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Cao Thien Tan, Phan Thi Thu Trang, Do Nghiem Duc, Ho Ngoc Anh, Hanyang Zhuang, Nguyen Duc Dung

Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to expand the effective receptive field efficiently. UCAN combines window-based spatial attention with a Hedgehog Attention mechanism to model both local texture and long-range dependencies, and introduces a distillation-based large-kernel module to preserve high-frequency structure without heavy computation. In addition, we employ cross-layer parameter sharing to further reduce complexity. On Manga109 (4x), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models. Extensive experiments show that UCAN achieves a superior trade-off between accuracy, efficiency, and scalability, making it well-suited for practical high-resolution image restoration.

Chung-Shien Brian Wang, Christian Schmidt, Jens Piekenbrinck, Bastian Leibe

Efficient and accurate feed-forward multi-view reconstruction has long been an important task in computer vision. Recent transformer-based models like VGGT, \pi^3 and MapAnything have demonstrated remarkable performance with relatively simple architectures. However, their scalability is fundamentally constrained by the quadratic complexity of global attention, which imposes a significant runtime bottleneck when processing large image sets. In this work, we empirically analyze the global attention matrix of these models and observe that the probability mass concentrates on a small subset of patch-patch interactions corresponding to cross-view geometric correspondences. Building on this insight and inspired by recent advances in large language models, we propose a training-free, block-sparse replacement for dense global attention, implemented with highly optimized kernels. Our method accelerates inference by more than 3xwhile maintaining comparable task performance. Evaluations on a comprehensive suite of multi-view benchmarks demonstrate that our approach seamlessly integrates into existing global attention-based architectures such as VGGT, \pi^3, and MapAnything, while substantially improving scalability to large image collections.

Yuqiao He, Xiaoyan Liu, Jianxu Mao, Yaonan Wang, Hui Zhang, Lizhu Liu, Yurong Chen, Wenbin He

Coded Aperture Snapshot Spectral Imaging (CASSI) has emerged as a prominent technique for efficient hyperspectral imaging. However, the tight coupling between physical encoding and computational decoding makes CASSI highly sensitive to slight hardware misalignments, which can significantly degrade reconstruction quality. Existing methods either assume ideal imaging conditions or rely on offline calibration, making them vulnerable to dynamic perturbations, such as thermal expansion and mechanical vibrations, which cause mask shifts. To address these limitations, we propose a Self-supervised Geometry Degradation Estimation (SGDE) framework that explicitly models mask misalignments as an affine transformation and embeds it into the imaging model. SGDE jointly estimates affine parameters and reconstructs the hyperspectral image in a self-supervised manner, eliminating the need for reference targets or device-specific training data. Furthermore, we introduce a multi-kernel estimation strategy to enhance the robustness of degradation estimation under large perturbations. Extensive experiments on both simulated and real-world datasets demonstrate that SGDE achieves superior robustness against mask misalignments. Moreover, the estimated affine parameters can be directly integrated into existing reconstruction algorithms, enabling plug-and-play degradation estimation for practical CASSI systems. The code is available at https://github.com/heyuqiao/SGDE.

Young-Han Son, Dong-Hee Shin, Deok-Joong Lee, Hyun Jung Lee, Tae-Eui Kam

In recent years, the increasing demand for smaller and more powerful semiconductors highlighted the critical role of lithography--a key stage in semiconductor manufacturing responsible for precise mask design and wafer patterning. To meet these demands, the semiconductor industry has increasingly adopted computational lithography, employing machine learning and deep learning techniques to accelerate advancements in lithographic technology. Despite the various research efforts and successes in computational lithography, there remains a lack of explicit incorporation of physical principles. This gap limits the ability of existing methods to fully capture the complex physical phenomena inherent in lithography behaviors. To bridge this gap, we propose OptiCo, a novel convolutional neural network that seamlessly integrates optical diffraction principles into its architecture. At its core, OptiCo employs an optical phase kernel to model phase variations resulting from light propagation, effectively capturing the physical interactions among light, masks, and wafers. We evaluate OptiCo on semiconductor lithography benchmarks, demonstrating its superior performance in mask optimization tasks, with its remarkable generalization capabilities in OOD datasets.

