pwc-archive/papers-with-abstracts huggingface dataset:
Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets
Authors: Ruochen Gao, Donghang Lyu, Marius Staring
Date: 2024-09-11 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Medical imaging is essential for the diagnosis and treatment of diseases, with medical image segmentation as a subtask receiving high attention. However, automatic medical image segmentation models are typically task-specific and struggle to handle multiple scenarios, such as different imaging modalities and regions of interest. With the introduction of the Segment Anything Model (SAM), training a universal model for various clinical scenarios has become feasible. Recently, several Medical SAM (MedSAM) methods have been proposed, but these models often rely on heavy image encoders to achieve high performance, which may not be practical for real-world applications due to their high computational demands and slow inference speed. To address this issue, a lightweight version of the MedSAM (LiteMedSAM) can provide a viable solution, achieving high performance while requiring fewer resources and less time. In this work, we introduce Swin-LiteMedSAM, a new variant of LiteMedSAM. This model integrates the tiny Swin Transformer as the image encoder, incorporates multiple types of prompts, including box-based points and scribble generated from a given bounding box, and establishes skip connections between the image encoder and the mask decoder. In the \textit{Segment Anything in Medical Images on Laptop} challenge (CVPR 2024), our approach strikes a good balance between segmentation performance and speed, demonstrating significantly improved overall results across multiple modalities compared to the LiteMedSAM baseline provided by the challenge organizers. Our proposed model achieved a DSC score of \textbf{0.8678} and an NSD score of \textbf{0.8844} on the validation set. On the final test set, it attained a DSC score of \textbf{0.8193} and an NSD score of \textbf{0.8461}, securing fourth place in the challenge.
AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distribution
Authors: Wangduo Xie, Richard Schoonhoven, Tristan van Leeuwen, Matthew B. Blaschko
Date: 2024-09-11 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Medical Diagnosis, Semantic Segmentation

Computed tomography (CT) reconstruction plays a crucial role in industrial nondestructive testing and medical diagnosis. Sparse view CT reconstruction aims to reconstruct high-quality CT images while only using a small number of projections, which helps to improve the detection speed of industrial assembly lines and is also meaningful for reducing radiation in medical scenarios. Sparse CT reconstruction methods based on implicit neural representations (INRs) have recently shown promising performance, but still produce artifacts because of the difficulty of obtaining useful prior information. In this work, we incorporate a powerful prior: the total number of material categories of objects. To utilize the prior, we design AC-IND, a self-supervised method based on Attenuation Coefficient Estimation and Implicit Neural Distribution. Specifically, our method first transforms the traditional INR from scalar mapping to probability distribution mapping. Then we design a compact attenuation coefficient estimator initialized with values from a rough reconstruction and fast segmentation. Finally, our algorithm finishes the CT reconstruction by jointly optimizing the estimator and the generated distribution. Through experiments, we find that our method not only outperforms the comparative methods in sparse CT reconstruction but also can automatically generate semantic segmentation maps.
Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models
Authors: Jiahang Cao, Qiang Zhang, Jingkai Sun, Jiaxu Wang, Hao Cheng, Yulin Li, Jun Ma, Kun Wu, Zhiyuan Xu, Yecheng Shao, Wen Zhao, Gang Han, Yijie Guo, Renjing Xu
Date: 2024-09-11 00:00:00
Tasks: Mamba

Diffusion models have been widely employed in the field of 3D manipulation due to their efficient capability to learn distributions, allowing for precise prediction of action trajectories. However, diffusion models typically rely on large parameter UNet backbones as policy networks, which can be challenging to deploy on resource-constrained devices. Recently, the Mamba model has emerged as a promising solution for efficient modeling, offering low computational complexity and strong performance in sequence modeling. In this work, we propose the Mamba Policy, a lighter but stronger policy that reduces the parameter count by over 80% compared to the original policy network while achieving superior performance. Specifically, we introduce the XMamba Block, which effectively integrates input information with conditional features and leverages a combination of Mamba and Attention mechanisms for deep feature extraction. Extensive experiments demonstrate that the Mamba Policy excels on the Adroit, Dexart, and MetaWorld datasets, requiring significantly fewer computational resources. Additionally, we highlight the Mamba Policy's enhanced robustness in long-horizon scenarios compared to baseline methods and explore the performance of various Mamba variants within the Mamba Policy framework. Real-world experiments are also conducted to further validate its effectiveness. Our open-source project page can be found at https://andycao1125.github.io/mamba_policy/.
MVLLaVA: An Intelligent Agent for Unified and Flexible Novel View Synthesis
Authors: Hanyu Jiang, Jian Xue, Xing Lan, Guohong Hu, Ke Lu
Date: 2024-09-11 00:00:00
Tasks: Descriptive, Novel View Synthesis

