pwc-archive/papers-with-abstracts huggingface dataset:
WriteViT: Handwritten Text Generation with Vision Transformer
Authors: Dang Hoai Nam, Huynh Tong Dang Khoa, Vo Nguyen Le Duy
Date: 2025-05-19 00:00:00
Tasks: Handwriting generation, Text Generation

Humans can quickly generalize handwriting styles from a single example by intuitively separating content from style. Machines, however, struggle with this task, especially in low-data settings, often missing subtle spatial and stylistic cues. Motivated by this gap, we introduce WriteViT, a one-shot handwritten text synthesis framework that incorporates Vision Transformers (ViT), a family of models that have shown strong performance across various computer vision tasks. WriteViT integrates a ViT-based Writer Identifier for extracting style embeddings, a multi-scale generator built with Transformer encoder-decoder blocks enhanced by conditional positional encoding (CPE), and a lightweight ViT-based recognizer. While previous methods typically rely on CNNs or CRNNs, our design leverages transformers in key components to better capture both fine-grained stroke details and higher-level style information. Although handwritten text synthesis has been widely explored, its application to Vietnamese -- a language rich in diacritics and complex typography -- remains limited. Experiments on Vietnamese and English datasets demonstrate that WriteViT produces high-quality, style-consistent handwriting while maintaining strong recognition performance in low-resource scenarios. These results highlight the promise of transformer-based designs for multilingual handwriting generation and efficient style adaptation.
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Authors: Lincan Cai, Jingxuan Kang, Shuang Li, Wenxuan Ma, Binhui Xie, Zhida Qin, Jian Liang
Date: 2025-05-19 00:00:00
Tasks: feature selection, Out-of-Distribution Generalization, Test-time Adaptation, zero-shot-classification, Zero-Shot Learning

Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmentation techniques, like random cropping, in alignment with fine-grained class descriptions generated by large language models (LLMs), significantly enhancing zero-shot performance by incorporating multi-view information. However, the inherent randomness of these augmentations can inevitably introduce background artifacts and cause models to overly focus on local details, compromising global semantic understanding. To address these issues, we propose an \textbf{A}ttention-\textbf{B}ased \textbf{S}election (\textbf{ABS}) method from local details to global context, which applies attention-guided cropping in both raw images and feature space, supplement global semantic information through strategic feature selection. Additionally, we introduce a soft matching technique to effectively filter LLM descriptions for better alignment. \textbf{ABS} achieves state-of-the-art performance on out-of-distribution generalization and zero-shot classification tasks. Notably, \textbf{ABS} is training-free and even rivals few-shot and test-time adaptation methods. Our code is available at \href{https://github.com/BIT-DA/ABS}{\textcolor{darkgreen}{https://github.com/BIT-DA/ABS}}.
Automatic Complementary Separation Pruning Toward Lightweight CNNs
Authors: David Levin, Gonen Singer
Date: 2025-05-19 00:00:00

In this paper, we present Automatic Complementary Separation Pruning (ACSP), a novel and fully automated pruning method for convolutional neural networks. ACSP integrates the strengths of both structured pruning and activation-based pruning, enabling the efficient removal of entire components such as neurons and channels while leveraging activations to identify and retain the most relevant components. Our approach is designed specifically for supervised learning tasks, where we construct a graph space that encodes the separation capabilities of each component with respect to all class pairs. By employing complementary selection principles and utilizing a clustering algorithm, ACSP ensures that the selected components maintain diverse and complementary separation capabilities, reducing redundancy and maintaining high network performance. The method automatically determines the optimal subset of components in each layer, utilizing a knee-finding algorithm to select the minimal subset that preserves performance without requiring user-defined pruning volumes. Extensive experiments on multiple architectures, including VGG-16, ResNet-50, and MobileNet-V2, across datasets like CIFAR-10, CIFAR-100, and ImageNet-1K, demonstrate that ACSP achieves competitive accuracy compared to other methods while significantly reducing computational costs. This fully automated approach not only enhances scalability but also makes ACSP especially practical for real-world deployment by eliminating the need for manually defining the pruning volume.
Swin DiT: Diffusion Transformer using Pseudo Shifted Windows
Authors: Jiafu Wu, Yabiao Wang, Jian Li, Jinlong Peng, Yun Cao, Chengjie Wang, Jiangning Zhang
Date: 2025-05-19 00:00:00
Tasks: Image Generation

