Computer Vision
HydroSense: A Dual-Microcontroller IoT Framework for Real-Time Multi-Parameter Water Quality Monitoring with Edge Processing and Cloud Analytics
Abdul Hasib, A. S. M. Ahsanul Sarkar Akib and Anish Giri
The global water crisis necessitates affordable, accurate, and real-time water quality monitoring solutions. Traditional approaches relying on manual sampling or expensive commercial systems fail to address accessibility challenges in resource-constrained environments. This paper presents HydroSense, an innovative Internet of Things framework that integrates six critical water quality parameters including pH, dissolved oxygen (DO), temperature, total dissolved solids (TDS), estimated nitrogen, and water level into a unified monitoring system. HydroSense employs a novel dual-microcontroller architecture, utilizing Arduino Uno for precision analog measurements with five-point calibration
algorithms and ESP32 for wireless connectivity, edge processing, and cloud integration. The system implements advanced signal processing techniques including median filtering for TDS measurement, temperature compensation
algorithms, and robust error handling. Experimental validation over 90 days demonstrates exceptional performance metrics: pH accuracy of plus or minus 0.08 units across the 0 to 14 range, DO measurement stability within plus or minus 0.2 mg/L, TDS accuracy of plus or minus 1.9 percent across 0 to 1000 ppm, and 99.8 percent cloud data transmission reliability. With a total implementation cost of 32,983 BDT (approximately 300 USD), HydroSense achieves an 85 percent cost reduction compared to commercial systems while providing enhanced connectivity through the Firebase real-time database. This research establishes a new paradigm for accessible environmental monitoring, demonstrating that professional-grade water quality assessment can be achieved through intelligent system architecture and cost-effective component selection.
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Computer Vision
Unifying Heterogeneous Degradations: Uncertainty-Aware Diffusion Bridge Model for All-in-One Image Restoration
Luwei Tu, Jiawei Wu, Xing Luo et al. (+1 more)
All-in-One Image Restoration (AiOIR) faces the fundamental challenge in reconciling conflicting optimization objectives across heterogeneous degradations. Existing methods are often constrained by coarse-grained control mechanisms or fixed mapping schedules, yielding suboptimal adaptation. To address this, we propose an Uncertainty-Aware
Diffusion Bridge Model (UDBM), which innovatively reformulates AiOIR as a
stochastic transport problem steered by pixel-wise uncertainty. By introducing a relaxed
diffusion bridge formulation which replaces the strict terminal constraint with a relaxed constraint, we model the uncertainty of degradations while theoretically resolving the drift singularity inherent in standard
diffusion bridges. Furthermore, we devise a dual modulation strategy: the noise schedule aligns diverse degradations into a shared high-
entropy latent space, while the path schedule adaptively regulates the transport trajectory motivated by the viscous dynamics of
entropy regularization. By effectively rectifying the transport geometry and dynamics, UDBM achieves state-of-the-art performance across diverse restoration tasks within a single inference step.
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All authors Luwei Tu, Jiawei Wu, Xing Luo, Zhi Jin
Computer Vision
Bi-Anchor Interpolation Solver for Accelerating Generative Modeling
Hongxu Chen, Hongxiang Li, Zhen Wang et al. (+1 more)
Flow Matching (FM) models have emerged as a leading paradigm for high-fidelity synthesis. However, their reliance on iterative Ordinary Differential Equation (ODE) solving creates a significant latency bottleneck. Existing solutions face a dichotomy: training-free solvers suffer from significant performance degradation at low Neural Function Evaluations (NFEs), while training-based one- or few-steps generation methods incur prohibitive training costs and lack plug-and-play versatility. To bridge this gap, we propose the Bi-Anchor Interpolation Solver (BA-solver). BA-solver retains the versatility of standard training-free solvers while achieving significant acceleration by introducing a lightweight SideNet (1-2% backbone size) alongside the frozen backbone. Specifically, our method is founded on two synergistic components: 1) Bidirectional Temporal Perception, where the SideNet learns to approximate both future and historical velocities without retraining the heavy backbone; and 2) Bi-Anchor Velocity Integration, which utilizes the SideNet with two anchor velocities to efficiently approximate intermediate velocities for batched high-order integration. By utilizing the backbone to establish high-precision “anchors” and the SideNet to densify the trajectory, BA-solver enables large interval sizes with minimized error. Empirical results on ImageNet-256^2 demonstrate that BA-solver achieves generation quality comparable to 100+ NFEs Euler solver in just 10 NFEs and maintains high fidelity in as few as 5 NFEs, incurring negligible training costs. Furthermore, BA-solver ensures seamless integration with existing generative pipelines, facilitating downstream tasks such as image editing.