This paper introduces MVLLaVA, an intelligent agent designed for novel view synthesis tasks. MVLLaVA integrates multiple multi-view diffusion models with a large multimodal model, LLaVA, enabling it to handle a wide range of tasks efficiently. MVLLaVA represents a versatile and unified platform that adapts to diverse input types, including a single image, a descriptive caption, or a specific change in viewing azimuth, guided by language instructions for viewpoint generation. We carefully craft task-specific instruction templates, which are subsequently used to fine-tune LLaVA. As a result, MVLLaVA acquires the capability to generate novel view images based on user instructions, demonstrating its flexibility across diverse tasks. Experiments are conducted to validate the effectiveness of MVLLaVA, demonstrating its robust performance and versatility in tackling diverse novel view synthesis challenges.
Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations
Authors: Gaia Romana De Paolis, Dimitrios Lenis, Johannes Novotny, Maria Wimmer, Astrid Berg, Theresa Neubauer, Philip Matthias Winter, David Major, Ariharasudhan Muthusami, Gerald Schröcker, Martin Mienkina, Katja Bühler
Date: 2024-09-11 00:00:00
Tasks: Decision Making, Meta-Learning

Efficient and fast reconstruction of anatomical structures plays a crucial role in clinical practice. Minimizing retrieval and processing times not only potentially enhances swift response and decision-making in critical scenarios but also supports interactive surgical planning and navigation. Recent methods attempt to solve the medical shape reconstruction problem by utilizing implicit neural functions. However, their performance suffers in terms of generalization and computation time, a critical metric for real-time applications. To address these challenges, we propose to leverage meta-learning to improve the network parameters initialization, reducing inference time by an order of magnitude while maintaining high accuracy. We evaluate our approach on three public datasets covering different anatomical shapes and modalities, namely CT and MRI. Our experimental results show that our model can handle various input configurations, such as sparse slices with different orientations and spacings. Additionally, we demonstrate that our method exhibits strong transferable capabilities in generalizing to shape domains unobserved at training time.
Deep intra-operative illumination calibration of hyperspectral cameras
Authors: Alexander Baumann, Leonardo Ayala, Alexander Studier-Fischer, Jan Sellner, Berkin Özdemir, Karl-Friedrich Kowalewski, Slobodan Ilic, Silvia Seidlitz, Lena Maier-Hein
Date: 2024-09-11 00:00:00
Tasks: parameter estimation, Scene Segmentation

Hyperspectral imaging (HSI) is emerging as a promising novel imaging modality with various potential surgical applications. Currently available cameras, however, suffer from poor integration into the clinical workflow because they require the lights to be switched off, or the camera to be manually recalibrated as soon as lighting conditions change. Given this critical bottleneck, the contribution of this paper is threefold: (1) We demonstrate that dynamically changing lighting conditions in the operating room dramatically affect the performance of HSI applications, namely physiological parameter estimation, and surgical scene segmentation. (2) We propose a novel learning-based approach to automatically recalibrating hyperspectral images during surgery and show that it is sufficiently accurate to replace the tedious process of white reference-based recalibration. (3) Based on a total of 742 HSI cubes from a phantom, porcine models, and rats we show that our recalibration method not only outperforms previously proposed methods, but also generalizes across species, lighting conditions, and image processing tasks. Due to its simple workflow integration as well as high accuracy, speed, and generalization capabilities, our method could evolve as a central component in clinical surgical HSI.
Structure Modeling Activation Free Fourier Network for Spacecraft Image Denoising
Authors: Jingfan Yang, Hu Gao, Ying Zhang, Bowen Ma, Depeng Dang
Date: 2024-09-11 00:00:00
Tasks: Denoising, Image Denoising

Spacecraft image denoising is a crucial fundamental technology closely related to aerospace research. However, the existing deep learning-based image denoising methods are primarily designed for natural image and fail to adequately consider the characteristics of spacecraft image(e.g. low-light conditions, repetitive periodic structures), resulting in suboptimal performance in the spacecraft image denoising task. To address the aforementioned problems, we propose a Structure modeling Activation Free Fourier Network (SAFFN), which is an efficient spacecraft image denoising method including Structure Modeling Block (SMB) and Activation Free Fourier Block (AFFB). We present SMB to effectively extract edge information and model the structure for better identification of spacecraft components from dark regions in spacecraft noise image. We present AFFB and utilize an improved Fast Fourier block to extract repetitive periodic features and long-range information in noisy spacecraft image. Extensive experimental results demonstrate that our SAFFN performs competitively compared to the state-of-the-art methods on spacecraft noise image datasets. The codes are available at: https://github.com/shenduke/SAFFN.
Pushing the Limits of Vision-Language Models in Remote Sensing without Human Annotations
Authors: Keumgang Cha, Donggeun Yu, Junghoon Seo
Date: 2024-09-11 00:00:00
Tasks: Image-text Retrieval, Text Retrieval, zero-shot-classification, Zero-Shot Learning