Diffusion Transformers (DiTs) achieve remarkable performance within the domain of image generation through the incorporation of the transformer architecture. Conventionally, DiTs are constructed by stacking serial isotropic global information modeling transformers, which face significant computational cost when processing high-resolution images. We empirically analyze that latent space image generation does not exhibit a strong dependence on global information as traditionally assumed. Most of the layers in the model demonstrate redundancy in global computation. In addition, conventional attention mechanisms exhibit low-frequency inertia issues. To address these issues, we propose \textbf{P}seudo \textbf{S}hifted \textbf{W}indow \textbf{A}ttention (PSWA), which fundamentally mitigates global model redundancy. PSWA achieves intermediate global-local information interaction through window attention, while employing a high-frequency bridging branch to simulate shifted window operations, supplementing appropriate global and high-frequency information. Furthermore, we propose the Progressive Coverage Channel Allocation(PCCA) strategy that captures high-order attention similarity without additional computational cost. Building upon all of them, we propose a series of Pseudo \textbf{S}hifted \textbf{Win}dow DiTs (\textbf{Swin DiT}), accompanied by extensive experiments demonstrating their superior performance. For example, our proposed Swin-DiT-L achieves a 54%$\uparrow$ FID improvement over DiT-XL/2 while requiring less computational. https://github.com/wujiafu007/Swin-DiT
Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
Authors: Seungjun Oh, Younggeun Lee, Hyejin Jeon, Eunbyung Park
Date: 2025-05-19 00:00:00
Tasks: 3D Scene Reconstruction, Computational Efficiency, Novel View Synthesis

Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an appealing approach due to its ability to model high-fidelity spatial and temporal variations. However, existing methods suffer from substantial computational and memory overhead due to the redundant allocation of 4D Gaussians to static regions, which can also degrade image quality. In this work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework that adaptively represents static regions with 3D Gaussians while reserving 4D Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian representation and iteratively converts temporally invariant Gaussians into 3D, significantly reducing the number of parameters and improving computational efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation, capturing complex motions with high fidelity. Our approach achieves significantly faster training times compared to baseline 4D Gaussian Splatting methods while maintaining or improving the visual quality.
RB-SCD: A New Benchmark for Semantic Change Detection of Roads and Bridges in Traffic Scenes
Authors: Qingling Shu, Sibao Chen, Zhihui You, Wei Lu, Jin Tang, Bin Luo
Date: 2025-05-19 00:00:00
Tasks: Change Detection

Accurate detection of changes in roads and bridges, such as construction, renovation, and demolition, is essential for urban planning and traffic management. However, existing methods often struggle to extract fine-grained semantic change information due to the lack of high-quality annotated datasets in traffic scenarios. To address this, we introduce the Road and Bridge Semantic Change Detection (RB-SCD) dataset, a comprehensive benchmark comprising 260 pairs of high-resolution remote sensing images from diverse cities and countries. RB-SCD captures 11 types of semantic changes across varied road and bridge structures, enabling detailed structural and functional analysis. Building on this dataset, we propose a novel framework, Multimodal Frequency-Driven Change Detector (MFDCD), which integrates multimodal features in the frequency domain. MFDCD includes a Dynamic Frequency Coupler (DFC) that fuses hierarchical visual features with wavelet-based frequency components, and a Textual Frequency Filter (TFF) that transforms CLIP-derived textual features into the frequency domain and applies graph-based filtering. Experimental results on RB-SCD and three public benchmarks demonstrate the effectiveness of our approach.
FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching
Authors: Alp Eren Sari, Paolo Favaro
Date: 2025-05-19 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation, Video Instance Segmentation, Video Segmentation, Video Semantic Segmentation

