pwc-archive/papers-with-abstracts huggingface dataset:
Adapter is All You Need for Tuning Visual Tasks
Authors: Dongshuo Yin, Leiyi Hu, Bin Li, Youqun Zhang
Date: 2023-11-25 00:00:00
Tasks: All, image-classification, Image Classification, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent delta-tuning methods provide more options for visual classification tasks. Despite their success, existing visual delta-tuning art fails to exceed the upper limit of full fine-tuning on challenging tasks like instance segmentation and semantic segmentation. To find a competitive alternative to full fine-tuning, we propose the Multi-cognitive Visual Adapter (Mona) tuning, a novel adapter-based tuning method. First, we introduce multiple vision-friendly filters into the adapter to enhance its ability to process visual signals, while previous methods mainly rely on language-friendly linear filters. Second, we add the scaled normalization layer in the adapter to regulate the distribution of input features for visual filters. To fully demonstrate the practicality and generality of Mona, we conduct experiments on multiple representative visual tasks, including instance segmentation on COCO, semantic segmentation on ADE20K, object detection on Pascal VOC, and image classification on several common datasets. Exciting results illustrate that Mona surpasses full fine-tuning on all these tasks and is the only delta-tuning method outperforming full fine-tuning on instance segmentation and semantic segmentation tasks. For example, Mona achieves a 1% performance gain on the COCO dataset compared to full fine-tuning. Comprehensive results suggest that Mona-tuning is more suitable for retaining and utilizing the capabilities of pre-trained models than full fine-tuning. The code will be released at https://github.com/Leiyi-Hu/mona.
Coordinate-Aware Modulation for Neural Fields
Authors: Joo Chan Lee, Daniel Rho, Seungtae Nam, Jong Hwan Ko, Eunbyung Park
Date: 2023-11-25 00:00:00
Tasks: Novel View Synthesis, Video Compression

Neural fields, mapping low-dimensional input coordinates to corresponding signals, have shown promising results in representing various signals. Numerous methodologies have been proposed, and techniques employing MLPs and grid representations have achieved substantial success. MLPs allow compact and high expressibility, yet often suffer from spectral bias and slow convergence speed. On the other hand, methods using grids are free from spectral bias and achieve fast training speed, however, at the expense of high spatial complexity. In this work, we propose a novel way for exploiting both MLPs and grid representations in neural fields. Unlike the prevalent methods that combine them sequentially (extract features from the grids first and feed them to the MLP), we inject spectral bias-free grid representations into the intermediate features in the MLP. More specifically, we suggest a Coordinate-Aware Modulation (CAM), which modulates the intermediate features using scale and shift parameters extracted from the grid representations. This can maintain the strengths of MLPs while mitigating any remaining potential biases, facilitating the rapid learning of high-frequency components. In addition, we empirically found that the feature normalizations, which have not been successful in neural filed literature, proved to be effective when applied in conjunction with the proposed CAM. Experimental results demonstrate that CAM enhances the performance of neural representation and improves learning stability across a range of signals. Especially in the novel view synthesis task, we achieved state-of-the-art performance with the least number of parameters and fast training speed for dynamic scenes and the best performance under 1MB memory for static scenes. CAM also outperforms the best-performing video compression methods using neural fields by a large margin.
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Authors: Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya, Michael C. Kampffmeyer, Robert Jenssen
Date: 2023-11-25 00:00:00
Tasks: Computed Tomography (CT), Lesion Segmentation

Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in medical images. However, most deep learning models rely on augmentation techniques that treat medical images as natural images. For contrast-enhanced Computed Tomography (CT) images in particular, the signals producing the voxel intensities have physical meaning, which is lost during preprocessing and augmentation when treating such images as natural images. To address this, we propose a novel preprocessing and intensity augmentation scheme inspired by how radiologists leverage multiple viewing windows when evaluating CT images. Our proposed method, window shifting, randomly places the viewing windows around the region of interest during training. This approach improves liver lesion segmentation performance and robustness on images with poorly timed contrast agent. Our method outperforms classical intensity augmentations as well as the intensity augmentation pipeline of the popular nn-UNet on multiple datasets.
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
Authors: Lin Tian, Zi Li, Fengze Liu, Xiaoyu Bai, Jia Ge, Le Lu, Marc Niethammer, Xianghua Ye, Ke Yan, Daikai Jin
Date: 2023-11-25 00:00:00
Tasks: Image Registration, Medical Image Analysis, Medical Image Registration