The prominence of generalized foundation models in vision-language integration has witnessed a surge, given their multifarious applications. Within the natural domain, the procurement of vision-language datasets to construct these foundation models is facilitated by their abundant availability and the ease of web crawling. Conversely, in the remote sensing domain, although vision-language datasets exist, their volume is suboptimal for constructing robust foundation models. This study introduces an approach to curate vision-language datasets by employing an image decoding machine learning model, negating the need for human-annotated labels. Utilizing this methodology, we amassed approximately 9.6 million vision-language paired datasets in VHR imagery. The resultant model outperformed counterparts that did not leverage publicly available vision-language datasets, particularly in downstream tasks such as zero-shot classification, semantic localization, and image-text retrieval. Moreover, in tasks exclusively employing vision encoders, such as linear probing and k-NN classification, our model demonstrated superior efficacy compared to those relying on domain-specific vision-language datasets.
SoftShadow: Leveraging Penumbra-Aware Soft Masks for Shadow Removal
Authors: Xinrui Wang, Lanqing Guo, Xiyu Wang, Siyu Huang, Bihan Wen
Date: 2024-09-11 00:00:00
Tasks: Image Shadow Removal, Shadow Removal

Recent advancements in deep learning have yielded promising results for the image shadow removal task. However, most existing methods rely on binary pre-generated shadow masks. The binary nature of such masks could potentially lead to artifacts near the boundary between shadow and non-shadow areas. In view of this, inspired by the physical model of shadow formation, we introduce novel soft shadow masks specifically designed for shadow removal. To achieve such soft masks, we propose a \textit{SoftShadow} framework by leveraging the prior knowledge of pretrained SAM and integrating physical constraints. Specifically, we jointly tune the SAM and the subsequent shadow removal network using penumbra formation constraint loss and shadow removal loss. This framework enables accurate predictions of penumbra (partially shaded regions) and umbra (fully shaded regions) areas while simultaneously facilitating end-to-end shadow removal. Through extensive experiments on popular datasets, we found that our SoftShadow framework, which generates soft masks, can better restore boundary artifacts, achieve state-of-the-art performance, and demonstrate superior generalizability.
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement
Authors: Xianmin Chen, Peiliang Huang, Xiaoxu Feng, Dingwen Zhang, Longfei Han, Junwei Han
Date: 2024-09-11 00:00:00
Tasks: Demosaicking, Denoising, Exposure Correction, Image Enhancement, Low-Light Image Enhancement, Mamba

Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. Many deep learning-based methods have been developed to address this issue and have shown promising results in recent years. However, single-stage methods, which attempt to unify the complex mapping across both domains, leading to limited denoising performance. In contrast, two-stage approaches typically decompose a raw image with color filter arrays (CFA) into a four-channel RGGB format before feeding it into a neural network. However, this strategy overlooks the critical role of demosaicing within the Image Signal Processing (ISP) pipeline, leading to color distortions under varying lighting conditions, especially in low-light scenarios. To address these issues, we design a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs. Furthermore, we present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction. By bridging demosaicing and denoising, better raw image enhancement is achieved. Experimental evaluations conducted on public datasets SID and MCR demonstrate that our proposed RAWMamba achieves state-of-the-art performance on cross-domain mapping.
SCLNet: A Scale-Robust Complementary Learning Network for Object Detection in UAV Images
Authors: Xuexue Li
Date: 2024-09-11 00:00:00
Tasks: object-detection, Object Detection

Most recent UAV (Unmanned Aerial Vehicle) detectors focus primarily on general challenge such as uneven distribution and occlusion. However, the neglect of scale challenges, which encompass scale variation and small objects, continues to hinder object detection in UAV images. Although existing works propose solutions, they are implicitly modeled and have redundant steps, so detection performance remains limited. And one specific work addressing the above scale challenges can help improve the performance of UAV image detectors. Compared to natural scenes, scale challenges in UAV images happen with problems of limited perception in comprehensive scales and poor robustness to small objects. We found that complementary learning is beneficial for the detection model to address the scale challenges. Therefore, the paper introduces it to form our scale-robust complementary learning network (SCLNet) in conjunction with the object detection model. The SCLNet consists of two implementations and a cooperation method. In detail, one implementation is based on our proposed scale-complementary decoder and scale-complementary loss function to explicitly extract complementary information as complement, named comprehensive-scale complementary learning (CSCL). Another implementation is based on our proposed contrastive complement network and contrastive complement loss function to explicitly guide the learning of small objects with the rich texture detail information of the large objects, named inter-scale contrastive complementary learning (ICCL). In addition, an end-to-end cooperation (ECoop) between two implementations and with the detection model is proposed to exploit each potential.
Insight Any Instance: Promptable Instance Segmentation for Remote Sensing Images
Authors: Xuexue Li
Date: 2024-09-11 00:00:00
Tasks: Instance Segmentation, Prompt Learning, Segmentation, Semantic Segmentation