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All authors Hongxu Chen, Hongxiang Li, Zhen Wang, Long Chen
Computer Vision
Vision KAN: Towards an Attention-Free Backbone for Vision with Kolmogorov-Arnold Networks
Zhuoqin Yang, Jiansong Zhang, Xiaoling Luo et al. (+3 more)
Attention mechanisms have become a key module in modern
vision backbones due to their ability to model long-range dependencies. However, their quadratic complexity in sequence length and the difficulty of interpreting
attention weights limit both scalability and clarity. Recent
attention-free architectures demonstrate that strong performance can be achieved without pairwise
attention, motivating the search for alternatives. In this work, we introduce
Vision KAN (ViK), an
attention-free backbone inspired by the Kolmogorov-Arnold Networks. At its core lies MultiPatch-RBFKAN, a unified
token mixer that combines (a) patch-wise nonlinear transform with Radial Basis Function-based KANs, (b) axis-wise separable mixing for efficient local propagation, and (c) low-rank global mapping for long-range interaction. Employing as a drop-in replacement for
attention modules, this formulation tackles the prohibitive cost of full KANs on high-resolution features by adopting a patch-wise grouping strategy with lightweight operators to restore cross-patch dependencies. Experiments on ImageNet-1K show that ViK achieves competitive accuracy with linear complexity, demonstrating the potential of KAN-based
token mixing as an efficient and theoretically grounded alternative to
attention.
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All authors Zhuoqin Yang, Jiansong Zhang, Xiaoling Luo, Xu Wu, Zheng Lu, Linlin Shen
Computer Vision · Cross-listed from cs.CR
On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
Xinwei Zhang, Hangcheng Liu, Li Bai et al. (+4 more)
Visual
token compression is widely used to accelerate large
vision-
language models (LVLMs) by
pruning or merging visual
tokens, yet its
adversarial robustness remains unexplored. We show that existing encoder-based attacks cannot fully disclose the robustness vulnerabilities of compressed LVLMs, due to an optimization-inference mismatch: perturbations are optimized on the full-
token representation, while inference is performed through a
token-compression bottleneck. To address this gap, we propose the Compression-AliGnEd attack (CAGE), which aligns perturbation optimization with compression inference without assuming access to the deployed compression mechanism or its
token budget. CAGE combines (i) expected feature disruption, which concentrates distortion on
tokens likely to survive across plausible budgets, and (ii) rank distortion
alignment, which actively aligns
token distortions with rank scores to promote the retention of highly distorted evidence. Across diverse representative plug-and-play compression mechanisms and
datasets, our results show that CAGE consistently achieves lower robust accuracy than the baseline. This work highlights that robustness assessments ignoring compression can be overly optimistic, calling for compression-aware security evaluation and defenses for efficient LVLMs.
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All authors Xinwei Zhang, Hangcheng Liu, Li Bai, Hao Wang, Qingqing Ye, Tianwei Zhang, Haibo Hu
Computer Vision
HERS: Hidden-Pattern Expert Learning for Risk-Specific Vehicle Damage Adaptation in Diffusion Models
Teerapong Panboonyuen
Recent advances in text-to-image (T2I)
diffusion models have enabled increasingly realistic synthesis of vehicle damage, raising concerns about their reliability in automated insurance workflows. The ability to generate crash-like imagery challenges the boundary between authentic and synthetic data, introducing new risks of misuse in fraud or claim manipulation. To address these issues, we propose HERS (Hidden-Pattern Expert Learning for Risk-Specific Damage Adaptation), a framework designed to improve fidelity, controllability, and domain
alignment of
diffusion-generated damage images. HERS fine-tunes a base
diffusion model via domain-specific expert adaptation without requiring manual annotation. Using self-supervised image-text pairs automatically generated by a large
language model and T2I pipeline, HERS models each damage category, such as dents, scratches, broken lights, or cracked paint, as a separate expert. These experts are later integrated into a unified multi-damage model that balances specialization with generalization. We evaluate HERS across four
diffusion backbones and observe consistent improvements: plus 5.5 percent in text faithfulness and plus 2.3 percent in human preference ratings compared to baselines. Beyond image fidelity, we discuss implications for fraud detection, auditability, and safe deployment of generative models in high-stakes domains. Our findings highlight both the opportunities and risks of domain-specific
diffusion, underscoring the importance of trustworthy generation in safety-critical applications such as auto insurance.
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Computer Vision
SimGraph: A Unified Framework for Scene Graph-Based Image Generation and Editing
Thanh-Nhan Vo, Trong-Thuan Nguyen, Tam V. Nguyen et al. (+1 more)
Recent advancements in Generative Artificial Intelligence (GenAI) have significantly enhanced the capabilities of both image generation and editing. However, current approaches often treat these tasks separately, leading to inefficiencies and challenges in maintaining spatial consistency and semantic coherence between generated content and edits. Moreover, a major obstacle is the lack of structured control over object relationships and spatial arrangements. Scene graph-based methods, which represent objects and their interrelationships in a structured format, offer a solution by providing greater control over composition and interactions in both image generation and editing. To address this, we introduce SimGraph, a unified framework that integrates scene graph-based image generation and editing, enabling precise control over object interactions, layouts, and spatial coherence. In particular, our framework integrates
token-based generation and
diffusion-based editing within a single scene graph-driven model, ensuring high-quality and consistent results. Through extensive experiments, we empirically demonstrate that our approach outperforms existing state-of-the-art methods.
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All authors Thanh-Nhan Vo, Trong-Thuan Nguyen, Tam V. Nguyen, Minh-Triet Tran
Computer Vision
Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking
Kaito Shiku, Ichika Seo, Tetsuya Matoba et al. (+3 more)
In this paper, we present the first attempt to estimate the necessity of debulking coronary artery calcifications from computed
tomography (CT) images. We formulate this task as a Multiple-instance Learning (MIL) problem. The difficulty of this task lies in that physicians adjust their focus and decision criteria for device usage according to tabular data representing each patient's condition. To address this issue, we propose a hypernetwork-based adaptive aggregation
transformer (HyperAdAgFormer), which adaptively modifies the feature aggregation strategy for each patient based on tabular data through a hypernetwork. The experiments using the clinical
dataset demonstrated the effectiveness of HyperAdAgFormer. The code is publicly available at https://github.com/Shiku-Kaito/HyperAdAgFormer.
