pwc-archive/papers-with-abstracts huggingface dataset:
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification
Authors: Feihong He, Gang Li, Lingyu Si, Leilei Yan, Fanzhang Li, Fuchun Sun
Date: 2023-10-05 00:00:00
Tasks: Classification, Contrastive Learning, Few-Shot Image Classification, Few-Shot Learning, image-classification, Image Classification

Few-shot image classification has received considerable attention for addressing the challenge of poor classification performance with limited samples in novel classes. However, numerous studies have employed sophisticated learning strategies and diversified feature extraction methods to address this issue. In this paper, we propose our method called PrototypeFormer, which aims to significantly advance traditional few-shot image classification approaches by exploring prototype relationships. Specifically, we utilize a transformer architecture to build a prototype extraction module, aiming to extract class representations that are more discriminative for few-shot classification. Additionally, during the model training process, we propose a contrastive learning-based optimization approach to optimize prototype features in few-shot learning scenarios. Despite its simplicity, the method performs remarkably well, with no bells and whistles. We have experimented with our approach on several popular few-shot image classification benchmark datasets, which shows that our method outperforms all current state-of-the-art methods. In particular, our method achieves 97.07% and 90.88% on 5-way 5-shot and 5-way 1-shot tasks of miniImageNet, which surpasses the state-of-the-art results with accuracy of 7.27% and 8.72%, respectively. The code will be released later.
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery
Authors: Joseph A. Gallego-Mejia, Anna Jungbluth, Laura Martínez-Ferrer, Matt Allen, Francisco Dorr, Freddie Kalaitzis, Raúl Ramos-Pollán
Date: 2023-10-05 00:00:00
Tasks: Earth Observation, Image Segmentation, Segmentation, Self-Supervised Learning, Semantic Segmentation

Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent characteristics of the Self-Distillation with No Labels (DINO) algorithm and its application to Synthetic Aperture Radar (SAR) imagery. We pre-train a vision transformer (ViT)-based DINO model using unlabeled SAR data, and later fine-tune the model to predict high-resolution land cover maps. We rigorously evaluate the utility of attention maps generated by the ViT backbone and compare them with the model's token embedding space. We observe a small improvement in model performance with pre-training compared to training from scratch and discuss the limitations and opportunities of SSL for remote sensing and land cover segmentation. Beyond small performance increases, we show that ViT attention maps hold great intrinsic value for remote sensing, and could provide useful inputs to other algorithms. With this, our work lays the groundwork for bigger and better SSL models for Earth Observation.
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
Authors: Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva, Vladimir Arkhipkin, Igor Pavlov, Ilya Ryabov, Angelina Kuts, Alexander Panchenko, Andrey Kuznetsov, Denis Dimitrov
Date: 2023-10-05 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative architectures. Among these, there are diffusion-based models that have demonstrated essential quality enhancements. These models are generally split into two categories: pixel-level and latent-level approaches. We present Kandinsky1, a novel exploration of latent diffusion architecture, combining the principles of the image prior models with latent diffusion techniques. The image prior model is trained separately to map text embeddings to image embeddings of CLIP. Another distinct feature of the proposed model is the modified MoVQ implementation, which serves as the image autoencoder component. Overall, the designed model contains 3.3B parameters. We also deployed a user-friendly demo system that supports diverse generative modes such as text-to-image generation, image fusion, text and image fusion, image variations generation, and text-guided inpainting/outpainting. Additionally, we released the source code and checkpoints for the Kandinsky models. Experimental evaluations demonstrate a FID score of 8.03 on the COCO-30K dataset, marking our model as the top open-source performer in terms of measurable image generation quality.
IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
Authors: Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux, David Neubauer, Ulrike Lohmann
Date: 2023-10-05 00:00:00

Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets. This work will enable novel research to improve ice cloud process understanding and hence, reduce uncertainties in a changing climate and help assess geoengineering methods for cirrus clouds.
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification
Authors: Dimitrios Kollias, Karanjot Vendal, Priyanka Gadhavi, Solomon Russom
Date: 2023-10-05 00:00:00
Tasks: Data Augmentation

Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. Accurate determination of the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is crucial for personalized treatment strategies. However, traditional methods are labor-intensive and time-consuming. This paper proposes a novel multi-modal approach, BTDNet, leveraging multi-parametric MRI scans, including FLAIR, T1w, T1wCE, and T2 3D volumes, to predict MGMT promoter methylation status. BTDNet addresses two main challenges: the variable volume lengths (i.e., each volume consists of a different number of slices) and the volume-level annotations (i.e., the whole 3D volume is annotated and not the independent slices that it consists of). BTDNet consists of four components: i) the data augmentation one (that performs geometric transformations, convex combinations of data pairs and test-time data augmentation); ii) the 3D analysis one (that performs global analysis through a CNN-RNN); iii) the routing one (that contains a mask layer that handles variable input feature lengths), and iv) the modality fusion one (that effectively enhances data representation, reduces ambiguities and mitigates data scarcity). The proposed method outperforms by large margins the state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge, offering a promising avenue for enhancing brain tumor diagnosis and treatment.
Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis
Authors: Shunkai Shi, Yuqi Wang, Qihui Ye, Yanran Wang, Yiming Zhu, Muhammad Hassan, Aikaterini Melliou, Dongmei Yu
Date: 2023-10-05 00:00:00
Tasks: Diagnostic, Segmentation, Semantic Segmentation

In Traditional Chinese Medicine, the tooth marks on the tongue, stemming from prolonged dental pressure, serve as a crucial indicator for assessing qi (yang) deficiency, which is intrinsically linked to visceral health. Manual diagnosis of tooth-marked tongue solely relies on experience. Nonetheless, the diversity in shape, color, and type of tooth marks poses a challenge to diagnostic accuracy and consistency. To address these problems, herein we propose a multi-task joint learning model named Ammonia-Net. This model employs a convolutional neural network-based architecture, specifically designed for multi-class segmentation and classification of tongue images. Ammonia-Net performs semantic segmentation of tongue images to identify tongue and tooth marks. With the assistance of segmentation output, it classifies the images into the desired number of classes: healthy tongue, light tongue, moderate tongue, and severe tongue. As far as we know, this is the first attempt to apply the semantic segmentation results of tooth marks for tooth-marked tongue classification. To train Ammonia-Net, we collect 856 tongue images from 856 subjects. After a number of extensive experiments, the experimental results show that the proposed model achieves 99.06% accuracy in the two-class classification task of tooth-marked tongue identification and 80.02%. As for the segmentation task, mIoU for tongue and tooth marks amounts to 71.65%.
Multi-Resolution Audio-Visual Feature Fusion for Temporal Action Localization
Authors: Edward Fish, Jon Weinbren, Andrew Gilbert
Date: 2023-10-05 00:00:00
Tasks: Action Localization, Temporal Action Localization

Temporal Action Localization (TAL) aims to identify actions' start, end, and class labels in untrimmed videos. While recent advancements using transformer networks and Feature Pyramid Networks (FPN) have enhanced visual feature recognition in TAL tasks, less progress has been made in the integration of audio features into such frameworks. This paper introduces the Multi-Resolution Audio-Visual Feature Fusion (MRAV-FF), an innovative method to merge audio-visual data across different temporal resolutions. Central to our approach is a hierarchical gated cross-attention mechanism, which discerningly weighs the importance of audio information at diverse temporal scales. Such a technique not only refines the precision of regression boundaries but also bolsters classification confidence. Importantly, MRAV-FF is versatile, making it compatible with existing FPN TAL architectures and offering a significant enhancement in performance when audio data is available.
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images
Authors: Sireesha Chamarthi, Katharina Fogelberg, Roman C. Maron, Titus J. Brinker, Julia Niebling
Date: 2023-10-05 00:00:00
Tasks: Domain Adaptation, Lesion Classification, Skin Lesion Classification, Unsupervised Domain Adaptation

The potential of deep neural networks in skin lesion classification has already been demonstrated to be on-par if not superior to the dermatologists diagnosis. However, the performance of these models usually deteriorates when the test data differs significantly from the training data (i.e. domain shift). This concerning limitation for models intended to be used in real-world skin lesion classification tasks poses a risk to patients. For example, different image acquisition systems or previously unseen anatomical sites on the patient can suffice to cause such domain shifts. Mitigating the negative effect of such shifts is therefore crucial, but developing effective methods to address domain shift has proven to be challenging. In this study, we carry out an in-depth analysis of eight different unsupervised domain adaptation methods to analyze their effectiveness in improving generalization for dermoscopic datasets. To ensure robustness of our findings, we test each method on a total of ten distinct datasets, thereby covering a variety of possible domain shifts. In addition, we investigated which factors in the domain shifted datasets have an impact on the effectiveness of domain adaptation methods. Our findings show that all of the eight domain adaptation methods result in improved AUPRC for the majority of analyzed datasets. Altogether, these results indicate that unsupervised domain adaptations generally lead to performance improvements for the binary melanoma-nevus classification task regardless of the nature of the domain shift. However, small or heavily imbalanced datasets lead to a reduced conformity of the results due to the influence of these factors on the methods performance.
Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Covering
Authors: Fei Hou, Xuhui Chen, Wencheng Wang, Hong Qin, Ying He
Date: 2023-10-05 00:00:00