Donglai Xiang, Vismay Modi, Rishit Dagli, Ty Trusty, Gilles Daviet, Anka He Chen, Nicholas Sharp, David I.W. Levin

We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynamic simulation represents the input geometry by either meshes, which can be difficult to obtain due to challenges in scanning and triangulating complex shapes, or by neural fields that require per-shape optimization. We propose to adopt a Reproducing Kernel Particle Method (RKPM) representation, which enables the construction of reduced-order skinning weights by solving a generalized eigensystem on the Hessian matrix of the elastic energy. We demonstrate that this formulation not only leads to a 40x training speedup compared with the per-shape optimization of neural fields, but also achieves lower simulation error when evaluated against the converged results of finite element method. We show our simulation results on a wide variety of objects in different representations including meshes and Gaussian splats, as well as the application of our method in the downstream task of robot simulation.

Shengdong Xue, Haoxiang Ma, Hao Chen, Zhen Yang, Yongjian Deng

Event-based motion deblurring has attracted increasing attention, as the high temporal resolution of event cameras provides motion cues unavailable to conventional RGB sensors, thereby enabling more effective deblurring. In real-world scenes, motion blur is often complex and nonlinear, with different regions exhibiting diverse motion speeds and directions. However, most existing approaches rely on handcrafted event representations that overlook such spatiotemporal motion heterogeneity, resulting in suboptimal deblurring performance. To address this limitation, we propose a learnable 3D Gaussian event representation module that adaptively selects key spatiotemporal coordinates for deblurring based on the blurred image content and the event density distribution. The event stream is then aggregated with 3D Gaussian weighting kernels to extract local motion features that are sensitive to motion direction and speed. Furthermore, to fully exploit the motion information encoded in the event representation, we adopt a two-stage fusion strategy. In the first stage, local motion features are used to enhance fine detail restoration. In the second stage, a bidirectional attention fusion module leverages one-dimensional Gaussian-weighted event frames to correct global spatial misalignment, leading to more accurate structural alignment. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method and show that it consistently outperforms state-of-the-art approaches.

Chen Zhang, Wei Zuo, Bingyang Cheng, Yikun Wang, Wei-Bin Kou, Yik-Chung Wu, Ngai Wong

Implicit Neural Representations (INRs) parameterize continuous signals via multilayer perceptrons (MLPs), enabling compact, resolution-independent modeling for tasks like image, audio, and 3D reconstruction. However, fitting high-resolution signals demands optimizing over millions of coordinates, incurring prohibitive computational costs. To address it, we propose NTK-Guided Implicit Neural Teaching (NINT), which accelerates training by dynamically selecting coordinates that maximize global functional updates. Leveraging the Neural Tangent Kernel (NTK), NINT scores examples by the norm of their NTK-augmented loss gradients, capturing both fitting errors and heterogeneous leverage (self-influence and cross-coordinate coupling). This dual consideration enables faster convergence compared to existing methods. Through extensive experiments, we demonstrate that NINT significantly reduces training time by nearly half while maintaining or improving representation quality, establishing state-of-the-art acceleration among recent sampling-based strategies.

Mashiat Mustaq, Xavier M. Tricoche

Understanding the global structure of neural network loss landscapes is important for gaining insight into model merging, hyperparameter selection, generalization, and the relationships between distinct solutions. Visualizing the global structure of loss landscapes is very challenging because of the high dimensionality of the parameter space of neural networks. Prior work has primarily focused on visualizing the loss landscape around one single basin, missing how different minima or solutions relate to each other. We introduce Globscope, a framework that provides a global view of the loss landscape across multiple solutions or basins. Globscope learns a low-dimensional non-linear manifold of model parameters using an autoencoder, enabling both latent-space visualization and reconstruction of full model weights. Then it summarizes the relations and connections among minima on this manifold through topological data analysis. Our framework produces continuous, interpretable visualizations that reveal global connectivity patterns in the landscape. We compare Globscope with kernel-based methods and demonstrate its superior performance in preserving the global structure across diverse solutions. We further show how Globscope can be used to analyze two applications: revealing global low-loss solution pathways between distinct solutions using mode connectivity algorithms, and visualizing permutation symmetries of different solutions using re-basin approaches.