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All authors Kaito Shiku, Ichika Seo, Tetsuya Matoba, Rissei Hino, Yasuhiro Nakano, Ryoma Bise
Computer Vision
Mining Forgery Traces from Reconstruction Error: A Weakly Supervised Framework for Multimodal Deepfake Temporal Localization
Midou Guo, Qilin Yin, Wei Lu et al. (+1 more)
Modern deepfakes have evolved into localized and intermittent manipulations that require fine-grained temporal localization to mitigate severe digital security risks. The prohibitive cost of frame-level annotation makes weakly supervised methods a practical necessity, which rely only on
video-level labels. To this end, we propose Reconstruction-based Temporal Deepfake Localization (RT-DeepLoc), a weakly supervised temporal forgery localization framework that identifies forgeries via reconstruction errors. Our framework uses a Masked Autoencoder (MAE) trained exclusively on authentic data to learn its intrinsic spatiotemporal patterns; this allows the model to produce significant reconstruction discrepancies for forged segments, effectively providing the missing fine-grained cues for accurate localization without demanding dense human annotations. To robustly leverage these indicators, we introduce a novel Asymmetric Intra-
video Contrastive Loss (AICL). By focusing on the compactness of authentic features guided by these reconstruction cues, AICL establishes a stable decision boundary that enhances local discrimination while preserving generalization to unseen forgeries by advanced generative models. Extensive experiments on large-scale
datasets, including LAV-DF, demonstrate that RT-DeepLoc achieves state-of-the-art performance in weakly-supervised temporal forgery localization.
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All authors Midou Guo, Qilin Yin, Wei Lu, Rui Yang
Computer Vision · Cross-listed from cs.RO
4D-CAAL: 4D Radar-Camera Calibration and Auto-Labeling for Autonomous Driving
Shanliang Yao, Zhuoxiao Li, Runwei Guan et al. (+8 more)
4D radar has emerged as a critical sensor for autonomous driving, primarily due to its enhanced capabilities in elevation measurement and higher resolution compared to traditional 3D radar. Effective integration of 4D radar with cameras requires accurate extrinsic calibration, and the development of radar-based perception
algorithms demands large-scale annotated
datasets. However, existing calibration methods often employ separate targets optimized for either visual or radar modalities, complicating correspondence establishment. Furthermore, manually labeling sparse radar data is labor-intensive and unreliable. To address these challenges, we propose 4D-CAAL, a unified framework for 4D radar-camera calibration and auto-labeling. Our approach introduces a novel dual-purpose calibration target design, integrating a checkerboard pattern on the front surface for camera detection and a corner reflector at the center of the back surface for radar detection. We develop a robust correspondence matching
algorithm that aligns the checkerboard center with the strongest radar reflection point, enabling accurate extrinsic calibration. Subsequently, we present an auto-labeling pipeline that leverages the calibrated sensor relationship to transfer annotations from camera-based
segmentations to radar point clouds through
geometric projection and multi-feature optimization. Extensive experiments demonstrate that our method achieves high calibration accuracy while significantly reducing manual annotation effort, thereby accelerating the development of robust multi-modal perception systems for autonomous driving.
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All authors Shanliang Yao, Zhuoxiao Li, Runwei Guan, Kebin Cao, Meng Xia, Fuping Hu, Sen Xu, Yong Yue, Xiaohui Zhu, Weiping Ding, Ryan Wen Liu
Computer Vision
Variance & Greediness: A comparative study of metric-learning losses
Donghuo Zeng, Hao Niu, Zhi Li et al. (+1 more)
Metric learning is central to
retrieval, yet its effects on
embedding geometry and optimization dynamics are not well understood. We introduce a diagnostic framework, VARIANCE (intra-/inter-class variance) and GREEDINESS (active ratio and gradient norms), to compare seven representative losses, i.e., Contrastive, Triplet, N-pair, InfoNCE, ArcFace, SCL, and CCL, across five image-
retrieval datasets. Our analysis reveals that Triplet and SCL preserve higher within-class variance and clearer inter-class margins, leading to stronger top-1
retrieval in fine-grained settings. In contrast, Contrastive and InfoNCE compact
embeddings are achieved quickly through many small updates, accelerating convergence but potentially oversimplifying class structures. N-pair achieves a large mean separation but with uneven spacing. These insights reveal a form of efficiency-granularity trade-off and provide practical guidance: prefer Triplet/SCL when diversity preservation and hard-sample discrimination are critical, and Contrastive/InfoNCE when faster
embedding compaction is desired.