Image registration is a fundamental medical image analysis task. Ideally, registration should focus on aligning semantically corresponding voxels, i.e., the same anatomical locations. However, existing methods often optimize similarity measures computed directly on intensities or on hand-crafted features, which lack anatomical semantic information. These similarity measures may lead to sub-optimal solutions where large deformations, complex anatomical differences, or cross-modality imagery exist. In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration building on top of a Self-supervised Anatomical eMbedding (SAM) algorithm, which is capable of computing dense anatomical correspondences between two images at the voxel level. We name our approach SAM-Enhanced registration (SAME++), which decomposes image registration into four steps: affine transformation, coarse deformation, deep non-parametric transformation, and instance optimization. Using SAM embeddings, we enhance these steps by finding more coherent correspondence and providing features with better semantic guidance. We extensively evaluated SAME++ using more than 50 labeled organs on three challenging inter-subject registration tasks of different body parts. As a complete registration framework, SAME++ markedly outperforms leading methods by $4.2\%$ - $8.2\%$ in terms of Dice score while being orders of magnitude faster than numerical optimization-based methods. Code is available at \url{https://github.com/alibaba-damo-academy/same}.
Neural Network Based Approach to Recognition of Meteor Tracks in the Mini-EUSO Telescope Data
Authors: Mikhail Zotov, Dmitry Anzhiganov, Aleksandr Kryazhenkov, Dario Barghini, Matteo Battisti, Alexander Belov, Mario Bertaina, Marta Bianciotto, Francesca Bisconti, Carl Blaksley, Sylvie Blin, Giorgio Cambiè, Francesca Capel, Marco Casolino, Toshikazu Ebisuzaki, Johannes Eser, Francesco Fenu, Massimo Alberto Franceschi, Alessio Golzio, Philippe Gorodetzky, Fumiyoshi Kajino, Hiroshi Kasuga, Pavel Klimov, Massimiliano Manfrin, Laura Marcelli, Hiroko Miyamoto, Alexey Murashov, Tommaso Napolitano, Hiroshi Ohmori, Angela Olinto, Etienne Parizot, Piergiorgio Picozza, Lech Wiktor Piotrowski, Zbigniew Plebaniak, Guillaume Prévôt, Enzo Reali, Marco Ricci, Giulia Romoli, Naoto Sakaki, Kenji Shinozaki, Christophe De La Taille, Yoshiyuki Takizawa, Michal Vrábel, Lawrence Wiencke
Date: 2023-11-25 00:00:00
Tasks: Binary Classification

Mini-EUSO is a wide-angle fluorescence telescope that registers ultraviolet (UV) radiation in the nocturnal atmosphere of Earth from the International Space Station. Meteors are among multiple phenomena that manifest themselves not only in the visible range but also in the UV. We present two simple artificial neural networks that allow for recognizing meteor signals in the Mini-EUSO data with high accuracy in terms of a binary classification problem. We expect that similar architectures can be effectively used for signal recognition in other fluorescence telescopes, regardless of the nature of the signal. Due to their simplicity, the networks can be implemented in onboard electronics of future orbital or balloon experiments.
Multi-task Planar Reconstruction with Feature Warping Guidance
Authors: Luan Wei, Anna Hilsmann, Peter Eisert
Date: 2023-11-25 00:00:00
Tasks: 3D Reconstruction, Instance Segmentation, Multi-Task Learning, Scene Understanding, Segmentation, Semantic Segmentation

Piece-wise planar 3D reconstruction simultaneously segments plane instances and recovers their 3D plane parameters from an image, which is particularly useful for indoor or man-made environments. Efficient reconstruction of 3D planes coupled with semantic predictions offers advantages for a wide range of applications requiring scene understanding and concurrent spatial mapping. However, most existing planar reconstruction models either neglect semantic predictions or do not run efficiently enough for real-time applications. We introduce SOLOPlanes, a real-time planar reconstruction model based on a modified instance segmentation architecture which simultaneously predicts semantics for each plane instance, along with plane parameters and piece-wise plane instance masks. We achieve an improvement in instance mask segmentation by including multi-view guidance for plane predictions in the training process. This cross-task improvement, training for plane prediction but improving the mask segmentation, is due to the nature of feature sharing in multi-task learning. Our model simultaneously predicts semantics using single images at inference time, while achieving real-time predictions at 43 FPS.
Incorporating granularity bias as the margin into contrastive loss for video captioning
Authors: Jiayang Gu, Fengming Yao
Date: 2023-11-25 00:00:00
Tasks: Contrastive Learning, Sentence, Triplet, Video Captioning

