Computer Vision
BARE: Towards Bias-Aware and Reasoning-Enhanced One-Tower Visual Grounding
Hongbing Li, Linhui Xiao, Zihan Zhao et al. (+4 more)
Visual Grounding (VG), which aims to locate a specific region referred to by expressions, is a fundamental yet challenging task in the
multimodal understanding fields. While recent grounding transfer works have advanced the field through one-tower architectures, they still suffer from two primary limitations: (1) over-entangled
multimodal representations that exacerbate deceptive modality biases, and (2) insufficient semantic
reasoning that hinders the comprehension of referential cues. In this paper, we propose BARE, a bias-aware and
reasoning-enhanced framework for one-tower visual grounding. BARE introduces a mechanism that preserves modality-specific features and constructs referential semantics through three novel modules: (i)
language salience modulator, (ii) visual bias correction and (iii) referential relationship enhancement, which jointly mitigate
multimodal distractions and enhance referential comprehension. Extensive experimental results on five
benchmarks demonstrate that BARE not only achieves state-of-the-art performance but also delivers superior computational efficiency compared to existing approaches. The code is publicly accessible at https://github.com/Marloweeee/BARE.
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All authors Hongbing Li, Linhui Xiao, Zihan Zhao, Qi Shen, Yixiang Huang, Bo Xiao, Zhanyu Ma
Computer Vision
FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation
Gen Li and Peiyu Liu
Vision-
Language Models (VLMs) excel at visual
reasoning but still struggle with integrating external knowledge.
Retrieval-Augmented Generation (RAG) is a promising solution, but current methods remain inefficient and often fail to maintain high answer quality. To address these challenges, we propose VideoSpeculateRAG, an efficient VLM-based RAG framework built on two key ideas. First, we introduce a speculative decoding pipeline: a lightweight draft model quickly generates multiple answer candidates, which are then verified and refined by a more accurate heavyweight model, substantially reducing inference latency without sacrificing correctness. Second, we identify a major source of error - incorrect entity recognition in retrieved knowledge - and mitigate it with a simple yet effective similarity-based filtering strategy that improves entity
alignment and boosts overall answer accuracy. Experiments demonstrate that VideoSpeculateRAG achieves comparable or higher accuracy than standard RAG approaches while accelerating inference by approximately 2x. Our framework highlights the potential of combining speculative decoding with
retrieval-augmented
reasoning to enhance efficiency and reliability in complex, knowledge-intensive
multimodal tasks.
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Computer Vision
A Novel Deep Learning Method for Segmenting the Left Ventricle in Cardiac Cine MRI
Wenhui Chu, Aobo Jin and Hardik A. Gohel
This research aims to develop a novel deep learning network, GBU-Net, utilizing a group-batch-normalized U-Net framework, specifically designed for the precise semantic
segmentation of the left ventricle in short-axis cine MRI scans. The methodology includes a down-sampling pathway for feature extraction and an up-sampling pathway for detail restoration, enhanced for medical imaging. Key modifications include techniques for better contextual understanding crucial in cardiac MRI
segmentation. The
dataset consists of 805 left ventricular MRI scans from 45 patients, with comparative analysis using established metrics such as the dice coefficient and mean perpendicular distance. GBU-Net significantly improves the accuracy of left ventricle
segmentation in cine MRI scans. Its innovative design outperforms existing methods in tests, surpassing standard metrics like the dice coefficient and mean perpendicular distance. The approach is unique in its ability to capture contextual information, often missed in traditional CNN-based
segmentation. An ensemble of the GBU-Net attains a 97% dice score on the SunnyBrook testing
dataset. GBU-Net offers enhanced precision and contextual understanding in left ventricle
segmentation for
surgical robotics and medical analysis.
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Computer Vision
DiffKD-DCIS: Predicting Upgrade of Ductal Carcinoma In Situ with Diffusion Augmentation and Knowledge Distillation
Tao Li, Qing Li, Na Li et al. (+1 more)
Accurately predicting the upgrade of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) is crucial for
surgical planning. However, traditional deep learning methods face challenges due to limited ultrasound data and poor generalization ability. This study proposes the DiffKD-DCIS framework, integrating conditional
diffusion modeling with teacher-student knowledge
distillation. The framework operates in three stages: First, a conditional
diffusion model generates high-fidelity ultrasound images using
multimodal conditions for data augmentation. Then, a deep teacher network extracts robust features from both original and synthetic data. Finally, a compact student network learns from the teacher via knowledge
distillation, balancing generalization and computational efficiency. Evaluated on a multi-center
dataset of 1,435 cases, the synthetic images were of good quality. The student network had fewer parameters and faster inference. On external test sets, it outperformed partial combinations, and its accuracy was comparable to senior radiologists and superior to junior ones, showing significant clinical potential.