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All authors Donghuo Zeng, Hao Niu, Zhi Li, Masato Taya
Computer Vision
APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention
Yuxiang Huang, Mingye Li, Xu Han et al. (+7 more)
The efficiency of long-
video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large
Multimodal Models (LMMs). Existing methods either compress visual
embeddings or apply sparse
attention on a single GPU, yielding limited acceleration or degraded performance and restricting LMMs from handling longer, more complex
videos. To overcome these issues, we propose APB-V, a sequence-parallel framework with optimized
attention that accelerates long-
video inference across multiple GPUs. By distributing approximate
attention, APB-V reduces computation and increases parallelism, enabling efficient processing of more visual
embeddings without compression and thereby improving task performance. System-level optimizations, such as load balancing and fused forward passes, further unleash the potential of APB-V, delivering speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB, without notable performance loss. Code available at https://github.com/thunlp/APB
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All authors Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu
Computer Vision · Cross-listed from cs.LG
From Consistency to Complementarity: Aligned and Disentangled Multi-modal Learning for Time Series Understanding and Reasoning
Hang Ni, Weijia Zhang, Fei Wang et al. (+2 more)
Advances in multi-modal large
language models (MLLMs) have inspired time series understanding and
reasoning tasks, that enable natural
language querying over time series, producing textual analyses of complex temporal dynamics. Recent attempts hybridize numerical time series with their visualized plots, facilitating precise value
reasoning and visual structure comprehension for comprehensive time series understanding of MLLMs. However, effective numerical-visual modality integration remains challenging due to fine-grained temporal misalignment across modalities and severe
entanglement between shared and modality-specific semantics, which hinder localized interpretation and complementary
reasoning. To address these issues, we propose MADI, a multi-modal LLM enhanced with fine-grained
alignment and disentangled interaction, featuring (1) Patch-level
Alignment, which enforces physically grounded fine-grained correspondence across heterogeneous modalities, (2) Discrete Disentangled Interaction, which separates modality-common semantics into compact discrete latents and adaptively synergizes the purified modality-unique information, and (3) Critical-
token Highlighting, which emphasizes informative, query-relevant signals for robust
reasoning. Experiments on synthetic and real-world
benchmarks show that MADI consistently outperforms general-purpose LLMs and time-series-specialized MLLMs.
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All authors Hang Ni, Weijia Zhang, Fei Wang, Zezhi Shao, Hao Liu
Computer Vision
MultiModal Fine-tuning with Synthetic Captions
Shohei Enomoto and Shin'ya Yamaguchi
In this paper, we address a fundamental gap between pre-training and fine-tuning of deep neural networks: while pre-training has shifted from unimodal to
multimodal learning with enhanced visual understanding, fine-tuning predominantly remains unimodal, limiting the benefits of rich pre-trained representations. To bridge this gap, we propose a novel approach that transforms unimodal
datasets into
multimodal ones using
Multimodal Large
Language Models (MLLMs) to generate synthetic image captions for fine-tuning models with a
multimodal objective. Our method employs carefully designed
prompts incorporating class labels and domain context to produce high-quality captions tailored for classification tasks. Furthermore, we introduce a supervised contrastive loss function that explicitly encourages clustering of same-class representations during fine-tuning, along with a new inference technique that leverages class-averaged text
embeddings from multiple synthetic captions per image. Extensive experiments across 13 image classification
benchmarks demonstrate that our approach outperforms baseline methods, with particularly significant improvements in few-shot learning scenarios. Our work establishes a new paradigm for
dataset enhancement that effectively bridges the gap between
multimodal pre-training and fine-tuning. Our code is available at https://github.com/s-enmt/MMFT.
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Computer Vision · Cross-listed from cs.LG
Lossy Common Information in a Learnable Gray-Wyner Network
Anderson de Andrade, Alon Harell and Ivan V. Bajić
Many computer
vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and inefficient representations. The Gray-Wyner network, a classical concept from information theory, offers a principled framework for separating common and task-specific information. Inspired by this idea, we develop a learnable three-channel codec that disentangles shared information from task-specific details across multiple
vision tasks. We characterize the limits of this approach through the notion of lossy common information, and propose an optimization objective that balances inherent tradeoffs in learning such representations. Through comparisons of three codec architectures on two-task scenarios spanning six
vision benchmarks, we demonstrate that our approach substantially reduces redundancy and consistently outperforms independent coding. These results highlight the practical value of revisiting Gray-Wyner theory in modern machine learning contexts, bridging classic information theory with task-driven representation learning.
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Computer Vision
From Implicit Ambiguity to Explicit Solidity: Diagnosing Interior Geometric Degradation in Neural Radiance Fields for Dense 3D Scene Understanding
Jiangsan Zhao, Jakob Geipel and Kryzysztof Kusnierek
Neural Radiance Fields (NeRFs) have emerged as a powerful paradigm for multi-view reconstruction, complementing classical photogrammetric pipelines based on Structure-from-Motion (SfM) and Multi-View Stereo (MVS). However, their reliability for quantitative 3D analysis in dense, self-occluding scenes remains poorly understood. In this study, we identify a fundamental failure mode of implicit density fields under heavy occlusion, which we term Interior
Geometric Degradation (IGD). We show that transmittance-based volumetric optimization satisfies photometric supervision by reconstructing hollow or fragmented structures rather than solid interiors, leading to systematic instance undercounting. Through controlled experiments on synthetic
datasets with increasing occlusion, we demonstrate that state-of-the-art mask-supervised NeRFs saturate at approximately 89% instance recovery in dense scenes, despite improved surface coherence and mask quality. To overcome this limitation, we introduce an explicit
geometric pipeline based on Sparse Voxel Rasterization (SVRaster), initialized from SfM feature geometry. By projecting 2D instance masks onto an explicit voxel grid and enforcing
geometric separation via recursive splitting, our approach preserves physical solidity and achieves a 95.8% recovery rate in dense clusters. A sensitivity analysis using degraded
segmentation masks further shows that explicit SfM-based geometry is substantially more robust to supervision failure, recovering 43% more instances than implicit baselines. These results demonstrate that explicit
geometric priors are a prerequisite for reliable quantitative analysis in highly self-occluding 3D scenes.