Instance segmentation of remote sensing images (RSIs) is an essential task for a wide range of applications such as land planning and intelligent transport. Instance segmentation of RSIs is constantly plagued by the unbalanced ratio of foreground and background and limited instance size. And most of the instance segmentation models are based on deep feature learning and contain operations such as multiple downsampling, which is harmful to instance segmentation of RSIs, and thus the performance is still limited. Inspired by the recent superior performance of prompt learning in visual tasks, we propose a new prompt paradigm to address the above issues. Based on the existing instance segmentation model, firstly, a local prompt module is designed to mine local prompt information from original local tokens for specific instances; secondly, a global-to-local prompt module is designed to model the contextual information from the global tokens to the local tokens where the instances are located for specific instances. Finally, a proposal's area loss function is designed to add a decoupling dimension for proposals on the scale to better exploit the potential of the above two prompt modules. It is worth mentioning that our proposed approach can extend the instance segmentation model to a promptable instance segmentation model, i.e., to segment the instances with the specific boxes prompt. The time consumption for each promptable instance segmentation process is only 40 ms. The paper evaluates the effectiveness of our proposed approach based on several existing models in four instance segmentation datasets of RSIs, and thorough experiments prove that our proposed approach is effective for addressing the above issues and is a competitive model for instance segmentation of RSIs.
DDEvENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRI
Authors: Chenjun Li, Dian Yang, Shun Yao, Shuyue Wang, Ye Wu, Le Zhang, Qiannuo Li, Kang Ik Kevin Cho, Johanna Seitz-Holland, Lipeng Ning, Jon Haitz Legarreta, Yogesh Rathi, Carl-Fredrik Westin, Lauren J. O'Donnell, Nir A. Sochen, Ofer Pasternak, Fan Zhang
Date: 2024-09-11 00:00:00
Tasks: Diffusion  MRI, Ensemble Learning

In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI. Using EVENet, we obtained accurate parcellation and uncertainty estimates across different datasets from healthy and clinical populations and with different imaging acquisitions. The overall network includes five parallel subnetworks, where each is dedicated to learning the FreeSurfer parcellation for a certain diffusion MRI parameter. An evidence-based ensemble methodology is then proposed to fuse the individual outputs. We perform experimental evaluations on large-scale datasets from multiple imaging sources, including high-quality diffusion MRI data from healthy adults and clinically diffusion MRI data from participants with various brain diseases (schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, Parkinson's disease, cerebral small vessel disease, and neurosurgical patients with brain tumors). Compared to several state-of-the-art methods, our experimental results demonstrate highly improved parcellation accuracy across the multiple testing datasets despite the differences in dMRI acquisition protocols and health conditions. Furthermore, thanks to the uncertainty estimation, our EVENet approach demonstrates a good ability to detect abnormal brain regions in patients with lesions, enhancing the interpretability and reliability of the segmentation results.
Performance Assessment of Feature Detection Methods for 2-D FS Sonar Imagery
Authors: Hitesh Kyatham, Shahriar Negahdaripour, Michael Xu, Xiaomin Lin, Miao Yu, Yiannis Aloimonos
Date: 2024-09-11 00:00:00
Tasks: Object Recognition

Underwater robot perception is crucial in scientific subsea exploration and commercial operations. The key challenges include non-uniform lighting and poor visibility in turbid environments. High-frequency forward-look sonar cameras address these issues, by providing high-resolution imagery at maximum range of tens of meters, despite complexities posed by high degree of speckle noise, and lack of color and texture. In particular, robust feature detection is an essential initial step for automated object recognition, localization, navigation, and 3-D mapping. Various local feature detectors developed for RGB images are not well-suited for sonar data. To assess their performances, we evaluate a number of feature detectors using real sonar images from five different sonar devices. Performance metrics such as detection accuracy, false positives, and robustness to variations in target characteristics and sonar devices are applied to analyze the experimental results. The study would provide a deeper insight into the bottlenecks of feature detection for sonar data, and developing more effective methods