Anmin Liu, Ruixuan Yang, Huiqiang Jiang, Bin Lin, Minmin Sun, Yong Li, Chen Zhang, Tao Xie

Long-context video understanding and generation pose a significant computational challenge for Transformer-based video models due to the quadratic complexity of self-attention. While existing sparse attention methods employ coarse-grained patterns to improve efficiency, they typically incur redundant computation and suboptimal performance. To address this issue, in this paper, we propose VecAttention, a novel vector-wise sparse attention framework that achieves superior accuracy-efficiency trade-offs for video models. We observe that video attention maps exhibit a strong vertical-vector sparse pattern, and further demonstrate that this vertical-vector pattern offers consistently better accuracy-sparsity trade-offs compared with existing coarse-grained sparse patterns. Based on this observation, VecAttention dynamically selects and processes only informative vertical vectors through a lightweight important-vector selection that minimizes memory access overhead and an optimized vector sparse attention kernel. Comprehensive evaluations on video understanding (VideoMME, LongVideoBench, and VCRBench) and generation (VBench) tasks show that VecAttention delivers a 2.65xspeedup over full attention and a 1.83xspeedup over state-of-the-art sparse attention methods, with comparable accuracy to full attention.

Qiuhai Yan, Kang Chen, Zhengjie Lu, Tingting Wang, Faming Fang, Guixu Zhang

Implicit neural representation (INR) based methods learn a continuous mapping from a low-resolution (LR) target magnetic resonance (MR) image and a high-resolution (HR) reference image to achieve arbitrary-scale super-resolution (SR). However, their inherent spectral bias favors learning low-frequency (LF) components, often failing to capture the sharp transitions at anatomical boundaries and resulting in the loss of high-frequency (HF) details. Inspired by 3D Gaussian splatting, we propose GaussM2ASR (Gaussian Multi-contrast MRI Arbitrary-scale Super-Resolution), which converts the challenging task of HF anatomical reconstruction into a smoother parameter optimization problem by learning the parameters of anisotropic 2D Gaussian kernels. To handle inter-contrast discrepancies, we introduce an anatomy-guided pipeline comprising three core modules: a Structure Prior Modulation Fusion (SPMF) module for feature enhancement; an Anatomy-Guided Dual-Domain Cross Attention (AG-DDCA) module for joint spatial-frequency modeling; and an Anatomy-Guided Gaussian Parametrizer (AGGP) that leverages gradient-based sparse attention to concentrate Gaussian centers on critical anatomical structures. Extensive experiments on multiple datasets demonstrate that GaussM2ASR surpasses state-of-the-art methods in recovering fine anatomical details. Our source codes have been released at https://github.com/Qiuhai-CV/GaussM2ASR.

Yaoxin Yang, Peng Ye, Xudong Tan, Chongjun Tu, Maosen Zhao, Jia Hao, Tao Chen

Multimodal large language models suffer from substantial inference overhead since multimodal KV Cache grows proportionally with the visual input length. Existing multimodal KV cache compression methods mostly rely on attention score to reduce cache size, which makes them are incompatible with established efficient attention kernels (e.g., FlashAttention) and ignores the contribution of value vectors to the attention output. In this work, we revisit multimodal KV cache compression from the perspective of the KV matrices' distribution. First, we observe that frequency-domain energy of multimodal KV matrices is predominantly concentrated in low-frequency and extract this principal energy via a low-pass filter. Further, we find that removing KV pairs that deviate substantially from this principal energy leads to a pronounced performance drop, which we define as Outlier KVs. Considering Outlier KVs are more likely to encode features critical for inference, we propose FlashCache, a frequency-domain-guided, Outlier-KV-aware KV Cache compression framework. First, we introduce an Outlier KV Recognition Module that models the principal component of multimodal KV matrices in the frequency domain and preferentially retains KV pairs that significantly deviate from it. Furthermore, Dynamic Budget Allocation Module is designed to adaptively determine the per-layer KV cache size to retain more Outlier KVs. Experiments on multiple MLLMs and benchmarks demonstrate that FlashCache outperforms state-of-the-art multimoal KV compression methods, achieving up to 1.69x faster decoding with 80% lower KV memory usage while maintaining task performance.