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Computer Vision · Cross-listed from cs.LG
Understanding and Learning Prediction Targets in Diffusion Models
Qing Jin and Chaoyang Wang
Recent advances in
diffusion and flow matching models have highlighted a shift in the preferred prediction target – moving from noise ($\varepsilon$) and velocity (v) to direct data (x) prediction – particularly in high-dimensional settings. However, a formal explanation of why the optimal target depends on the specific properties of the data remains elusive. In this work, we provide a theoretical framework based on a generalized prediction formulation $u=kx-(1-k)n$, where $x$ represents clean data and $n$ denotes noise. This formulation accommodates arbitrary targets, of which $\varepsilon$-, v-, and x-prediction are special cases. We derive the analytical relationship between data's geometry and the optimal prediction target, offering a rigorous justification for why x-prediction becomes superior when the ambient dimension significantly exceeds the data's intrinsic dimension. Furthermore, our controlled analysis reveals how time conditioning and loss variations systematically modulate this optimal objective. While our theory identifies dimensionality as the governing factor for the optimal prediction target, the intrinsic dimension of
manifold-bound data is typically intractable to estimate in practice. To bridge this gap, we propose k-Diff, a framework that employs a data-driven approach to learn the optimal prediction parameter k directly from data, bypassing the need for explicit dimension estimation. Extensive experiments in both latent-space and pixel-space image generation demonstrate that k-Diff consistently outperforms fixed-target baselines across varying architectures and data scales, providing a principled and automated approach to enhancing generative performance.
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Computer Vision
MPF-Net: Exposing High-Fidelity AI-Generated Video Forgeries via Hierarchical Manifold Deviation and Micro-Temporal Fluctuations
Xinan He, Kaiqing Lin, Yue Zhou et al. (+8 more)
With the rapid advancement of
video generation models such as Veo and Wan, the visual quality of synthetic content has reached a level where macro-level semantic errors and temporal inconsistencies are no longer prominent. However, this does not imply that the distinction between real and cutting-edge high-fidelity fake is untraceable. We argue that AI-generated
videos are essentially products of a
manifold-fitting process rather than a physical recording. Consequently, the pixel composition logic of consecutive adjacent frames residual in AI
videos exhibits a structured and homogenous characteristic. We term this phenomenon `
Manifold Projection Fluctuations' (MPF). Driven by this insight, we propose a hierarchical dual-path framework that operates as a sequential filtering process. The first, the Static
Manifold Deviation Branch, leverages the refined perceptual boundaries of Large-Scale
Vision Foundation Models (VFMs) to capture residual spatial anomalies or physical violations that deviate from the natural real-world
manifold (off-
manifold). For the remaining high-fidelity
videos that successfully reside on-
manifold and evade spatial detection, we introduce the Micro-Temporal Fluctuation Branch as a secondary, fine-grained filter. By analyzing the structured MPF that persists even in visually perfect sequences, our framework ensures that forgeries are exposed regardless of whether they manifest as global real-world
manifold deviations or subtle computational fingerprints.
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All authors Xinan He, Kaiqing Lin, Yue Zhou, Jiaming Zhong, Wei Ye, Wenhui Yi, Bing Fan, Feng Ding, Haodong Li, Bo Cao, Bin Li
Computer Vision
Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation
Zihan Su, Hongyang Wei, Kangrui Cen et al. (+4 more)
Unified
Multimodal Models (UMMs) integrate both visual understanding and generation within a single framework. Their ultimate aspiration is to create a cycle where understanding and generation mutually reinforce each other. While recent post-training methods have successfully leveraged understanding to enhance generation, the reverse direction of utilizing generation to improve understanding remains largely unexplored. In this work, we propose UniMRG (Unified Multi-Representation Generation), a simple yet effective architecture-agnostic post-training method. UniMRG enhances the understanding capabilities of UMMs by incorporating auxiliary generation tasks. Specifically, we train UMMs to generate multiple intrinsic representations of input images, namely pixel (reconstruction), depth (geometry), and
segmentation (structure), alongside standard visual understanding objectives. By synthesizing these diverse representations, UMMs capture complementary information regarding appearance, spatial relations, and structural layout. Consequently, UMMs develop a deeper and more comprehensive understanding of visual inputs. Extensive experiments across diverse UMM architectures demonstrate that our method notably enhances fine-grained perception, reduces
hallucinations, and improves spatial understanding, while simultaneously boosting generation capabilities.