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All authors Tao Li, Qing Li, Na Li, Hui Xie
Computer Vision
DeepInv: A Novel Self-supervised Learning Approach for Fast and Accurate Diffusion Inversion
Ziyue Zhang, Luxi Lin, Xiaolin Hu et al. (+4 more)
Diffusion inversion is a task of recovering the noise of an image in a
diffusion model, which is vital for controllable
diffusion image editing. At present,
diffusion inversion still remains a challenging task due to the lack of viable supervision signals. Thus, most existing methods resort to approximation-based solutions, which however are often at the cost of performance or efficiency. To remedy these shortcomings, we propose a novel self-supervised
diffusion inversion approach in this paper, termed Deep Inversion (DeepInv). Instead of requiring ground-truth noise annotations, we introduce a self-supervised objective as well as a data augmentation strategy to generate high-quality pseudo noises from real images without manual intervention. Based on these two innovative designs, DeepInv is also equipped with an iterative and multi-scale training regime to train a parameterized inversion solver, thereby achieving the fast and accurate image-to-noise mapping. To the best of our knowledge, this is the first attempt of presenting a trainable solver to predict inversion noise step by step. The extensive experiments show that our DeepInv can achieve much better performance and inference speed than the compared methods, e.g., +40.435% SSIM than EasyInv and +9887.5% speed than ReNoise on COCO
dataset. Moreover, our careful designs of trainable solvers can also provide insights to the community. Codes and model parameters will be released in https://github.com/potato-kitty/DeepInv.
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All authors Ziyue Zhang, Luxi Lin, Xiaolin Hu, Chao Chang, HuaiXi Wang, Yiyi Zhou, Rongrong Ji
Computer Vision
Higher-Order Domain Generalization in Magnetic Resonance-Based Assessment of Alzheimer's Disease
Zobia Batool, Diala Lteif, Vijaya B. Kolachalama et al. (+2 more)
Despite progress in deep learning for Alzheimer's disease (AD) diagnostics, models trained on structural
magnetic resonance imaging (sMRI) often do not perform well when applied to new cohorts due to domain shifts from varying scanners, protocols and patient demographics. AD, the primary driver of dementia, manifests through progressive cognitive and neuroanatomical changes like atrophy and ventricular expansion, making robust, generalizable classification essential for real-world use. While convolutional neural networks and
transformers have advanced feature extraction via
attention and fusion techniques, single-domain generalization (SDG) remains underexplored yet critical, given the fragmented nature of AD
datasets. To bridge this gap, we introduce Extended MixStyle (EM), a framework for blending higher-order feature moments (skewness and kurtosis) to mimic diverse distributional variations. Trained on sMRI data from the National Alzheimer's Coordinating Center (NACC; n=4,647) to differentiate persons with normal cognition (NC) from those with mild cognitive impairment (MCI) or AD and tested on three unseen cohorts (total n=3,126), EM yields enhanced cross-domain performance, improving macro-F1 on average by 2.4 percentage points over state-of-the-art SDG
benchmarks, underscoring its promise for invariant, reliable AD detection in heterogeneous real-world settings. The source code will be made available upon acceptance at https://github.com/zobia111/Extended-Mixstyle.
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All authors Zobia Batool, Diala Lteif, Vijaya B. Kolachalama, Huseyin Ozkan, Erchan Aptoula
Computer Vision
Unified Generation and Self-Verification for Vision-Language Models via Advantage Decoupled Preference Optimization
Xinyu Qiu, Heng Jia, Zhengwen Zeng et al. (+4 more)
Parallel test-time scaling typically trains separate generation and verification models, incurring high training and inference costs. We propose Advantage Decoupled Preference Optimization (ADPO), a unified reinforcement learning framework that jointly learns answer generation and self-verification within a single policy. ADPO introduces two innovations: a preference verification reward improving verification capability and a decoupled optimization mechanism enabling synergistic optimization of generation and verification. Specifically, the preference verification reward computes mean verification scores from positive and negative samples as decision thresholds, providing positive feedback when prediction correctness aligns with answer correctness. Meanwhile, the advantage decoupled optimization computes separate advantages for generation and verification, applies
token masks to isolate gradients, and combines masked GRPO objectives, preserving generation quality while calibrating verification scores. ADPO achieves up to +34.1% higher verification AUC and -53.5% lower inference time, with significant gains of +2.8%/+1.4% accuracy on MathVista/MMMU, +1.9 cIoU on ReasonSeg, and +1.7%/+1.0% step success rate on AndroidControl/GUI Odyssey.
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All authors Xinyu Qiu, Heng Jia, Zhengwen Zeng, Shuheng Shen, Changhua Meng, Yi Yang, Linchao Zhu
Computer Vision
Robust Ship Detection and Tracking Using Modified ViBe and Backwash Cancellation Algorithm
Mohammad Hassan Saghafi, Seyed Majid Noorhosseini, Seyed Abolfazl Seyed Javadein et al. (+1 more)
In this paper, we propose a robust real time detection and tracking method for detecting ships in a coastal
video sequences. Since coastal scenarios are unpredictable and scenes have dynamic properties it is essential to apply detection methods that are robust to these conditions. This paper presents modified ViBe for moving object detection which detects ships and backwash. In the modified ViBe the probability of losing ships is decreased in comparison with the original ViBe. It is robust to natural sea waves and variation of lights and is capable of quickly updating the background. Based on geometrical properties of ship and some concepts such as brightness distortion, a new method for backwash cancellation is proposed. Experimental results demonstrate that the proposed strategy and methods have outstanding performance in ship detection and tracking. These results also illustrate real time and precise performance of the proposed strategy.