Lance C. Moore, Aranyo Mitra, Ryan Truong, Karoline Kallis, Kelly Kisling, Sandra M. Meyers, Nuno Vasconcelos

Brachytherapy is a complex radiation oncology problem that requires the simultaneous prediction of radiation dose, which is used for treatment planning, and a set of machine parameters, known as dwell times, used for treatment delivery. We propose Direct Dwell Time Transformer (D2T2), the first deep learning architecture that directly predicts dwell times during dose prediction. D2T2 is a two stage model, where the first stage predicts a vector of dwell times and the second implements the physical model of radiation delivery, namely a linear combination of radiation dose kernels. Besides the automatic prediction of dwell times, this has the benefit of constraining the model to make physically plausible dose predictions, when trained end-to-end. To enhance this training, we also propose a new loss function, denoted as the gamma loss, based on the prediction of the gamma index, which is the gold standard of dose comparisons. This is implemented by training a model to predict the latter using a synthetic dataset of groundtruth and predicted dose pairs. We train D2T2 on a large dataset of ~5,000 clinical brachytherapy plans---the largest such dataset to date---spanning gynecological, breast, and other treatment sites. Results demonstrate that D2T2 outperforms existing methods in both accuracy and speed. Notably, D2T2 produces deliverable plans and physically valid dose distributions in a single forward pass, for any application of brachytherapy, hours faster than manual planning and minutes faster than more recent automated methods.

Jaesung Rim, Woohyeok Kim, Haeyun Lee, Heemin Yang, Ke Wang, Sunghyun Cho

Modern cameras, such as smartphone cameras and DSLRs, are equipped with gyro sensors that measure motion of the camera. While the motion information is valuable for deblurring, gyro-based deblurring has not been widely studied, particularly for video. A few gyro-based video deblurring methods have been proposed, but they exhibit inherent limitations. First, gyro sensors capture only rotational motion, leading these methods to ignore translational motion. Second, their dependence on simplified blur models and deconvolution-based solutions restricts overall performance. To address these limitations, we introduce GyroDVD, the first learning-based framework for gyro-based video deblurring. We propose a novel blur kernel construction scheme that jointly accounts for rotational and translational motion. A video deblurring network then restores sharp videos by exploiting the constructed kernels together with the video frames. For training and evaluation, we introduce the GyroVD dataset, a large-scale and realistic dataset specifically designed for gyro-based deblurring. Extensive experiments demonstrate that our method significantly outperforms prior gyro-based image and video deblurring methods.

Lihan Li, Haofeng Zhong, Rui Bu, Mingchao Sun, Wenzheng Chen, Baoquan Chen, Yangyan Li

Existing convolutional learning methods for 3D point cloud data are divided into two paradigms: point-based methods that preserve geometric precision but often face performance challenges, and voxel-based methods that achieve high efficiency through quantization at the cost of geometric fidelity. This loss of precision is a critical bottleneck for tasks such as point cloud registration. We propose PointCNN++, a novel architectural design that fundamentally mitigates this precision-performance trade-off. It generalizes sparse convolution from voxels to points, treating voxel-based convolution as a specialized, degraded case of our more general point-based convolution. First, we introduce a point-centric convolution where the receptive field is centered on the original, high-precision point coordinates. Second, to make this high-fidelity operation performant, we design a computational strategy that operates natively on points. We formulate the convolution on native points as a Matrix-Vector Multiplication and Reduction (MVMR) problem, for which we develop a dedicated, highly-optimized GPU kernel. Experiments demonstrate that PointCNN++ uses an order of magnitude less memory and is several times faster than representative point-based methods. Furthermore, when used as a simple replacement for the voxel-based backbones it generalizes, it significantly improves point cloud registration accuracies while proving both more memory-efficient and faster. PointCNN++ shows that preserving geometric detail and achieving high performance are not mutually exclusive, paving the way for a new class of 3D learning with high fidelity and efficiency.