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All authors Zihan Su, Hongyang Wei, Kangrui Cen, Yong Wang, Guanhua Chen, Chun Yuan, Xiangxiang Chu
Computer Vision
Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification
Kailash A. Hambarde and Hugo Proença
Aerial-ground person re-identification (AG-ReID) is fundamentally challenged by extreme viewpoint and distance discrepancies between aerial and ground cameras, which induce severe
geometric distortions and invalidate the assumption of a shared similarity space across views. Existing methods primarily rely on geometry-aware feature learning or appearance-conditioned prompting, while implicitly assuming that the geometry-invariant dot-product similarity used in
attention mechanisms remains reliable under large viewpoint and scale variations. We argue that this assumption does not hold. Extreme camera geometry systematically distorts the query-key similarity space and degrades
attention-based matching, even when feature representations are partially aligned. To address this issue, we introduce Geometry-Induced Query-Key Transformation (GIQT), a lightweight low-rank module that explicitly rectifies the similarity space by conditioning query-key interactions on camera geometry. Rather than modifying feature representations or the
attention formulation itself, GIQT adapts the similarity computation to compensate for dominant geometry-induced anisotropic distortions. Building on this local similarity rectification, we further incorporate a geometry-conditioned
prompt generation mechanism that provides global, view-adaptive representation priors derived directly from camera geometry.Experiments on four aerial-ground person re-identification
benchmarks demonstrate that the proposed framework consistently improves robustness under extreme and previously unseen
geometric conditions, while introducing minimal computational overhead compared to state-of-the-art methods.
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Computer Vision
Towards Geometry-Aware and Motion-Guided Video Human Mesh Recovery
Hongjun Chen, Huan Zheng, Wencheng Han et al. (+1 more)
Existing
video-based 3D Human Mesh Recovery (HMR) methods often produce physically implausible results, stemming from their reliance on flawed intermediate 3D pose anchors and their inability to effectively model complex spatiotemporal dynamics. To overcome these deep-rooted architectural problems, we introduce HMRMamba, a new paradigm for HMR that pioneers the use of Structured State Space Models (SSMs) for their efficiency and long-range modeling prowess. Our framework is distinguished by two core contributions. First, the Geometry-Aware Lifting Module, featuring a novel dual-scan Mamba architecture, creates a robust foundation for reconstruction. It directly grounds the 2D-to-3D pose lifting process with
geometric cues from image features, producing a highly reliable 3D pose sequence that serves as a stable anchor. Second, the Motion-guided Reconstruction Network leverages this anchor to explicitly process kinematic patterns over time. By injecting this crucial temporal awareness, it significantly enhances the final mesh's coherence and robustness, particularly under occlusion and motion blur. Comprehensive evaluations on 3DPW, MPI-INF-3DHP, and Human3.6M
benchmarks confirm that HMRMamba sets a new state-of-the-art, outperforming existing methods in both reconstruction accuracy and temporal consistency while offering superior computational efficiency.
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All authors Hongjun Chen, Huan Zheng, Wencheng Han, Jianbing Shen
Computer Vision · Cross-listed from cs.NI
ViTMAlis: Towards Latency-Critical Mobile Video Analytics with Vision Transformers
Miao Zhang, Guanzhen Wu, Hao Fang et al. (+4 more)
Edge-assisted mobile
video analytics (MVA) applications are increasingly shifting from using
vision models based on convolutional neural networks (CNNs) to those built on
vision transformers (ViTs) to leverage their superior global context modeling and generalization capabilities. However, deploying these advanced models in latency-critical MVA scenarios presents significant challenges. Unlike traditional CNN-based offloading paradigms where network transmission is the primary bottleneck, ViT-based systems are constrained by substantial inference delays, particularly for dense prediction tasks where the need for high-resolution inputs exacerbates the inherent quadratic computational complexity of ViTs. To address these challenges, we propose a dynamic mixed-resolution inference strategy tailored for ViT-backboned dense prediction models, enabling flexible runtime trade-offs between speed and accuracy. Building on this, we introduce ViTMAlis, a ViT-native device-to-edge offloading framework that dynamically adapts to network conditions and
video content to jointly reduce transmission and inference delays. We implement a fully functional prototype of ViTMAlis on commodity mobile and edge devices. Extensive experiments demonstrate that, compared to state-of-the-art accuracy-centric, content-aware, and latency-adaptive baselines, ViTMAlis significantly reduces end-to-end offloading latency while improving user-perceived rendering accuracy, providing a practical foundation for next-generation mobile intelligence.
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All authors Miao Zhang, Guanzhen Wu, Hao Fang, Yifei Zhu, Fangxin Wang, Ruixiao Zhang, Jiangchuan Liu
Computer Vision
Semantic-Guided Dynamic Sparsification for Pre-Trained Model-based Class-Incremental Learning
Ruiqi Liu, Boyu Diao, Zijia An et al. (+3 more)
Class-Incremental Learning (CIL) requires a model to continually learn new classes without forgetting old ones. A common and efficient solution freezes a pre-trained model and employs lightweight adapters, whose parameters are often forced to be orthogonal to prevent inter-task interference. However, we argue that this parameter-constraining method is detrimental to plasticity. To this end, we propose Semantic-Guided Dynamic Sparsification (SGDS), a novel method that proactively guides the activation space by governing the orientation and rank of its subspaces through targeted sparsification. Specifically, SGDS promotes knowledge transfer by encouraging similar classes to share a compact activation subspace, while simultaneously preventing interference by assigning non-overlapping activation subspaces to dissimilar classes. By sculpting class-specific sparse subspaces in the activation space, SGDS effectively mitigates interference without imposing rigid constraints on the parameter space. Extensive experiments on various
benchmark datasets demonstrate the state-of-the-art performance of SGDS.
