pwc-archive/papers-with-abstracts huggingface dataset:
SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer
Authors: Hongda Liu, Longguang Wang, Ye Zhang, Ziru Yu, Yulan Guo
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Decoder, Mamba, Style Transfer

Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transformers) suffer huge computational complexity to achieve global receptive fields. Recently, the State Space Model (SSM), especially the improved variant Mamba, has shown great potential for long-range dependency modeling with linear complexity, which offers a approach to resolve the above dilemma. In this paper, we develop a Mamba-based style transfer framework, termed SaMam. Specifically, a mamba encoder is designed to efficiently extract content and style information. In addition, a style-aware mamba decoder is developed to flexibly adapt to various styles. Moreover, to address the problems of local pixel forgetting, channel redundancy and spatial discontinuity of existing SSMs, we introduce both local enhancement and zigzag scan. Qualitative and quantitative results demonstrate that our SaMam outperforms state-of-the-art methods in terms of both accuracy and efficiency.
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report Generation
Authors: Yaxiong Chen, Chuang Du, Chunlei Li, Jingliang Hu, Yilei Shi, Shengwu Xiong, Xiao Xiang Zhu, Lichao Mou
Date: 2025-03-20 00:00:00
Tasks: Image Captioning, Transfer Learning

Automated radiology report generation aims to expedite the tedious and error-prone reporting process for radiologists. While recent works have made progress, learning to align medical images and textual findings remains challenging due to the relative scarcity of labeled medical data. For example, datasets for this task are much smaller than those used for image captioning in computer vision. In this work, we propose to transfer representations from CLIP, a large-scale pre-trained vision-language model, to better capture cross-modal semantics between images and texts. However, directly applying CLIP is suboptimal due to the domain gap between natural images and radiology. To enable efficient adaptation, we introduce UniCrossAdapter, lightweight adapter modules that are incorporated into CLIP and fine-tuned on the target task while keeping base parameters fixed. The adapters are distributed across modalities and their interaction to enhance vision-language alignment. Experiments on two public datasets demonstrate the effectiveness of our approach, advancing state-of-the-art in radiology report generation. The proposed transfer learning framework provides a means of harnessing semantic knowledge from large-scale pre-trained models to tackle data-scarce medical vision-language tasks. Code is available at https://github.com/chauncey-tow/MRG-CLIP.
CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention
Authors: Yaxiong Chen, Minghong Wei, Zixuan Zheng, Jingliang Hu, Yilei Shi, Shengwu Xiong, Xiao Xiang Zhu, Lichao Mou
Date: 2025-03-20 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Referring medical image segmentation targets delineating lesions indicated by textual descriptions. Aligning visual and textual cues is challenging due to their distinct data properties. Inspired by large-scale pre-trained vision-language models, we propose CausalCLIPSeg, an end-to-end framework for referring medical image segmentation that leverages CLIP. Despite not being trained on medical data, we enforce CLIP's rich semantic space onto the medical domain by a tailored cross-modal decoding method to achieve text-to-pixel alignment. Furthermore, to mitigate confounding bias that may cause the model to learn spurious correlations instead of meaningful causal relationships, CausalCLIPSeg introduces a causal intervention module which self-annotates confounders and excavates causal features from inputs for segmentation judgments. We also devise an adversarial min-max game to optimize causal features while penalizing confounding ones. Extensive experiments demonstrate the state-of-the-art performance of our proposed method. Code is available at https://github.com/WUTCM-Lab/CausalCLIPSeg.
V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
Authors: JunGyu Lee, Kunyoung Lee, Haesol Park, Ig-Jae Kim, Gi Pyo Nam
Date: 2025-03-20 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial Expression Recognition (FER) plays a crucial role in human affective analysis and has been widely applied in computer vision tasks such as human-computer interaction and psychological assessment. The 8th Affective Behavior Analysis in-the-Wild (ABAW) Challenge aims to assess human emotions using the video-based Aff-Wild2 dataset. This challenge includes various tasks, including the video-based EXPR recognition track, which is our primary focus. In this paper, we demonstrate that addressing label ambiguity and class imbalance, which are known to cause performance degradation, can lead to meaningful performance improvements. Specifically, we propose Video-based Noise-aware Adaptive Weighting (V-NAW), which adaptively assigns importance to each frame in a clip to address label ambiguity and effectively capture temporal variations in facial expressions. Furthermore, we introduce a simple and effective augmentation strategy to reduce redundancy between consecutive frames, which is a primary cause of overfitting. Through extensive experiments, we validate the effectiveness of our approach, demonstrating significant improvements in video-based FER performance.
STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding
Authors: Zichen Liu, Kunlun Xu, Bing Su, Xu Zou, Yuxin Peng, Jiahuan Zhou
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Video Understanding, Zero-shot Generalization