Tingjia Zhang, Bo Chen, Shengzhong Liu, Fan Wu, Guihai Chen

Recent breakthroughs in 3D Gaussian Splatting (3DGS) have advanced neural rendering with high fidelity and speed. However, its performance degrades significantly in large-scale scenes due to the computational burden of tile-based rasterization. Existing optimization efforts either require costly scene re-training or focus on narrow aspects of the pipeline, overlooking critical inefficiencies in real-world deployments. Through a comprehensive analysis, we identify three primary sources of redundancy and low GPU utilization: redundant inter-frame pre-processing, viewpoint-based occlusion redundancy, and severe tile-level load imbalance. To address these issues, we propose CaT-GS, a novel and efficient 3DGS rendering pipeline. CaT-GS introduces a speculative multi-frame preprocessing method to eliminate redundant computations across consecutive frames, and an inter-frame caching mechanism to eliminate viewpoint redundant rendering stages. Furthermore, it refactors rasterization tasks with a dedicated kernel to mitigate tile load imbalance, significantly boosting GPU utilization. Extensive experiments demonstrate that CaT-GS achieves a speedup of up to 10 times over the original 3DGS and up to 70% over previous state-of-the-art methods, establishing a new benchmark for high-fidelity, real-time rendering of large-scale scenes.

Yunuo Chen, Bing He, Zezheng Lyu, Hongwei Hu, Qunshan Gu, Yuan Tian, Guo Lu

Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inherently rigid. Standard CNN kernels and window-based attention mechanisms impose fixed receptive fields and static connectivity patterns, which potentially couple non-redundant pixels simply due to their proximity in Euclidean space. This rigidity limits the model's ability to adaptively capture spatially varying redundancy across the image, particularly at the global level. To overcome these limitations, we propose a content-adaptive image compression framework based on Graph Neural Networks (GNNs). Specifically, our approach constructs dual-scale graphs that enable flexible, data-driven receptive fields. Furthermore, we introduce adaptive connectivity by dynamically adjusting the number of neighbors for each node based on local content complexity. These innovations empower our Graph-based Learned Image Compression (GLIC) model to effectively model diverse redundancy patterns across images, leading to more efficient and adaptive compression. Experiments demonstrate that GLIC achieves state-of-the-art performance, achieving BD-rate reductions of 19.29%, 21.69%, and 18.71% relative to VTM-9.1 on Kodak, Tecnick, and CLIC, respectively. Code will be released at https://github.com/UnoC-727/GLIC.

Mijeong Kim, Jungtaek Kim, Bohyung Han

We present GP-4DGS, a novel framework that integrates Gaussian Processes (GPs) into 4D Gaussian Splatting (4DGS) for principled probabilistic modeling of dynamic scenes. While existing 4DGS methods focus on deterministic reconstruction, they are inherently limited in capturing motion ambiguity and lack mechanisms to assess prediction reliability. By leveraging the kernel-based probabilistic nature of GPs, our approach introduces three key capabilities: (i) uncertainty quantification for motion predictions, (ii) motion estimation for unobserved or sparsely sampled regions, and (iii) temporal extrapolation beyond observed training frames. To scale GPs to the large number of Gaussian primitives in 4DGS, we design spatio-temporal kernels that capture the correlation structure of deformation fields and adopt variational Gaussian Processes with inducing points for tractable inference. Our experiments show that GP-4DGS enhances reconstruction quality while providing reliable uncertainty estimates that effectively identify regions of high motion ambiguity. By addressing these challenges, our work takes a meaningful step toward bridging probabilistic modeling and neural graphics.