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All authors Ruiqi Liu, Boyu Diao, Zijia An, Runjie Shao, Feixiang Ge, Yongjun Xu
Computer Vision
Dynamical Adapter Fusion: Constructing A Global Adapter for Pre-Trained Model-based Class-Incremental Learning
Ruiqi Liu, Boyu Diao, Zijia An et al. (+3 more)
Class-Incremental Learning (CIL) requires models to continuously acquire new classes without forgetting previously learned ones. A dominant paradigm involves freezing a pre-trained model and training lightweight, task-specific adapters. However, maintaining task-specific parameters hinders knowledge transfer and incurs high
retrieval costs, while naive parameter fusion often leads to destructive interference and catastrophic forgetting. To address these challenges, we propose Dynamical Adapter Fusion (DAF) to construct a single robust global adapter. Grounded in the PAC-Bayes
theorem, we derive a fusion mechanism that explicitly integrates three components: the optimized task-specific adapter parameters, the previous global adapter parameters, and the initialization parameters. We utilize the Taylor expansion of the loss function to derive the optimal fusion coefficients, dynamically achieving the best balance between stability and plasticity. Furthermore, we propose a Robust Initialization strategy to effectively capture global knowledge patterns. Experiments on multiple CIL
benchmarks demonstrate that DAF achieves state-of-the-art (SOTA) performance.
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All authors Ruiqi Liu, Boyu Diao, Zijia An, Zhulin An, Fei Wang, Yongjun Xu
Computer Vision
Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution
Ji-Xuan He, Guohang Zhuang, Bo Junge et al. (+5 more)
Hyperspectral image super-resolution (HSI-SR) aims to recover spatial detail while preserving the spectral shape on which quantitative analysis relies. Recent HSI-SR methods, from repurposed RGB super-resolution backbones to dedicated spectral-spatial architectures, have greatly improved spatial reconstruction. However, overlooking the compact spectral structure of hyperspectral data leaves residual spectral errors, while binding the spectral treatment to each architecture forces it to be rebuilt for every new backbone. Yet the low-dimensional structure of spectra belongs to the data, not to any backbone. Backbones differ in the errors they leave, but not in the structure of the true spectra. One rectifier design can therefore serve any backbone. Building on this insight, we propose the Spectral Rectification Super-Resolution Network (\textbf{SR$^{2}$-Net}), a model-agnostic rectifier that needs nothing from the backbone but its output, leaves its internal architecture untouched, and is trained per backbone. SR$^{2}$-Net follows an enhance-then-rectify pipeline in which Hierarchical Spectral-Spatial Synergy
Attention (\textbf{H-S$^{3}$A}) reinforces cross-band interactions, while Mode-Constrained Rectification (MCR) confines the correction to a learned compact spectral subspace. A degradation-consistency constraint further ties the output to the observed low-resolution input. Experiments with five backbones spanning CNN,
Transformer, and
diffusion families show that one fixed configuration improves spectral fidelity in every reported setting while preserving or improving spatial quality. Averaged over thirty in-domain settings, SR$^{2}$-Net removes 22.6% of the residual spectral error at a backbone-independent cost of 0.048M parameters.
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All authors Ji-Xuan He, Guohang Zhuang, Bo Junge, Chen Ling, Tingyi Li, Yanan Qiao, Miaomiao Cai, Jungfeng Fang
Computer Vision
Do Pathology Foundation Models Encode Disease Progression? A Pseudotime Analysis of Visual Representations
Pritika Vig, Ren-Chin Wu and William Lotter
Vision foundation models trained on discretely sampled images achieve strong performance on classification
benchmarks, yet whether their representations encode the continuous processes underlying their training data remains unclear. This question is especially pertinent in computational pathology, where we posit that models whose latent representations implicitly capture ordinal disease progression may better reflect underlying biology, support more robust generalization, and enable quantitative analyses of features associated with disease transitions. Using
diffusion pseudotime, a method developed to infer developmental trajectories from single-
cell transcriptomics, we probe whether foundation models organize disease states along coherent progression directions in representation space. Across four cancer progressions and six models, we find that all pathology-specific models recover trajectory orderings significantly exceeding null baselines, with pathology
vision-only models achieving the highest average fidelities. Model rankings by trajectory fidelity on reference diseases predict few-shot classification performance on held-out diseases ($\rho = 0.91$), and exploratory analysis shows
cell-type composition varies along inferred trajectories in patterns consistent with known stromal remodeling. Together, these results suggest that
vision foundation models can implicitly learn to represent continuous processes from independent static observations, and that trajectory fidelity provides a complementary measure of representation quality beyond downstream performance. While demonstrated in pathology, this framework could be applied to other domains where continuous processes are observed through static snapshots.