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All authors Mohammad Hassan Saghafi, Seyed Majid Noorhosseini, Seyed Abolfazl Seyed Javadein, Hadi Khalili
Computer Vision
Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network
Mohd Usama, Belal Ahmad, Christer Gronlund et al. (+1 more)
Deep learning has been extensively used in medical imaging applications, assuming that the test and training
datasets belong to the same probability distribution. However, a common challenge arises when working with medical images generated by different systems or even the same system with different parameter settings. Such images contain diverse textures and reverberation noise that violate the aforementioned assumption. Consequently, models trained on data from one device or setting often struggle to perform effectively with data from other devices or settings. In addition, retraining models for each specific device or setting is labor-intensive and costly. To address these issues in ultrasound images, we propose a novel Generative
Adversarial Network (GAN)-based model. We formulated the domain adaptation tasks as an image-to-image translation task, in which we modified the texture patterns and removed reverberation noise in the test data images from the source domain to align with those in the target domain images while keeping the image content unchanged. We applied the proposed method to two
datasets containing carotid ultrasound images from three different domains. The experimental results demonstrate that the model successfully translated the texture pattern of images and removed reverberation noise from the ultrasound images. Furthermore, we evaluated the CycleGAN approaches for a comparative study with the proposed model. The experimental findings conclusively demonstrated that the proposed model achieved domain adaptation (histogram correlation (0.960 (0.019), & 0.920 (0.043) and bhattacharya distance (0.040 (0.020), & 0.085 (0.048)), compared to no adaptation (0.916 (0.062) & 0.890 (0.077), 0.090 (0.070) & 0.121 (0.095)) for both
datasets.
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All authors Mohd Usama, Belal Ahmad, Christer Gronlund, Faleh Menawer R Althiyabi
Computer Vision
Language as Prior, Vision as Calibration: Metric Scale Recovery for Monocular Depth Estimation
Mingxia Zhan, Li Zhang, Beibei Wang et al. (+2 more)
Relative-depth foundation models transfer well, yet
monocular metric depth remains ill-posed due to unidentifiable global scale and heightened domain-shift sensitivity. Under a frozen-backbone calibration setting, we recover metric depth via an image-specific affine transform in inverse depth and train only lightweight calibration heads while keeping the relative-depth backbone and the CLIP text encoder fixed. Since captions provide coarse but noisy scale cues that vary with phrasing and missing objects, we use
language to predict an uncertainty-aware envelope that bounds feasible calibration parameters in an unconstrained space, rather than committing to a text-only point estimate. We then use pooled multi-scale frozen visual features to select an image-specific calibration within this envelope. During training, a closed-form least-squares oracle in inverse depth provides per-image supervision for learning the envelope and the selected calibration. Experiments on NYUv2 and KITTI improve in-domain accuracy, while zero-shot transfer to SUN-RGBD and DDAD demonstrates improved robustness over strong
language-only baselines.
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All authors Mingxia Zhan, Li Zhang, Beibei Wang, Yingjie Wang, Zenglin Shi
Computer Vision
Rethinking Multimodal Few-Shot 3D Point Cloud Segmentation: From Fused Refinement to Decoupled Arbitration
Wentao Bian and Fenglei Xu
In this paper, we revisit
multimodal few-shot 3D point cloud semantic
segmentation (FS-PCS), identifying a conflict in "Fuse-then-Refine" paradigms: the "Plasticity-Stability Dilemma." In addition, CLIP's inter-class confusion can result in semantic blindness. To address these issues, we present the Decoupled-experts Arbitration Few-Shot SegNet (DA-FSS), a model that effectively distinguishes between semantic and
geometric paths and mutually regularizes their gradients to achieve better generalization. DA-FSS employs the same backbone and pre-trained text encoder as MM-FSS to generate text
embeddings, which can increase free modalities' utilization rate and better leverage each modality's information space. To achieve this, we propose a Parallel Expert Refinement module to generate each modal correlation. We also propose a Stacked Arbitration Module (SAM) to perform convolutional fusion and arbitrate correlations for each modality pathway. The Parallel Experts decouple two paths: a
Geometric Expert maintains plasticity, and a Semantic Expert ensures stability. They are coordinated via a Decoupled
Alignment Module (DAM) that transfers knowledge without propagating confusion. Experiments on popular
datasets (S3DIS, ScanNet) demonstrate the superiority of DA-FSS over MM-FSS. Meanwhile,
geometric boundaries, completeness, and texture differentiation are all superior to the baseline. The code is available at: https://github.com/MoWenQAQ/DA-FSS/.
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Computer Vision
PartImageNet++ Dataset: Enhancing Visual Models with High-Quality Part Annotations
Xiao Li, Zilong Liu, Yining Liu et al. (+4 more)
To address the scarcity of high-quality part annotations in existing
datasets, we introduce PartImageNet++ (PIN++), a
dataset that provides detailed part annotations for all categories in ImageNet-1K. With 100 annotated images per category, totaling 100K images, PIN++ represents the most comprehensive
dataset covering a diverse range of object categories. Leveraging PIN++, we propose a Multi-scale Part-supervised recognition Model (MPM) for robust classification on ImageNet-1K. We first trained a part
segmentation network using PIN++ and used it to generate pseudo part labels for the remaining unannotated images. MPM then integrated a conventional recognition architecture with auxiliary bypass layers, jointly supervised by both pseudo part labels and the original part annotations. Furthermore, we conducted extensive experiments on PIN++, including part
segmentation, object
segmentation, and few-shot learning, exploring various ways to leverage part annotations in downstream tasks. Experimental results demonstrated that our approach not only enhanced part-based models for robust object recognition but also established strong baselines for multiple downstream tasks, highlighting the potential of part annotations in improving model performance. The
dataset and the code are available at https://github.com/LixiaoTHU/PartImageNetPP.