Video captioning models easily suffer from long-tail distribution of phrases, which makes captioning models prone to generate vague sentences instead of accurate ones. However, existing debiasing strategies tend to export external knowledge to build dependency trees of words or refine frequency distribution by complex losses and extra input features, which lack interpretability and are hard to train. To mitigate the impact of granularity bias on the model, we introduced a statistical-based bias extractor. This extractor quantifies the information content within sentences and videos, providing an estimate of the likelihood that a video-sentence pair is affected by granularity bias. Furthermore, with the growing trend of integrating contrastive learning methods into video captioning tasks, we use a bidirectional triplet loss to get more negative samples in a batch. Subsequently, we incorporate the margin score into the contrastive learning loss, establishing distinct training objectives for head and tail sentences. This approach facilitates the model's training effectiveness on tail samples. Our simple yet effective loss, incorporating Granularity bias, is referred to as the Margin-Contrastive Loss (GMC Loss). The proposed model demonstrates state-of-the-art performance on MSRVTT with a CIDEr of 57.17, and MSVD, where CIDEr reaches up to 138.68.
Segmentation of diagnostic tissue compartments on whole slide images with renal thrombotic microangiopathies (TMAs)
Authors: Huy Q. Vo, Pietro A. Cicalese, Surya Seshan, Syed A. Rizvi, Aneesh Vathul, Gloria Bueno, Anibal Pedraza Dorado, Niels Grabe, Katharina Stolle, Francesco Pesce, Joris J. T. H. Roelofs, Jesper Kers, Vitoantonio Bevilacqua, Nicola Altini, Bernd Schröppel, Dario Roccatello, Antonella Barreca, Savino Sciascia, Chandra Mohan, Hien V. Nguyen, Jan U. Becker
Date: 2023-11-25 00:00:00
Tasks: Diagnostic, Segmentation, whole slide images

The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings. Precise diagnostic criteria for a renal biopsy diagnosis of TMA are missing. As a first step towards a machine learning- and computer vision-based analysis of wholes slide images from renal biopsies, we trained a segmentation model for the decisive diagnostic kidney tissue compartments artery, arteriole, glomerulus on a set of whole slide images from renal biopsies with TMAs and Mimickers (distinct diseases with a similar nephropathological appearance as TMA like severe benign nephrosclerosis, various vasculitides, Bevacizumab-plug glomerulopathy, arteriolar light chain deposition disease). Our segmentation model combines a U-Net-based tissue detection with a Shifted windows-transformer architecture to reach excellent segmentation results for even the most severely altered glomeruli, arterioles and arteries, even on unseen staining domains from a different nephropathology lab. With accurate automatic segmentation of the decisive renal biopsy compartments in human renal vasculopathies, we have laid the foundation for large-scale compartment-specific machine learning and computer vision analysis of renal biopsy repositories with TMAs.
Point Cloud Pre-training with Diffusion Models
Authors: Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, Yongshun Gong
Date: 2023-11-25 00:00:00
Proceeding: CVPR 2024 1
Tasks: Point Cloud Pre-training

Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However, due to the unordered and non-uniform density characteristics of point clouds, it is non-trivial to explore the prior knowledge of point clouds and pre-train a point cloud backbone. In this paper, we propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif). We consider the point cloud pre-training task as a conditional point-to-point generation problem and introduce a conditional point generator. This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object. We also present a recurrent uniform sampling optimization strategy, which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection. Specifically, PointDif attains 70.0% mIoU on S3DIS Area 5 for the segmentation task and achieves an average improvement of 2.4% on ScanObjectNN for the classification task compared to TAP. Furthermore, our pre-training framework can be flexibly applied to diverse point cloud backbones and bring considerable gains.
OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss
Authors: Zezhou Wang, Guitao Cao, Xidong Xi, Jiangtao Wang
Date: 2023-11-25 00:00:00
Tasks: Autonomous Driving, Incremental Learning, Object, object-detection, Object Detection