We propose FlowCut, a simple and capable method for unsupervised video instance segmentation consisting of a three-stage framework to construct a high-quality video dataset with pseudo labels. To our knowledge, our work is the first attempt to curate a video dataset with pseudo-labels for unsupervised video instance segmentation. In the first stage, we generate pseudo-instance masks by exploiting the affinities of features from both images and optical flows. In the second stage, we construct short video segments containing high-quality, consistent pseudo-instance masks by temporally matching them across the frames. In the third stage, we use the YouTubeVIS-2021 video dataset to extract our training instance segmentation set, and then train a video segmentation model. FlowCut achieves state-of-the-art performance on the YouTubeVIS-2019, YouTubeVIS-2021, DAVIS-2017, and DAVIS-2017 Motion benchmarks.
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model
Authors: Jonas Brenig, Radu Timofte
Date: 2025-05-19 00:00:00
Tasks: Decoder, Denoising, Image Compression, Image Generation, Video Compression

Denoising diffusion models achieved impressive results on several image generation tasks often outperforming GAN based models. Recently, the generative capabilities of diffusion models have been employed for perceptual image compression, such as in CDC. A major drawback of these diffusion-based methods is that, while producing impressive perceptual quality images they are dropping in fidelity/increasing the distortion to the original uncompressed images when compared with other traditional or learned image compression schemes aiming for fidelity. In this paper, we propose a hybrid compression scheme optimized for perceptual quality, extending the approach of the CDC model with a decoder network in order to reduce the impact on distortion metrics such as PSNR. After using the decoder network to generate an initial image, optimized for distortion, the latent conditioned diffusion model refines the reconstruction for perceptual quality by predicting the residual. On standard benchmarks, we achieve up to +2dB PSNR fidelity improvements while maintaining comparable LPIPS and FID perceptual scores when compared with CDC. Additionally, the approach is easily extensible to video compression, where we achieve similar results.
CacheFlow: Fast Human Motion Prediction by Cached Normalizing Flow
Authors: Takahiro Maeda, Jinkun Cao, Norimichi Ukita, Kris Kitani
Date: 2025-05-19 00:00:00
Tasks: Density Estimation, Human motion prediction, motion prediction, Prediction

Many density estimation techniques for 3D human motion prediction require a significant amount of inference time, often exceeding the duration of the predicted time horizon. To address the need for faster density estimation for 3D human motion prediction, we introduce a novel flow-based method for human motion prediction called CacheFlow. Unlike previous conditional generative models that suffer from time efficiency, CacheFlow takes advantage of an unconditional flow-based generative model that transforms a Gaussian mixture into the density of future motions. The results of the computation of the flow-based generative model can be precomputed and cached. Then, for conditional prediction, we seek a mapping from historical trajectories to samples in the Gaussian mixture. This mapping can be done by a much more lightweight model, thus saving significant computation overhead compared to a typical conditional flow model. In such a two-stage fashion and by caching results from the slow flow model computation, we build our CacheFlow without loss of prediction accuracy and model expressiveness. This inference process is completed in approximately one millisecond, making it 4 times faster than previous VAE methods and 30 times faster than previous diffusion-based methods on standard benchmarks such as Human3.6M and AMASS datasets. Furthermore, our method demonstrates improved density estimation accuracy and comparable prediction accuracy to a SOTA method on Human3.6M. Our code and models will be publicly available.
Learning to Adapt to Position Bias in Vision Transformer Classifiers
Authors: Robert-Jan Bruintjes, Jan van Gemert
Date: 2025-05-19 00:00:00
Tasks: image-classification, Image Classification, Position

