent interfaces and the transmitted geometry observed through the glass. We present GLINT, a framework that models scene-scale transparency through explicit decomposed Gaussian representation. GLINT reconstructs the primary interface and models reflected and transmitted radiance separately, enabling consistent radiance transport. During optimization, GLINT bootstraps transparency localization from geometry-separation cues induced by the decomposition, together with geometry and material priors from a pre-trained
video relighting model. Extensive experiments demonstrate consistent improvements over prior methods for reconstructing complex transparent scenes.
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All authors Youngju Na, Jaeseong Yun, Soohyun Ryu, Hyunsu Kim, Sung-Eui Yoon, Suyong Yeon
Computer Vision
Consistency Beyond Contrast: Enhancing Open-Vocabulary Object Detection Robustness via Contextual Consistency Learning
Bozhao Li, Shaocong Wu, Tong Shao et al. (+4 more)
Recent advances in open-vocabulary object detection focus primarily on two aspects: scaling up
datasets and leveraging contrastive learning to align
language and
vision modalities. However, these approaches often neglect internal consistency within a single modality, particularly when background or environmental changes occur. This lack of consistency leads to a performance drop because the model struggles to detect the same object in different scenes, which reveals a robustness gap. To address this issue, we introduce Contextual Consistency Learning (CCL), a novel framework that integrates two key strategies: Contextual Bootstrapped Data Generation (CBDG) and Contextual Consistency Loss (CCLoss). CBDG functions as a data generation mechanism, producing images that contain the same objects across diverse backgrounds. This is essential because existing
datasets alone do not support our CCL framework. The CCLoss further enforces the invariance of object features despite environmental changes, thereby improving the model's robustness in different scenes. These strategies collectively form a unified framework for ensuring contextual consistency within the same modality. Our method achieves state-of-the-art performance, surpassing previous approaches by +16.3 AP on OmniLabel and +14.9 AP on D3. These results demonstrate the importance of enforcing intra-modal consistency, significantly enhancing model generalization in diverse environments. Our code is publicly available at: https://github.com/bozhao-li/CCL.
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All authors Bozhao Li, Shaocong Wu, Tong Shao, Senqiao Yang, Qiben Shan, Zhuotao Tian, Jingyong Su
Computer Vision
CREval: An Automated Interpretable Evaluation for Creative Image Manipulation under Complex Instructions
Chonghuinan Wang, Zihan Chen, Yuxiang Wei et al. (+5 more)
Instruction-based
multimodal image manipulation has recently made rapid progress. However, existing evaluation methods lack a systematic and human-aligned framework for assessing model performance on complex and creative editing tasks. To address this gap, we propose CREval, a fully automated question-answer (QA)-based evaluation pipeline that overcomes the incompleteness and poor interpretability of opaque
Multimodal Large
Language Models (MLLMs) scoring. Simultaneously, we introduce CREval-Bench, a comprehensive
benchmark specifically designed for creative image manipulation under complex instructions. CREval-Bench covers three categories and nine creative dimensions, comprising over 800 editing samples and 13K evaluation queries. Leveraging this pipeline and
benchmark, we systematically evaluate a diverse set of state-of-the-art open and closed-source models. The results reveal that while closed-source models generally outperform open-source ones on complex and creative tasks, all models still struggle to complete such edits effectively. In addition, user studies demonstrate strong consistency between CREval's automated metrics and human judgments. Therefore, CREval provides a reliable foundation for evaluating image editing models on complex and creative image manipulation tasks, and highlights key challenges and opportunities for future research.
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All authors Chonghuinan Wang, Zihan Chen, Yuxiang Wei, Tianyi Jiang, Xiaohe Wu, Fan Li, Wangmeng Zuo, Hongxun Yao
Computer Vision · Cross-listed from cs.MM
ComVi: Context-Aware Optimized Comment Display in Video Playback
Minsun Kim, Dawon Lee and Junyong Noh
On general
video-sharing platforms like YouTube, comments are displayed independently of
video playback. As viewers often read comments while watching a
video, they may encounter ones referring to moments unrelated to the current scene, which can reveal spoilers and disrupt immersion. To address this problem, we present ComVi, a novel system that displays comments at contextually relevant moments, enabling viewers to see time-synchronized comments and
video content together. We first map all comments to relevant
video timestamps by computing audio-visual correlation, then construct the comment sequence through an optimization that considers temporal relevance, popularity (number of likes), and display duration for comfortable reading. In a user study, ComVi provided a significantly more engaging experience than conventional
video interfaces (i.e., YouTube and Danmaku), with 71.9% of participants selecting ComVi as their most preferred interface.
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