Jaroslav Moravec, Radim Sara, Akihiro Sugimoto

Maintaining long-term accuracy of stereo camera calibration parameters is important for autonomous systems' perception. This work proposes Online Tracking of Essential Matrix by Stochastic Optimization (TESO). The core mechanisms of TESO are: 1) a robust loss function based on kernel correlation over tentative correspondences, 2) an adaptive online stochastic optimization on the essential manifold. TESO has low CPU and memory requirements, relies on a few hyperparameters, and eliminates the need for data-driven training, enabling the usage in resource-constrained online perception systems. We evaluated the influence of TESO on geometric precision, rectification quality, and stereo depth consistency. On the large-scale MAN TruckScenes dataset, TESO tracks rotational calibration drift with 0.12 deg precision in the Y-axis-critical for stereo accuracy-while the X- and Z-axes are five times more precise. Tracking applied to sequences with simulated drift shows similar precision with respect to the reference as tracking applied to no-drift sequences, indicating the tracker is unbiased. On the KITTI dataset, TESO revealed systematic inconsistencies in extrinsic parameters across stereo pairs, confirming previous published findings. We verified that intrinsic decalibration affected these errors, as evidenced by the conflicting behavior of the rectification and depth metrics. After correcting the reference calibration, TESO improved its rotation precision around the Y-axis 20 times to 0.025 deg and its depth accuracy 50 times. Despite its lightweight design, direct optimization of the proposed TESO loss function alone achieves accuracy comparable to that of neural network-based single-frame methods.

Junshu Zhang, Sicheng Zhao, Xin Zhao, Fan Yang, Ruike Chen, Jungong Han, Guiguang Ding

Bird's-Eye-View (BEV) detection has become a dominant paradigm for 3D object detection in autonomous driving, due to its strong perception capability. However, most existing methods mainly focus on constructing high-quality BEV feature representations, while neglecting the design of task-specific detection heads. In practice, they directly adopt the center-based head originally developed for 2D detection, without any specific optimization. This leads to three inherent limitations: (i) a geometric mismatch between the Gaussian kernel used for classification and the real BEV object, (ii) degraded end-to-end performance without Non-Maximum Suppression (NMS), and (iii) sparse supervisory signals. To address these issues, we propose Spe-BEVHead, a detection head specifically tailored for BEV 3D object detection. Spe-BEVHead introduces three BEV-specific adaptations: (1) a Rotated Box Kernel that generates geometry-aligned classification weights, (2) a Local Response Refinement Module (LRRM) that suppresses non-peak responses and improves end-to-end performance, and (3) a dual-branch architecture that provides richer supervisory signals to promote more robust learning while inherently preserving the performance for end-to-end inference. Extensive experiments show that Spe-BEVHead can be seamlessly integrated into existing BEV backbones, delivering direct performance gains while retaining competitive performance under the challenging end-to-end setting.

Tao Xie, Tao An, Feng Liu, Wensheng Jin, Zhengyu Li, Lijun Zhao, Ruifeng Li

In this work, we observe that for indoor 3D object detection, fundamental geometric cues induce homogeneous spatial responses across scenes, whereas scene-specific structure yields heterogeneous signatures. However, existing detectors lack effective mechanisms to jointly extract and exploit such dual properties, which imposes inherent limitations on detection performance. Guided by this insight, we propose H^2A^2, a homogeneity-aware and heterogeneity-aware feature perception network for unified indoor 3D object detection under cross-scene training paradigms.Technically, we introduce a structural-feature-aware kernel selection (SF-KS) method, which encompasses three core components:(i) task-aware linear modulation, a channel-wise affine transformation that strengthens scene-structural feature representation; (ii) kernel weight selection strategy that integrates an offset validity prior to suppress non-informative cross-scene transfer while utilizing a structural consistency posterior to capture scene-homogeneous cues. and (iii) task-aware channel gating that suppresses scene-irrelevant feature responses. Overall, SF-KS enables the precise optimization of homogeneous features while specializing in scene-specific heterogeneous ones. In addition, to stabilize cross-scene optimization, we further introduce norm-based gradient homogenization (NGH) algorithm, which normalizes and dynamically reweights per-task gradient norms to mitigate conflicts and promote consistent updates. Extensive experiments on diverse indoor benchmarks show that H^2A^2 delivers consistent gains over strong baselines and improves cross-scene generalization.