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Computer Vision · Cross-listed from cs.LG
Adversarial Vulnerability Transcends Computational Paradigms: Feature Engineering Provides No Defense Against Neural Adversarial Transfer
Achraf Hsain, Ahmed Abdelkader, Emmanuel Baldwin Mbaya et al. (+1 more)
Deep neural networks are vulnerable to
adversarial examples–inputs with imperceptible perturbations causing misclassification. While
adversarial transfer within neural networks is well-documented, whether classical ML pipelines using handcrafted features inherit this vulnerability when attacked via neural surrogates remains unexplored. Feature engineering creates information bottlenecks through gradient quantization and spatial binning, potentially filtering high-frequency
adversarial signals. We evaluate this hypothesis through the first comprehensive study of
adversarial transfer from DNNs to HOG-based classifiers. Using VGG16 as a surrogate, we generate FGSM and PGD
adversarial examples and test transfer to four classical classifiers (KNN, Decision Tree, Linear SVM, Kernel SVM) and a shallow neural network across eight HOG configurations on CIFAR-10. Our results strongly refute the protective hypothesis: all classifiers suffer 16.6%-59.1% relative accuracy drops, comparable to neural-to-neural transfer. More surprisingly, we discover attack hierarchy reversal–contrary to patterns where iterative PGD dominates FGSM within neural networks, FGSM causes greater degradation than PGD in 100% of classical ML cases, suggesting iterative attacks overfit to surrogate-specific features that don't survive feature extraction. Block normalization provides partial but insufficient mitigation. These findings demonstrate that
adversarial vulnerability is not an artifact of end-to-end differentiability but a fundamental property of image classification systems, with implications for security-critical deployments across computational paradigms.
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All authors Achraf Hsain, Ahmed Abdelkader, Emmanuel Baldwin Mbaya, Hamoud Aljamaan
Computer Vision
Optimal Transport-Induced Samples against Out-of-Distribution Overconfidence
Keke Tang, Ziyong Du, Xiaofei Wang et al. (+3 more)
Deep neural networks (DNNs) often produce overconfident predictions on out-of-distribution (OOD) inputs, undermining their reliability in open-world environments. Singularities in semi-discrete optimal transport (OT) mark regions of semantic ambiguity, where classifiers are particularly prone to unwarranted high-confidence predictions. Motivated by this observation, we propose a principled framework to mitigate OOD overconfidence by leveraging the geometry of OT-induced singular boundaries. Specifically, we formulate an OT problem between a continuous base distribution and the latent
embeddings of training data, and identify the resulting singular boundaries. By sampling near these boundaries, we construct a class of OOD inputs, termed optimal transport-induced OOD samples (OTIS), which are geometrically grounded and inherently semantically ambiguous. During training, a confidence suppression loss is applied to OTIS to guide the model toward more calibrated predictions in structurally uncertain regions. Extensive experiments show that our method significantly alleviates OOD overconfidence and outperforms state-of-the-art methods.
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All authors Keke Tang, Ziyong Du, Xiaofei Wang, Weilong Peng, Peican Zhu, Zhihong Tian
Computer Vision
HiFi-Mesh: High-Fidelity Efficient 3D Mesh Generation via Compact Autoregressive Dependence
Yanfeng Li, Tao Tan, Qingquan Gao et al. (+3 more)
High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequences, which severely constrains the expressible structural details. We introduce the Latent Autoregressive Network (LANE), which incorporates compact autoregressive dependencies in the generation process, achieving a $6\times$ improvement in maximum generatable sequence length compared to existing methods. To further accelerate inference, we propose the Adaptive Computation Graph Reconfiguration (AdaGraph) strategy, which effectively overcomes the efficiency bottleneck of traditional serial inference through spatiotemporal decoupling in the generation process. Experimental validation demonstrates that LANE achieves superior performance across generation speed, structural detail, and
geometric consistency, providing an effective solution for high-quality 3D mesh generation.
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All authors Yanfeng Li, Tao Tan, Qingquan Gao, Zhiwen Cao, Xiaohong liu, Yue Sun
Computer Vision
Mam-App: A Novel Parameter-Efficient Mamba Model for Apple Leaf Disease Classification
Md Nadim Mahamood, Md Imran Hasan, Md Rasheduzzaman et al. (+3 more)
The rapid growth of the global population, alongside exponential technological advancement, has intensified the demand for food production. Meeting this demand depends not only on increasing agricultural yield but also on minimizing food loss caused by crop diseases. Diseases account for a substantial portion of apple production losses, despite apples being among the most widely produced and nutritionally valuable fruits worldwide. Previous studies have employed machine learning techniques for feature extraction and early diagnosis of apple leaf diseases, and more recently, deep learning-based models have shown remarkable performance in disease recognition. However, most state-of-the-art deep learning models are highly parameter-intensive, resulting in increased training and inference time. Although lightweight models are more suitable for user-friendly and resource-constrained applications, they often suffer from performance degradation. To address the trade-off between efficiency and performance, we propose Mam-App, a parameter-efficient Mamba-based model for feature extraction and leaf disease classification. The proposed approach achieves competitive state-of-the-art performance on the PlantVillage Apple Leaf Disease
dataset, attaining 99.58% accuracy, 99.30% precision, 99.14% recall, and a 99.22% F1-score, while using only 0.051M parameters. This extremely low parameter count makes the model suitable for deployment on drones, mobile devices, and other low-resource platforms. To demonstrate the robustness and generalizability of the proposed model, we further evaluate it on the PlantVillage Corn Leaf Disease and Potato Leaf Disease
datasets. The model achieves 99.48%, 99.20%, 99.34%, and 99.27% accuracy, precision, recall, and F1-score on the corn
dataset and 98.46%, 98.91%, 95.39%, and 97.01% on the potato
dataset, respectively.
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All authors Md Nadim Mahamood, Md Imran Hasan, Md Rasheduzzaman, Ausrukona Ray, Md Shafi Ud Doula, Kamrul Hasan