How discriminative position information is for image classification depends on the data. On the one hand, the camera position is arbitrary and objects can appear anywhere in the image, arguing for translation invariance. At the same time, position information is key for exploiting capture/center bias, and scene layout, e.g.: the sky is up. We show that position bias, the level to which a dataset is more easily solved when positional information on input features is used, plays a crucial role in the performance of Vision Transformers image classifiers. To investigate, we propose Position-SHAP, a direct measure of position bias by extending SHAP to work with position embeddings. We show various levels of position bias in different datasets, and find that the optimal choice of position embedding depends on the position bias apparent in the dataset. We therefore propose Auto-PE, a single-parameter position embedding extension, which allows the position embedding to modulate its norm, enabling the unlearning of position information. Auto-PE combines with existing PEs to match or improve accuracy on classification datasets.
Industrial Synthetic Segment Pre-training
Authors: Shinichi Mae, Ryousuke Yamada, Hirokatsu Kataoka
Date: 2025-05-19 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Pre-training on real-image datasets has been widely proven effective for improving instance segmentation. However, industrial applications face two key challenges: (1) legal and ethical restrictions, such as ImageNet's prohibition of commercial use, and (2) limited transferability due to the domain gap between web images and industrial imagery. Even recent vision foundation models, including the segment anything model (SAM), show notable performance degradation in industrial settings. These challenges raise critical questions: Can we build a vision foundation model for industrial applications without relying on real images or manual annotations? And can such models outperform even fine-tuned SAM on industrial datasets? To address these questions, we propose the Instance Core Segmentation Dataset (InsCore), a synthetic pre-training dataset based on formula-driven supervised learning (FDSL). InsCore generates fully annotated instance segmentation images that reflect key characteristics of industrial data, including complex occlusions, dense hierarchical masks, and diverse non-rigid shapes, distinct from typical web imagery. Unlike previous methods, InsCore requires neither real images nor human annotations. Experiments on five industrial datasets show that models pre-trained with InsCore outperform those trained on COCO and ImageNet-21k, as well as fine-tuned SAM, achieving an average improvement of 6.2 points in instance segmentation performance. This result is achieved using only 100k synthetic images, more than 100 times fewer than the 11 million images in SAM's SA-1B dataset, demonstrating the data efficiency of our approach. These findings position InsCore as a practical and license-free vision foundation model for industrial applications.
Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction
Authors: Yuanbo Wang, Zhaoxuan Zhang, Jiajin Qiu, Dilong Sun, Zhengyu Meng, Xiaopeng Wei, Xin Yang
Date: 2025-05-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Generation

Diffusion models have made breakthroughs in 3D generation tasks. Current 3D diffusion models focus on reconstructing target shape from images or a set of partial observations. While excelling in global context understanding, they struggle to capture the local details of complex shapes and limited to the occlusion and lighting conditions. To overcome these limitations, we utilize tactile images to capture the local 3D information and propose a Touch2Shape model, which leverages a touch-conditioned diffusion model to explore and reconstruct the target shape from touch. For shape reconstruction, we have developed a touch embedding module to condition the diffusion model in creating a compact representation and a touch shape fusion module to refine the reconstructed shape. For shape exploration, we combine the diffusion model with reinforcement learning to train a policy. This involves using the generated latent vector from the diffusion model to guide the touch exploration policy training through a novel reward design. Experiments validate the reconstruction quality thorough both qualitatively and quantitative analysis, and our touch exploration policy further boosts reconstruction performance.
Cross-modal feature fusion for robust point cloud registration with ambiguous geometry
Authors: Zhaoyi Wang, Shengyu Huang, Jemil Avers Butt, Yuanzhou Cai, Matej Varga, Andreas Wieser
Date: 2025-05-19 00:00:00
Tasks: Point Cloud Registration