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All authors Xiao Li, Zilong Liu, Yining Liu, Zhuhong Li, Na Dong, Sitian Qin, Xiaolin Hu
Computer Vision · Cross-listed from physics.app-ph
Image Synthesis Using Spintronic Deep Convolutional Generative Adversarial Network
Saumya Gupta, Abhinandan, Venkatesh vadde et al. (+2 more)
The computational requirements of generative
adversarial networks (GANs) exceed the limit of conventional Von Neumann architectures, necessitating energy efficient alternatives such as neuromorphic spintronics. This work presents a hybrid CMOS-spintronic deep convolutional generative
adversarial network (DCGAN) architecture for synthetic image generation. The proposed generative
vision model approach follows the standard framework, leveraging generator and discriminators
adversarial training with our designed spintronics hardware for deconvolution, convolution, and activation layers of the DCGAN architecture. To enable hardware aware spintronic implementation, the generator's deconvolution layers are restructured as zero padded convolution, allowing seamless integration with a 6-bit skyrmion based synapse in a crossbar, without compromising training performance. Nonlinear activation functions are implemented using a hybrid CMOS domain wall based Rectified linear unit (ReLU) and Leaky ReLU units. Our proposed tunable Leaky ReLU employs domain wall position coded, continuous resistance states and a piecewise uniaxial
parabolic anisotropy profile with a parallel MTJ readout, exhibiting energy consumption of 0.192 pJ. Our spintronic DCGAN model demonstrates adaptability across both grayscale and colored
datasets, achieving Fr'echet Inception Distances (FID) of 27.5 for the Fashion MNIST and 45.4 for Anime Face
datasets, with testing energy (training energy) of 4.9 nJ (14.97~nJ/image) and 24.72 nJ (74.7 nJ/image).
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All authors Saumya Gupta, Abhinandan, Venkatesh vadde, Bhaskaran Muralidharan, Abhishek Sharma
Computer Vision
In defense of the two-stage framework for open-set domain adaptive semantic segmentation
Wenqi Ren, Weijie Wang, Meng Zheng et al. (+4 more)
Open-Set Domain Adaptation for Semantic
Segmentation (OSDA-SS) presents a significant challenge, as it requires both domain adaptation for known classes and the distinction of unknowns. Existing methods attempt to address both tasks within a single unified stage. We question this design, as the annotation imbalance between known and unknown classes often leads to negative transfer of known classes and underfitting for unknowns. To overcome these issues, we propose SATS, a Separating-then-Adapting Training Strategy, which addresses OSDA-SS through two sequential steps: known/unknown separation and unknown-aware domain adaptation. By providing the model with more accurate and well-aligned unknown classes, our method ensures a balanced learning of discriminative features for both known and unknown classes, steering the model toward discovering truly unknown objects. Additionally, we present hard unknown exploration, an innovative data augmentation method that exposes the model to more challenging unknowns, strengthening its ability to capture more comprehensive understanding of target unknowns. We evaluate our method on public OSDA-SS
benchmarks. Experimental results demonstrate that our method achieves a substantial advancement, with a +3.85% H-Score improvement for GTA5-to-Cityscapes and +18.64% for SYNTHIA-to-Cityscapes, outperforming previous state-of-the-art methods.
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All authors Wenqi Ren, Weijie Wang, Meng Zheng, Ziyan Wu, Yang Tang, Zhun Zhong, Nicu Sebe
Computer Vision
EdgeNeRF: Edge-Guided Regularization for Neural Radiance Fields from Sparse Views
Weiqi Yu, Yiyang Yao, Lin He et al. (+1 more)
Neural Radiance Fields (NeRF) achieve remarkable performance in dense multi-view scenarios, but their reconstruction quality degrades significantly under sparse inputs due to
geometric artifacts. Existing methods utilize global depth regularization to mitigate artifacts, leading to the loss of
geometric boundary details. To address this problem, we propose EdgeNeRF, an edge-guided sparse-view 3D reconstruction
algorithm. Our method leverages the prior that abrupt changes in depth and normals generate edges. Specifically, we first extract edges from input images, then apply depth and normal regularization constraints to non-edge regions, enhancing
geometric consistency while preserving high-frequency details at boundaries. Experiments on LLFF and DTU
datasets demonstrate EdgeNeRF's superior performance, particularly in retaining sharp
geometric boundaries and suppressing artifacts. Additionally, the proposed edge-guided depth regularization module can be seamlessly integrated into other methods in a plug-and-play manner, significantly improving their performance without substantially increasing training time. Code is available at https://github.com/skyhigh404/edgenerf.
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All authors Weiqi Yu, Yiyang Yao, Lin He, Jianming Lv
Computer Vision
DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer
Xu Guo, Fulong Ye, Xinghui Li et al. (+6 more)
Video Face Swapping (VFS) requires seamlessly injecting a source identity into a target
video while meticulously preserving the original pose, expression, lighting, background, and dynamic information. Existing methods struggle to maintain identity similarity and attribute preservation while preserving temporal consistency. To address the challenge, we propose a comprehensive framework to seamlessly transfer the superiority of Image Face Swapping (IFS) to the
video domain. We first introduce a novel data pipeline SyncID-Pipe that pre-trains an Identity-Anchored
Video Synthesizer and combines it with IFS models to construct bidirectional ID quadruplets for explicit supervision. Building upon paired data, we propose the first
Diffusion Transformer-based framework DreamID-V, employing a core Modality-Aware Conditioning module to discriminatively inject multi-model conditions. Meanwhile, we propose a Synthetic-to-Real Curriculum mechanism and an Identity-Coherence Reinforcement Learning strategy to enhance visual realism and identity consistency under challenging scenarios. To address the issue of limited
benchmarks, we introduce IDBench-V, a comprehensive
benchmark encompassing diverse scenes. Extensive experiments demonstrate DreamID-V outperforms state-of-the-art methods and further exhibits exceptional versatility, which can be seamlessly adapted to various swap-related tasks.