Pre-trained on tremendous image-text pairs, vision-language models like CLIP have demonstrated promising zero-shot generalization across numerous image-based tasks. However, extending these capabilities to video tasks remains challenging due to limited labeled video data and high training costs. Recent video prompting methods attempt to adapt CLIP for video tasks by introducing learnable prompts, but they typically rely on a single static prompt for all video sequences, overlooking the diverse temporal dynamics and spatial variations that exist across frames. This limitation significantly hinders the model's ability to capture essential temporal information for effective video understanding. To address this, we propose an integrated Spatial-TempOral dynamic Prompting (STOP) model which consists of two complementary modules, the intra-frame spatial prompting and inter-frame temporal prompting. Our intra-frame spatial prompts are designed to adaptively highlight discriminative regions within each frame by leveraging intra-frame attention and temporal variation, allowing the model to focus on areas with substantial temporal dynamics and capture fine-grained spatial details. Additionally, to highlight the varying importance of frames for video understanding, we further introduce inter-frame temporal prompts, dynamically inserting prompts between frames with high temporal variance as measured by frame similarity. This enables the model to prioritize key frames and enhances its capacity to understand temporal dependencies across sequences. Extensive experiments on various video benchmarks demonstrate that STOP consistently achieves superior performance against state-of-the-art methods. The code is available at https://github.com/zhoujiahuan1991/CVPR2025-STOP.
Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation
Authors: Kendong Liu, Zhiyu Zhu, Hui Liu, Junhui Hou
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Computational Efficiency, Image to 3D

We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images. To derive high-quality reconstructions through few-step inferences, we emphasize the critical issue of regularizing the learning of score function in states of random noise. To this end, we propose edge consistency, i.e., consistent predictions across the high signal-to-noise ratio region, to enhance a pre-trained diffusion model, enabling a distillation-based refinement of the endpoint score function. Building on those distilled diffusion models, we propose an adversarial augmentation strategy to further enrich the generation detail and boost overall generation quality. The two modules complement each other, mutually reinforcing to elevate generative performance. Extensive experiments demonstrate that our Acc3D not only achieves over a $20\times$ increase in computational efficiency but also yields notable quality improvements, compared to the state-of-the-arts.
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli
Authors: Pengyu Liu, Guohua Dong, Dan Guo, Kun Li, Fengling Li, Xun Yang, Meng Wang, Xiaomin Ying
Date: 2025-03-20 00:00:00
Tasks: Brain Decoding, Image Generation, Survey

In daily life, we encounter diverse external stimuli, such as images, sounds, and videos. As research in multimodal stimuli and neuroscience advances, fMRI-based brain decoding has become a key tool for understanding brain perception and its complex cognitive processes. Decoding brain signals to reconstruct stimuli not only reveals intricate neural mechanisms but also drives progress in AI, disease treatment, and brain-computer interfaces. Recent advancements in neuroimaging and image generation models have significantly improved fMRI-based decoding. While fMRI offers high spatial resolution for precise brain activity mapping, its low temporal resolution and signal noise pose challenges. Meanwhile, techniques like GANs, VAEs, and Diffusion Models have enhanced reconstructed image quality, and multimodal pre-trained models have boosted cross-modal decoding tasks. This survey systematically reviews recent progress in fMRI-based brain decoding, focusing on stimulus reconstruction from passive brain signals. It summarizes datasets, relevant brain regions, and categorizes existing methods by model structure. Additionally, it evaluates model performance and discusses their effectiveness. Finally, it identifies key challenges and proposes future research directions, offering valuable insights for the field. For more information and resources related to this survey, visit https://github.com/LpyNow/BrainDecodingImage.
Automating 3D Dataset Generation with Neural Radiance Fields
Authors: P. Schulz, T. Hempel, A. Al-Hamadi
Date: 2025-03-20 00:00:00
Tasks: 3D Pose Estimation, Dataset Generation, Image Retrieval, Pose Estimation

3D detection is a critical task to understand spatial characteristics of the environment and is used in a variety of applications including robotics, augmented reality, and image retrieval. Training performant detection models require diverse, precisely annotated, and large scale datasets that involve complex and expensive creation processes. Hence, there are only few public 3D datasets that are additionally limited in their range of classes. In this work, we propose a pipeline for automatic generation of 3D datasets for arbitrary objects. By utilizing the universal 3D representation and rendering capabilities of Radiance Fields, our pipeline generates high quality 3D models for arbitrary objects. These 3D models serve as input for a synthetic dataset generator. Our pipeline is fast, easy to use and has a high degree of automation. Our experiments demonstrate, that 3D pose estimation networks, trained with our generated datasets, archive strong performance in typical application scenarios.
SenseExpo: Efficient Autonomous Exploration with Prediction Information from Lightweight Neural Networks
Authors: Haojia Gao, Haohua Que, Hoiian Au, Weihao Shan, Mingkai Liu, Yusen Qin, Lei Mu, Rong Zhao, Xinghua Yang, Qi Wei, Fei Qiao
Date: 2025-03-20 00:00:00
Tasks: SSIM