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user-specified parameter $r$ (a small positive real number) as input and extracts an iso-surface with an iso-value $r$ using the conventional marching cubes algorithm. We show that the computed iso-surface is the boundary of the $r$-offset volume of the target zero level-set $S$, which is an orientable manifold, regardless of the topology of $S$. Next, the algorithm computes a covering map to project the boundary mesh onto $S$, preserving the mesh's topology and avoiding folding. If $S$ is an orientable manifold surface, our algorithm separates the double-layered mesh into a single layer using a robust minimum-cut post-processing step. Otherwise, it keeps the double-layered mesh as the output. We validate our algorithm by reconstructing 3D surfaces of open models and demonstrate its efficacy and effectiveness on synthetic models and benchmark datasets. Our experimental results confirm that our method is robust and produces meshes with better quality in terms of both visual evaluation and quantitative measures than existing UDF-based methods. The source code is available at https://github.com/jjjkkyz/DCUDF.
FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators
Authors: Haiping Wang, YuAn Liu, Bing Wang, Yujing Sun, Zhen Dong, Wenping Wang, Bisheng Yang
Date: 2023-10-05 00:00:00
Tasks: Image to Point Cloud Registration, Metric Learning, Point Cloud Registration

Matching cross-modality features between images and point clouds is a fundamental problem for image-to-point cloud registration. However, due to the modality difference between images and points, it is difficult to learn robust and discriminative cross-modality features by existing metric learning methods for feature matching. Instead of applying metric learning on cross-modality data, we propose to unify the modality between images and point clouds by pretrained large-scale models first, and then establish robust correspondence within the same modality. We show that the intermediate features, called diffusion features, extracted by depth-to-image diffusion models are semantically consistent between images and point clouds, which enables the building of coarse but robust cross-modality correspondences. We further extract geometric features on depth maps produced by the monocular depth estimator. By matching such geometric features, we significantly improve the accuracy of the coarse correspondences produced by diffusion features. Extensive experiments demonstrate that without any task-specific training, direct utilization of both features produces accurate image-to-point cloud registration. On three public indoor and outdoor benchmarks, the proposed method averagely achieves a 20.6 percent improvement in Inlier Ratio, a three-fold higher Inlier Number, and a 48.6 percent improvement in Registration Recall than existing state-of-the-arts.
A Complementary Global and Local Knowledge Network for Ultrasound denoising with Fine-grained Refinement
Authors: Zhenyu Bu, Kai-Ni Wang, Fuxing Zhao, Shengxiao Li, Guang-Quan Zhou
Date: 2023-10-05 00:00:00
Tasks: Decoder, Denoising, Diagnostic, image smoothing

Ultrasound imaging serves as an effective and non-invasive diagnostic tool commonly employed in clinical examinations. However, the presence of speckle noise in ultrasound images invariably degrades image quality, impeding the performance of subsequent tasks, such as segmentation and classification. Existing methods for speckle noise reduction frequently induce excessive image smoothing or fail to preserve detailed information adequately. In this paper, we propose a complementary global and local knowledge network for ultrasound denoising with fine-grained refinement. Initially, the proposed architecture employs the L-CSwinTransformer as encoder to capture global information, incorporating CNN as decoder to fuse local features. We expand the resolution of the feature at different stages to extract more global information compared to the original CSwinTransformer. Subsequently, we integrate Fine-grained Refinement Block (FRB) within the skip-connection stage to further augment features. We validate our model on two public datasets, HC18 and BUSI. Experimental results demonstrate that our model can achieve competitive performance in both quantitative metrics and visual performance. Our code will be available at https://github.com/AAlkaid/USDenoising.
Learning to Simplify Spatial-Temporal Graphs in Gait Analysis
Authors: Adrian Cosma, Emilian Radoi
Date: 2023-10-05 00:00:00
Tasks: Anatomy, Gait Recognition, Person Identification