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All authors Xu Guo, Fulong Ye, Xinghui Li, Pengqi Tu, Pengze Zhang, Qichao Sun, Songtao Zhao, Xiangwang Hou, Qian He
Computer Vision
AirSpatialBot: A Spatially-Aware Aerial Agent for Fine-Grained Vehicle Attribute Recognization and Retrieval
Yue Zhou, Ran Ding, Xue Yang et al. (+2 more)
Despite notable advancements in remote sensing
vision-
language models (VLMs), existing models often struggle with spatial understanding, limiting their effectiveness in real-world applications. To push the boundaries of VLMs in remote sensing, we specifically address vehicle imagery captured by drones and introduce a spatially-aware
dataset AirSpatial, which comprises over 206K instructions and introduces two novel tasks: Spatial Grounding and Spatial Question Answering. It is also the first remote sensing grounding
dataset to provide 3DBB. To effectively leverage existing image understanding of VLMs to spatial domains, we adopt a two-stage training strategy comprising Image Understanding Pre-training and Spatial Understanding Fine-tuning. Utilizing this trained spatially-aware VLM, we develop an aerial
agent, AirSpatialBot, which is capable of fine-grained vehicle attribute recognition and
retrieval. By dynamically integrating task planning, image understanding, spatial understanding, and task execution capabilities, AirSpatialBot adapts to diverse query requirements. Experimental results validate the effectiveness of our approach, revealing the spatial limitations of existing VLMs while providing valuable insights. The model, code, and
datasets will be released at https://github.com/VisionXLab/AirSpatialBot
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All authors Yue Zhou, Ran Ding, Xue Yang, Xue Jiang, Xingzhao Liu
Computer Vision
Mask-Guided Multi-Task Network for Face Attribute Recognition
Gong Gao, Zekai Wang, Jian Zhao et al. (+3 more)
Face Attribute Recognition (FAR) plays a crucial role in applications such as person re-identification, face
retrieval, and face editing. Conventional multi-task attribute recognition methods often process the entire feature map for feature extraction and attribute classification, which can produce redundant features due to reliance on global regions. To address these challenges, we propose a novel approach emphasizing the selection of specific feature regions for efficient feature learning. We introduce the Mask-Guided Multi-Task Network (MGMTN), which integrates Adaptive Mask Learning (AML) and Group-Global Feature Fusion (G2FF) to address the aforementioned limitations. Leveraging a pre-trained keypoint annotation model and a fully convolutional network, AML accurately localizes critical facial parts (e.g., eye and mouth groups) and generates group masks that delineate meaningful feature regions, thereby mitigating negative transfer from global region usage. Furthermore, G2FF combines group and global features to enhance FAR learning, enabling more precise attribute identification. Extensive experiments on two challenging facial attribute recognition
datasets demonstrate the effectiveness of MGMTN in improving FAR performance.
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All authors Gong Gao, Zekai Wang, Jian Zhao, Ziqi Xie, Xianhui Liu, Weidong Zhao
Computer Vision
SwinIFS: Landmark Guided Swin Transformer For Identity Preserving Face Super Resolution
Habiba Kausar, Saeed Anwar, Omar Jamal Hammad et al. (+2 more)
Face super-resolution aims to recover high-quality facial images from severely degraded low-resolution inputs, but remains challenging due to the loss of fine structural details and identity-specific features. This work introduces SwinIFS, a landmark-guided super-resolution framework that integrates structural priors with hierarchical
attention mechanisms to achieve identity-preserving reconstruction at both moderate and extreme upscaling factors. The method incorporates dense Gaussian heatmaps of key facial landmarks into the input representation, enabling the network to focus on semantically important facial regions from the earliest stages of processing. A compact Swin
Transformer backbone is employed to capture long-range contextual information while preserving local geometry, allowing the model to restore subtle facial textures and maintain global structural consistency. Extensive experiments on the CelebA
benchmark demonstrate that SwinIFS achieves superior perceptual quality, sharper reconstructions, and improved identity retention; it consistently produces more photorealistic results and exhibits strong performance even under $8\times$ magnification, where most methods fail to recover meaningful structure. SwinIFS also provides an advantageous balance between reconstruction accuracy and computational efficiency, making it suitable for real-world applications in facial enhancement, surveillance, and digital restoration. Our code, model weights, and results are available at https://github.com/Habiba123-stack/SwinIFS.