This paper proposes SenseExpo, an efficient autonomous exploration framework based on a lightweight prediction network, which addresses the limitations of traditional methods in computational overhead and environmental generalization. By integrating Generative Adversarial Networks (GANs), Transformer, and Fast Fourier Convolution (FFC), we designed a lightweight prediction model with merely 709k parameters. Our smallest model achieves better performance on the KTH dataset than U-net (24.5M) and LaMa (51M), delivering PSNR 9.026 and SSIM 0.718, particularly representing a 38.7% PSNR improvement over the 51M-parameter LaMa model. Cross-domain testing demonstrates its strong generalization capability, with an FID score of 161.55 on the HouseExpo dataset, significantly outperforming comparable methods. Regarding exploration efficiency, on the KTH dataset,SenseExpo demonstrates approximately a 67.9% time reduction in exploration time compared to MapEx. On the MRPB 1.0 dataset, SenseExpo achieves 77.1% time reduction roughly compared to MapEx. Deployed as a plug-and-play ROS node, the framework seamlessly integrates with existing navigation systems, providing an efficient solution for resource-constrained devices.
Agentic Keyframe Search for Video Question Answering
Authors: Sunqi Fan, Meng-Hao Guo, Shuojin Yang
Date: 2025-03-20 00:00:00
Tasks: EgoSchema, Question Answering, Video Question Answering, Video Understanding, Visual Reasoning, Zero-Shot Video Question Answer

Video question answering (VideoQA) enables machines to extract and comprehend key information from videos through natural language interaction, which is a critical step towards achieving intelligence. However, the demand for a thorough understanding of videos and high computational costs still limit the widespread applications of VideoQA. To address it, we propose Agentic Keyframe Search (AKeyS), a simple yet powerful algorithm for identifying keyframes in the VideoQA task. It can effectively distinguish key information from redundant, irrelevant content by leveraging modern language agents to direct classical search algorithms. Specifically, we first segment the video and organize it as a tree structure. Then, AKeyS uses a language agent to estimate heuristics and movement costs while dynamically expanding nodes. Finally, the agent determines if sufficient keyframes have been collected based on termination conditions and provides answers. Extensive experiments on the EgoSchema and NExT-QA datasets show that AKeyS outperforms all previous methods with the highest keyframe searching efficiency, which means it can accurately identify key information and conduct effective visual reasoning with minimal computational overhead. For example, on the EgoSchema subset, it achieves 1.8% higher accuracy while processing only 43.5% of the frames compared to VideoTree. We believe that AKeyS represents a significant step towards building intelligent agents for video understanding. The code is publicly available at https://github.com/fansunqi/AKeyS.
Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation
Authors: Andrei Jelea, Ahmed Nabil Belbachir, Marius Leordeanu
Date: 2025-03-20 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Solving fish segmentation in underwater videos, a real-world problem of great practical value in marine and aquaculture industry, is a challenging task due to the difficulty of the filming environment, poor visibility and limited existing annotated underwater fish data. In order to overcome these obstacles, we introduce a novel two stage unsupervised segmentation approach that requires no human annotations and combines artificially created and real images. Our method generates challenging synthetic training data, by placing virtual fish in real-world underwater habitats, after performing fish transformations such as Thin Plate Spline shape warping and color Histogram Matching, which realistically integrate synthetic fish into the backgrounds, making the generated images increasingly closer to the real world data with every stage of our approach. While we validate our unsupervised method on the popular DeepFish dataset, obtaining a performance close to a fully-supervised SoTA model, we further show its effectiveness on the specific case of salmon segmentation in underwater videos, for which we introduce DeepSalmon, the largest dataset of its kind in the literature (30 GB). Moreover, on both datasets we prove the capability of our approach to boost the performance of the fully-supervised SoTA model.
Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution
Authors: Wanshu Fan, Yue Wang, Cong Wang, Yunzhe Zhang, Wei Wang, Dongsheng Zhou
Date: 2025-03-20 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