Gait analysis leverages unique walking patterns for person identification and assessment across multiple domains. Among the methods used for gait analysis, skeleton-based approaches have shown promise due to their robust and interpretable features. However, these methods often rely on hand-crafted spatial-temporal graphs that are based on human anatomy disregarding the particularities of the dataset and task. This paper proposes a novel method to simplify the spatial-temporal graph representation for gait-based gender estimation, improving interpretability without losing performance. Our approach employs two models, an upstream and a downstream model, that can adjust the adjacency matrix for each walking instance, thereby removing the fixed nature of the graph. By employing the Straight-Through Gumbel-Softmax trick, our model is trainable end-to-end. We demonstrate the effectiveness of our approach on the CASIA-B dataset for gait-based gender estimation. The resulting graphs are interpretable and differ qualitatively from fixed graphs used in existing models. Our research contributes to enhancing the explainability and task-specific adaptability of gait recognition, promoting more efficient and reliable gait-based biometrics.
OpenPatch: a 3D patchwork for Out-Of-Distribution detection
Authors: Paolo Rabino, Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi
Date: 2023-10-05 00:00:00
Tasks: Novelty Detection, Out-of-Distribution Detection

Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel classes during deployment poses a significant threat, thus it is crucial to effectively detect them. Ideally, this skill should be used when needed without requiring any further computational training effort at every new task. Out-of-distribution detection has attracted significant attention in the last years, however the majority of the studies deal with 2D images ignoring the inherent 3D nature of the real-world and often confusing between domain and semantic novelty. In this work, we focus on the latter, considering the objects geometric structure captured by 3D point clouds regardless of the specific domain. We advance the field by introducing OpenPatch that builds on a large pre-trained model and simply extracts from its intermediate features a set of patch representations that describe each known class. For any new sample, we obtain a novelty score by evaluating whether it can be recomposed mainly by patches of a single known class or rather via the contribution of multiple classes. We present an extensive experimental evaluation of our approach for the task of semantic novelty detection on real-world point cloud samples when the reference known data are synthetic. We demonstrate that OpenPatch excels in both the full and few-shot known sample scenarios, showcasing its robustness across varying pre-training objectives and network backbones. The inherent training-free nature of our method allows for its immediate application to a wide array of real-world tasks, offering a compelling advantage over approaches that need expensive retraining efforts.
ACT-Net: Anchor-context Action Detection in Surgery Videos
Authors: Luoying Hao, Yan Hu, Wenjun Lin, Qun Wang, Heng Li, Huazhu Fu, Jinming Duan, Jiang Liu
Date: 2023-10-05 00:00:00
Tasks: Action Detection, Denoising

Recognition and localization of surgical detailed actions is an essential component of developing a context-aware decision support system. However, most existing detection algorithms fail to provide high-accuracy action classes even having their locations, as they do not consider the surgery procedure's regularity in the whole video. This limitation hinders their application. Moreover, implementing the predictions in clinical applications seriously needs to convey model confidence to earn entrustment, which is unexplored in surgical action prediction. In this paper, to accurately detect fine-grained actions that happen at every moment, we propose an anchor-context action detection network (ACTNet), including an anchor-context detection (ACD) module and a class conditional diffusion (CCD) module, to answer the following questions: 1) where the actions happen; 2) what actions are; 3) how confidence predictions are. Specifically, the proposed ACD module spatially and temporally highlights the regions interacting with the extracted anchor in surgery video, which outputs action location and its class distribution based on anchor-context interactions. Considering the full distribution of action classes in videos, the CCD module adopts a denoising diffusion-based generative model conditioned on our ACD estimator to further reconstruct accurately the action predictions. Moreover, we utilize the stochastic nature of the diffusion model outputs to access model confidence for each prediction. Our method reports the state-of-the-art performance, with improvements of 4.0% mAP against baseline on the surgical video dataset.
Point-Based Radiance Fields for Controllable Human Motion Synthesis
Authors: HaiTao Yu, Deheng Zhang, Peiyuan Xie, Tianyi Zhang
Date: 2023-10-05 00:00:00
Tasks: Motion Synthesis, Novel View Synthesis

This paper proposes a novel controllable human motion synthesis method for fine-level deformation based on static point-based radiance fields. Although previous editable neural radiance field methods can generate impressive results on novel-view synthesis and allow naive deformation, few algorithms can achieve complex 3D human editing such as forward kinematics. Our method exploits the explicit point cloud to train the static 3D scene and apply the deformation by encoding the point cloud translation using a deformation MLP. To make sure the rendering result is consistent with the canonical space training, we estimate the local rotation using SVD and interpolate the per-point rotation to the query view direction of the pre-trained radiance field. Extensive experiments show that our approach can significantly outperform the state-of-the-art on fine-level complex deformation which can be generalized to other 3D characters besides humans.