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All authors Habiba Kausar, Saeed Anwar, Omar Jamal Hammad, Ibrahim Radwan, Abdul Bais
Computer Vision
Evaluation of Convolutional Neural Network For Image Classification with Agricultural and Urban Datasets
Shamik Shafkat Avro, Nazira Jesmin Lina and Shahanaz Sharmin
This paper presents the development and evaluation of a custom Convolutional Neural Network (CustomCNN) created to study how architectural design choices affect multi-domain image classification tasks. The network uses residual connections, Squeeze-and-Excitation
attention mechanisms, progressive channel scaling, and Kaiming initialization to improve its ability to represent data and speed up training. The model is trained and tested on five publicly available
datasets: unauthorized vehicle detection, footpath encroachment detection, polygon-annotated road damage and manhole detection, MangoImageBD and PaddyVarietyBD. A comparison with popular CNN architectures shows that the CustomCNN delivers competitive performance while remaining efficient in computation. The results underscore the importance of thoughtful architectural design for real-world Smart City and agricultural imaging applications.
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Computer Vision
ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking
Xiaobao Wei, Zhangjie Ye, Yuxiang Gu et al. (+10 more)
Parking is a critical task for autonomous driving systems (ADS), with unique challenges in crowded parking slots and GPS-denied environments. However, existing works focus on 2D parking slot perception, mapping, and localization, 3D reconstruction remains underexplored, which is crucial for capturing complex spatial geometry in parking scenarios. Naively improving the visual quality of reconstructed parking scenes does not directly benefit autonomous parking, as the key entry point for parking is the slots perception module. To address these limitations, we curate the first
benchmark named ParkRecon3D, specifically designed for parking scene reconstruction. It includes sensor data from four surround-view fisheye cameras with calibrated extrinsics and dense parking slot annotations. We then propose ParkGaussian, the first framework that integrates 3D Gaussian Splatting (3DGS) for parking scene reconstruction. To further improve the
alignment between reconstruction and downstream parking slot detection, we introduce a slot-aware reconstruction strategy that leverages existing parking perception methods to enhance the synthesis quality of slot regions. Experiments on ParkRecon3D demonstrate that ParkGaussian achieves state-of-the-art reconstruction quality and better preserves perception consistency for downstream tasks. The code and
dataset will be released at: https://github.com/wm-research/ParkGaussian
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All authors Xiaobao Wei, Zhangjie Ye, Yuxiang Gu, Zunjie Zhu, Yunfei Guo, Yingying Shen, Shan Zhao, Ming Lu, Haiyang Sun, Bing Wang, Guang Chen, Rongfeng Lu, Hangjun Ye
Computer Vision
Unsupervised SE(3) Disentanglement for in situ Macromolecular Morphology Identification from Cryo-Electron Tomography
Mostofa Rafid Uddin, Mahek Vora, Qifeng Wu et al. (+2 more)
Cryo-electron
tomography (cryo-ET) provides direct 3D visualization of macromolecules inside the
cell, enabling analysis of their in situ morphology. This morphology can be regarded as an SE(3)-invariant, denoised volumetric representation of subvolumes extracted from tomograms. Inferring morphology is therefore an inverse problem of estimating both a template morphology and its SE(3) transformation. Existing expectation-maximization based solution to this problem often misses rare but important morphologies and requires extensive manual hyperparameter tuning. Addressing this issue, we present a disentangled deep representation learning framework that separates SE(3) transformations from morphological content in the representation space. The framework includes a novel multi-choice learning module that enables this disentanglement for highly noisy cryo-ET data, and the learned morphological content is used to generate template morphologies. Experiments on simulated and real cryo-ET
datasets demonstrate clear improvements over prior methods, including the discovery of previously unidentified macromolecular morphologies.
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All authors Mostofa Rafid Uddin, Mahek Vora, Qifeng Wu, Muyuan Chen, Min Xu
Computer Vision
Garment Inertial Denoiser (GID): Endowing Accurate Motion Capture via Loose IMU Denoiser
Jiawei Fang, Ruonan Zheng, Xiaoxia Gao et al. (+4 more)
Wearable inertial motion capture (MoCap) provides a portable, occlusion-free, and
privacy-preserving alternative to camera-based systems, but its accuracy depends on tightly attached sensors - an intrusive and uncomfortable requirement for daily use.
Embedding IMUs into loose-fitting garments is a desirable alternative, yet sensor-body displacement introduces severe, structured, and location-dependent corruption that breaks standard inertial pipelines. We propose GID (Garment Inertial Denoiser), a lightweight, plug-and-play
Transformer that factorizes loose-wear MoCap into three stages: (i) location-specific denoising, (ii) adaptive cross-wear fusion, and (iii) general pose prediction. GID uses a location-aware expert architecture, where a shared spatio-temporal backbone models global motion while per-IMU expert heads specialize in local garment dynamics, and a lightweight fusion module ensures cross-part consistency. This inductive bias enables stable training and effective learning from limited paired loose-tight IMU data. We also introduce GarMoCap, a combined public and newly collected
dataset covering diverse users, motions, and garments. Experiments show that GID enables accurate, real-time denoising from single-user training and generalizes across unseen users, motions, and garment types, consistently improving state-of-the-art inertial MoCap methods when used as a drop-in module.