Single-Image Super-Resolution (SISR) plays a pivotal role in enhancing the accuracy and reliability of measurement systems, which are integral to various vision-based instrumentation and measurement applications. These systems often require clear and detailed images for precise object detection and recognition. However, images captured by visual measurement tools frequently suffer from degradation, including blurring and loss of detail, which can impede measurement accuracy.As a potential remedy, we in this paper propose a Semantic-Guided Global-Local Collaborative Network (SGGLC-Net) for lightweight SISR. Our SGGLC-Net leverages semantic priors extracted from a pre-trained model to guide the super-resolution process, enhancing image detail quality effectively. Specifically,we propose a Semantic Guidance Module that seamlessly integrates the semantic priors into the super-resolution network, enabling the network to more adeptly capture and utilize semantic priors, thereby enhancing image details. To further explore both local and non-local interactions for improved detail rendition,we propose a Global-Local Collaborative Module, which features three Global and Local Detail Enhancement Modules, as well as a Hybrid Attention Mechanism to work together to efficiently learn more useful features. Our extensive experiments show that SGGLC-Net achieves competitive PSNR and SSIM values across multiple benchmark datasets, demonstrating higher performance with the multi-adds reduction of 12.81G compared to state-of-the-art lightweight super-resolution approaches. These improvements underscore the potential of our approach to enhance the precision and effectiveness of visual measurement systems. Codes are at https://github.com/fanamber831/SGGLC-Net.
Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detection
Authors: Xu He, Zhen Huang, Qingsong Yao, Xiaoqian Zhou, S. Kevin Zhou
Date: 2025-03-20 00:00:00
Tasks: Age Estimation

Landmark detection plays a crucial role in medical imaging applications such as disease diagnosis, bone age estimation, and therapy planning. However, training models for detecting multiple landmarks simultaneously often encounters the "seesaw phenomenon", where improvements in detecting certain landmarks lead to declines in detecting others. Yet, training a separate model for each landmark increases memory usage and computational overhead. To address these challenges, we propose a novel approach based on the belief that "landmarks are distinct" by training models with pseudo-labels and template data updated continuously during the training process, where each model is dedicated to detecting a single landmark to achieve high accuracy. Furthermore, grounded on the belief that "landmarks are also alike", we introduce an adapter-based fusion model, combining shared weights with landmark-specific weights, to efficiently share model parameters while allowing flexible adaptation to individual landmarks. This approach not only significantly reduces memory and computational resource requirements but also effectively mitigates the seesaw phenomenon in multi-landmark training. Experimental results on publicly available medical image datasets demonstrate that the single-landmark models significantly outperform traditional multi-point joint training models in detecting individual landmarks. Although our adapter-based fusion model shows slightly lower performance compared to the combined results of all single-landmark models, it still surpasses the current state-of-the-art methods while achieving a notable improvement in resource efficiency.
Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model
Authors: Yingmao Miao, Zhanpeng Huang, Rui Han, Zibin Wang, Chenhao Lin, Chao Shen
Date: 2025-03-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Virtual Try-on

While virtual try-on for clothes and shoes with diffusion models has gained attraction, virtual try-on for ornaments, such as bracelets, rings, earrings, and necklaces, remains largely unexplored. Due to the intricate tiny patterns and repeated geometric sub-structures in most ornaments, it is much more difficult to guarantee identity and appearance consistency under large pose and scale variances between ornaments and models. This paper proposes the task of virtual try-on for ornaments and presents a method to improve the geometric and appearance preservation of ornament virtual try-ons. Specifically, we estimate an accurate wearing mask to improve the alignments between ornaments and models in an iterative scheme alongside the denoising process. To preserve structure details, we further regularize attention layers to map the reference ornament mask to the wearing mask in an implicit way. Experimental results demonstrate that our method successfully wears ornaments from reference images onto target models, handling substantial differences in scale and pose while preserving identity and achieving realistic visual effects.
Bokehlicious: Photorealistic Bokeh Rendering with Controllable Apertures
Authors: Tim Seizinger, Florin-Alexandru Vasluianu, Marcos V. Conde, Radu Timofte
Date: 2025-03-20 00:00:00
Tasks: Deblurring, Zero-shot Generalization

Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh with variable strength remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction due to reliance on synthetic data. In this work, we propose Bokehlicious, a highly efficient network that provides intuitive control over Bokeh strength through an Aperture-Aware Attention mechanism, mimicking the physical lens aperture. To further address the lack of high-quality real-world data, we present RealBokeh, a novel dataset featuring 23,000 high-resolution (24-MP) images captured by professional photographers, covering diverse scenes with varied aperture and focal length settings. Evaluations on both our new RealBokeh and established Bokeh rendering benchmarks show that Bokehlicious consistently outperforms SOTA methods while significantly reducing computational cost and exhibiting strong zero-shot generalization. Our method and dataset further extend to defocus deblurring, achieving competitive results on the RealDOF benchmark. Our code and data can be found at https://github.com/TimSeizinger/Bokehlicious