Point cloud registration has seen significant advancements with the application of deep learning techniques. However, existing approaches often overlook the potential of integrating radiometric information from RGB images. This limitation reduces their effectiveness in aligning point clouds pairs, especially in regions where geometric data alone is insufficient. When used effectively, radiometric information can enhance the registration process by providing context that is missing from purely geometric data. In this paper, we propose CoFF, a novel Cross-modal Feature Fusion method that utilizes both point cloud geometry and RGB images for pairwise point cloud registration. Assuming that the co-registration between point clouds and RGB images is available, CoFF explicitly addresses the challenges where geometric information alone is unclear, such as in regions with symmetric similarity or planar structures, through a two-stage fusion of 3D point cloud features and 2D image features. It incorporates a cross-modal feature fusion module that assigns pixel-wise image features to 3D input point clouds to enhance learned 3D point features, and integrates patch-wise image features with superpoint features to improve the quality of coarse matching. This is followed by a coarse-to-fine matching module that accurately establishes correspondences using the fused features. We extensively evaluate CoFF on four common datasets: 3DMatch, 3DLoMatch, IndoorLRS, and the recently released ScanNet++ datasets. In addition, we assess CoFF on specific subset datasets containing geometrically ambiguous cases. Our experimental results demonstrate that CoFF achieves state-of-the-art registration performance across all benchmarks, including remarkable registration recalls of 95.9% and 81.6% on the widely-used 3DMatch and 3DLoMatch datasets, respectively...(Truncated to fit arXiv abstract length)
Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning
Authors: Xiaoyu Yang, Jie Lu, En Yu
Date: 2025-05-19 00:00:00
Tasks: counterfactual, Counterfactual Reasoning

This paper uncovers a critical yet overlooked phenomenon in multi-modal large language models (MLLMs): detrimental concept drift within chain-of-thought (CoT) reasoning during non-stationary reinforcement fine-tuning (RFT), where reasoning token distributions evolve unpredictably, thereby introducing significant biases in final predictions. To address this, we are pioneers in establishing the theoretical bridge between concept drift theory and RFT processes by formalizing CoT's autoregressive token streams as non-stationary distributions undergoing arbitrary temporal shifts. Leveraging this framework, we propose a novel counterfact-aware RFT that systematically decouples beneficial distribution adaptation from harmful concept drift through concept graph-empowered LLM experts generating counterfactual reasoning trajectories. Our solution, Counterfactual Preference Optimization (CPO), enables stable RFT in non-stationary environments, particularly within the medical domain, through custom-tuning of counterfactual-aware preference alignment. Extensive experiments demonstrate our superior performance of robustness, generalization and coordination within RFT. Besides, we also contributed a large-scale dataset CXR-CounterFact (CCF), comprising 320,416 meticulously curated counterfactual reasoning trajectories derived from MIMIC-CXR. Our code and data are public.
3D Visual Illusion Depth Estimation
Authors: Chengtang Yao, Zhidan Liu, Jiaxi Zeng, Lidong Yu, Yuwei Wu, Yunde Jia
Date: 2025-05-19 00:00:00
Tasks: Common Sense Reasoning, Depth Estimation, Language Modeling, Language Modelling

3D visual illusion is a perceptual phenomenon where a two-dimensional plane is manipulated to simulate three-dimensional spatial relationships, making a flat artwork or object look three-dimensional in the human visual system. In this paper, we reveal that the machine visual system is also seriously fooled by 3D visual illusions, including monocular and binocular depth estimation. In order to explore and analyze the impact of 3D visual illusion on depth estimation, we collect a large dataset containing almost 3k scenes and 200k images to train and evaluate SOTA monocular and binocular depth estimation methods. We also propose a robust depth estimation framework that uses common sense from a vision-language model to adaptively select reliable depth from binocular disparity and monocular depth. Experiments show that SOTA monocular, binocular, and multi-view depth estimation approaches are all fooled by various 3D visual illusions, while our method achieves SOTA performance.