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All authors Jiawei Fang, Ruonan Zheng, Xiaoxia Gao, Shifan Jiang, Anjun Chen, Qi Ye, Shihui Guo
Computer Vision
Advanced Machine Learning Approaches for Enhancing Person Re-Identification Performance
Dang H. Pham, Tu N. Nguyen and Hoa N. Nguyen
Person re-identification (ReID) plays a critical role in intelligent surveillance systems by linking identities across multiple cameras in complex environments. However, ReID faces significant challenges such as appearance variations, domain shifts, and limited labeled data. This dissertation proposes three advanced approaches to enhance ReID performance under supervised, unsupervised domain adaptation (UDA), and fully unsupervised settings. First, SCM-ReID integrates supervised contrastive learning with hybrid loss optimization (classification, center, triplet, and centroid-triplet losses), improving discriminative feature representation and achieving state-of-the-art accuracy on
Market-1501 and CUHK03
datasets. Second, for UDA, IQAGA and DAPRH combine GAN-based image augmentation, domain-invariant mapping, and pseudo-label refinement to mitigate domain discrepancies and enhance cross-domain generalization. Experiments demonstrate substantial gains over baseline methods, with mAP and Rank-1 improvements up to 12% in challenging transfer scenarios. Finally, ViTC-UReID leverages
Vision Transformer-based feature encoding and camera-aware proxy learning to boost unsupervised ReID. By integrating global and local
attention with camera identity constraints, this method significantly outperforms existing unsupervised approaches on large-scale
benchmarks. Comprehensive evaluations across CUHK03,
Market-1501, DukeMTMC-reID, and MSMT17 confirm the effectiveness of the proposed methods. The contributions advance ReID research by addressing key limitations in feature learning, domain adaptation, and label noise handling, paving the way for robust deployment in real-world surveillance systems.
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Computer Vision
Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding
Yixuan Lai, He Wang, Kun Zhou et al. (+1 more)
Human identity-preserving text-to-
video generation remains challenging under large changes in viewpoint, facial expression, illumination, and motion. Existing methods condition the generator on a single reference portrait, but a static image cannot capture how identity-bearing cues evolve across views and expressions, leading to face deformation, pose locking, identity drift, or over-smoothed faces. We observe that a short reference clip naturally provides richer temporal and multi-view identity cues than any single image, motivating a
video-referential formulation. This richer signal, however, introduces a new challenge: identity evidence is distributed across many frames and must be distilled into a compact, stable representation under a limited
token budget. To this end, we propose Slot-ID, a lightweight identity-conditioning framework built on a frozen text-to-
video backbone. Slot-ID employs a slot-based temporal identity encoder with Sinkhorn-routed iterative reading to distill a compact, stable set of identity
tokens from the reference clip, complemented by an image-anchor stream for dual-source conditioning. Extensive experiments demonstrate that Slot-ID outperforms state-of-the-art methods in identity preservation and visual naturalness while remaining competitive in
prompt following, with particularly large gains under challenging pose, expression, and motion variations.
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All authors Yixuan Lai, He Wang, Kun Zhou, Tianjia Shao
Computer Vision
Achieving Fine-grained Cross-modal Understanding through Brain-inspired Hierarchical Representation Learning
Weihang You, Hanqi Jiang, Yi Pan et al. (+3 more)
Understanding neural responses to visual stimuli remains challenging due to the inherent complexity of
brain representations and the modality gap between neural data and visual inputs. Existing methods, mainly based on reducing neural decoding to generation tasks or simple correlations, fail to reflect the hierarchical and temporal processes of visual processing in the
brain. To address these limitations, we present NeuroAlign, a novel framework for fine-grained fMRI-
video alignment inspired by the hierarchical organization of the human visual system. Our framework implements a two-stage mechanism that mirrors biological visual pathways: global semantic understanding through Neural-Temporal Contrastive Learning (NTCL) and fine-grained pattern matching through enhanced vector quantization. NTCL explicitly models temporal dynamics through bidirectional prediction between modalities, while our DynaSyncMM-EMA approach enables dynamic multi-modal fusion with adaptive weighting. Experiments demonstrate that NeuroAlign significantly outperforms existing methods in cross-modal
retrieval tasks, establishing a new paradigm for understanding visual cognitive mechanisms.
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All authors Weihang You, Hanqi Jiang, Yi Pan, Junhao Chen, Tianming Liu, Fei Dou
Computer Vision
LinMU: Multimodal Understanding Made Linear
Hongjie Wang and Niraj K. Jha
Modern
Vision-
Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-
attention, which prevents their deployment on edge devices and makes their understanding of high-resolution images and long-context
videos prohibitively expensive. To address this challenge, we introduce LinMU (Linear-complexity
Multimodal Understanding), a VLM design that achieves linear complexity for the
language model decoder without using any quadratic-complexity modules while maintaining the performance of global-
attention-based VLMs. LinMU replaces every self-
attention layer in the
language model decoder with an M-MATE block: a dual-branch module that combines a bidirectional state-space model for global context (Flex-MA branch) with localized Swin-style window
attention (Local-Swin branch) for adjacent correlations. To transform a pre-trained VLM into the LinMU architecture, we propose a three-stage
distillation framework that (i) initializes both branches with self-
attention weights and trains the Flex-MA branch alone, (ii) unfreezes the Local-Swin branch and fine-tunes it jointly with the Flex-MA branch, and (iii) unfreezes the remaining blocks and fine-tunes them using LoRA adapters, while regressing on hidden states and
token-level logits of the frozen VLM teacher. On MMMU, TextVQA, LongVideoBench,
Video-MME, and other
benchmarks, LinMU matches the performance of teacher models, yet reduces Time-To-First-
Token (TTFT) by up to 2.7$\times$ and improves
token throughput by up to 9.0$\times$ on minute-length
videos. Ablations confirm the importance of each
distillation stage and the necessity of the two branches of the M-MATE block. The proposed framework demonstrates that state-of-the-art
multimodal reasoning can be achieved without quadratic
attention, thus opening up avenues for long-context VLMs that can deal with high-resolution images and long
videos.
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Computer Vision · Cross-listed from q-bio.TO
Quantifying Local Strain Field and Deformation in Active Contraction of Bladder Using a Pretrained Transformer Model: A Speckle-Free Approach
Alireza Asadbeygi, Anne M. Robertson, Yasutaka Tobe et al. (+5 more)
Accurate quantification of local strain fields during bladder contraction is essential for understanding the biomechanics of bladder micturition, in both health and disease. Conventional digital image correlation (DIC) methods have been successfully applied to various biological tissues; however, this approach requires artificial speckling, which can alter both passive and active properties of the tissue. In this study, we introduce a speckle-free framework for quantifying local strain fields using a state-of-the-art, zero-shot
transformer model, CoTracker3. We utilized a custom-designed, portable isotonic biaxial apparatus compatible with multiphoton microscopy (MPM) to demonstrate this approach, successfully tracking natural bladder lumen textures without artificial markers.
Benchmark tests validated the method's high pixel accuracy and low strain errors. Our framework effectively captured heterogeneous deformation patterns, despite complex folding and buckling, which conventional DIC often fails to track. Application to in vitro active bladder contractions in four rat specimens (n=4) revealed statistically significant anisotropy (p<0.01), with higher contraction longitudinally compared to circumferentially. Multiphoton microscopy further illustrated and confirmed heterogeneous morphological changes, such as large fold formation during active contraction. This non-invasive approach eliminates speckle-induced artifacts, enabling more physiologically relevant measurements, and has broad applicability for material testing of other biological and engineered systems.
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All authors Alireza Asadbeygi, Anne M. Robertson, Yasutaka Tobe, Masoud Zamani, Sean D. Stocker, Paul Watton, Naoki Yoshimura, Simon C Watkins
Computer Vision
VReID-XFD: Video-based Person Re-identification at Extreme Far Distance Challenge Results
Kailash A. Hambarde, Hugo Proença, Md Rashidunnabi et al. (+18 more)
Person re-identification (ReID) across aerial and ground views at extreme far distances introduces a distinct operating regime where severe resolution degradation, extreme viewpoint changes, unstable motion cues, and clothing variation jointly undermine the appearance-based assumptions of existing ReID systems. To study this regime, we introduce VReID-XFD, a
video-based
benchmark and community challenge for extreme far-distance (XFD) aerial-to-ground person re-identification. VReID-XFD is derived from the DetReIDX
dataset and comprises 371 identities, 11,288 tracklets, and 11.75 million frames, captured across altitudes from 5.8 m to 120 m, viewing angles from oblique (30 degrees) to nadir (90 degrees), and horizontal distances up to 120 m. The
benchmark supports aerial-to-aerial, aerial-to-ground, and ground-to-aerial evaluation under strict identity-disjoint splits, with rich physical metadata. The VReID-XFD-25 Challenge attracted 10 teams with hundreds of submissions. Systematic analysis reveals monotonic performance degradation with altitude and distance, a universal disadvantage of nadir views, and a trade-off between peak performance and robustness. Even the best-performing SAS-PReID method achieves only 43.93 percent mAP in the aerial-to-ground setting. The
dataset, annotations, and official evaluation protocols are publicly available at https://www.it.ubi.pt/DetReIDX/ .
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All authors Kailash A. Hambarde, Hugo Proença, Md Rashidunnabi, Pranita Samale, Qiwei Yang, Pingping Zhang, Zijing Gong, Yuhao Wang, Xi Zhang, Ruoshui Qu, Qiaoyun He, Yuhang Zhang, Thi Ngoc Ha Nguyen, Tien-Dung Mai, Cheng-Jun Kang, Yu-Fan Lin, Jin-Hui Jiang, Chih-Chung Hsu, Tamás Endrei, György Cserey, Ashwat Rajbhandari
Computer Vision
ShadowGS: Shadow-Aware 3D Gaussian Splatting for Satellite Imagery
Feng Luo, Hongbo Pan, Xiang Yang et al. (+3 more)
3D Gaussian Splatting (3DGS) has emerged as a novel paradigm for 3D reconstruction from satellite imagery. However, in multi-temporal satellite images, prevalent shadows exhibit significant inconsistencies due to varying illumination conditions. To address this, we propose ShadowGS, a novel framework based on 3DGS. It leverages a physics-based rendering equation from remote sensing, combined with an efficient ray marching technique, to precisely model geometrically consistent shadows while maintaining efficient rendering. Additionally, it effectively disentangles different illumination components and apparent attributes in the scene. Furthermore, we introduce a shadow consistency constraint that significantly enhances the
geometric accuracy of 3D reconstruction. We also incorporate a novel shadow map prior to improve performance with sparse-view inputs. Extensive experiments demonstrate that ShadowGS outperforms current state-of-the-art methods in shadow decoupling accuracy, 3D reconstruction precision, and novel view synthesis quality, with only a few minutes of training. ShadowGS exhibits robust performance across various settings, including RGB, pansharpened, and sparse-view satellite inputs.
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All authors Feng Luo, Hongbo Pan, Xiang Yang, Baoyu Jiang, Fengqing Liu, Tao Huang