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the CODA dataset show that the proposed method can obtain better performance than that of the existing methods.
View-Based Luminance Mapping in Open Workplace
Authors: Guanzhou Ji, Tingsong Ou, Azadeh O. Sawyer
Date: 2023-11-25 00:00:00

This paper introduces a novel computational method for mapping indoor luminance values on the facade of an open workplace to improve its daylight performance. 180-degree fisheye renderings from different indoor locations, view positions, and times of the year are created. These renderings are then transformed from two-dimensional (2D) images into three-dimensional (3D) hemispheres. High luminance values are filtered and projected from the hemisphere to the facade surface. This framework will highlight the areas of the facade that allow too much light penetration into the interior environment. The flexible workflow allows occupant centric lighting analysis that computes multiple design parameters and synthesizes results for localized facade optimization and daylight design.
Coordinate-based Neural Network for Fourier Phase Retrieval
Authors: Tingyou Li, Zixin Xu, Yong S. Chu, Xiaojing Huang, Jizhou Li
Date: 2023-11-25 00:00:00
Tasks: Retrieval

Fourier phase retrieval is essential for high-definition imaging of nanoscale structures across diverse fields, notably coherent diffraction imaging. This study presents the Single impliCit neurAl Network (SCAN), a tool built upon coordinate neural networks meticulously designed for enhanced phase retrieval performance. Remedying the drawbacks of conventional iterative methods which are easiliy trapped into local minimum solutions and sensitive to noise, SCAN adeptly connects object coordinates to their amplitude and phase within a unified network in an unsupervised manner. While many existing methods primarily use Fourier magnitude in their loss function, our approach incorporates both the predicted magnitude and phase, enhancing retrieval accuracy. Comprehensive tests validate SCAN's superiority over traditional and other deep learning models regarding accuracy and noise robustness. We also demonstrate that SCAN excels in the ptychography setting.
GDTS: Goal-Guided Diffusion Model with Tree Sampling for Multi-Modal Pedestrian Trajectory Prediction
Authors: Ge Sun, Sheng Wang, Lei Zhu, Ming Liu, Jun Ma
Date: 2023-11-25 00:00:00
Tasks: Autonomous Driving, Denoising, Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

Accurate prediction of pedestrian trajectories is crucial for improving the safety of autonomous driving. However, this task is generally nontrivial due to the inherent stochasticity of human motion, which naturally requires the predictor to generate multi-modal prediction. Previous works leverage various generative methods, such as GAN and VAE, for pedestrian trajectory prediction. Nevertheless, these methods may suffer from mode collapse and relatively low-quality results. The denoising diffusion probabilistic model (DDPM) has recently been applied to trajectory prediction due to its simple training process and powerful reconstruction ability. However, current diffusion-based methods do not fully utilize input information and usually require many denoising iterations that lead to a long inference time or an additional network for initialization. To address these challenges and facilitate the use of diffusion models in multi-modal trajectory prediction, we propose GDTS, a novel Goal-Guided Diffusion Model with Tree Sampling for multi-modal trajectory prediction. Considering the "goal-driven" characteristics of human motion, GDTS leverages goal estimation to guide the generation of the diffusion network. A two-stage tree sampling algorithm is presented, which leverages common features to reduce the inference time and improve accuracy for multi-modal prediction. Experimental results demonstrate that our proposed framework achieves comparable state-of-the-art performance with real-time inference speed in public datasets.
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism
Authors: Zhen Wang, Xinyun Jiang, Jun Xiao, Tao Chen, Long Chen
Date: 2023-11-25 00:00:00
Tasks: Caption Generation, Denoising, Image Captioning

Explicit Caption Editing (ECE) -- refining reference image captions through a sequence of explicit edit operations (e.g., KEEP, DETELE) -- has raised significant attention due to its explainable and human-like nature. After training with carefully designed reference and ground-truth caption pairs, state-of-the-art ECE models exhibit limited generalization ability beyond the original training data distribution, i.e., they are tailored to refine content details only in in-domain samples but fail to correct errors in out-of-domain samples. To this end, we propose a new Diffusion-based Explicit Caption editing method: DECap. Specifically, we reformulate the ECE task as a denoising process under the diffusion mechanism, and introduce innovative edit-based noising and denoising processes. Thanks to this design, the noising process can help to eliminate the need for meticulous paired data selection by directly introducing word-level noises for training, learning diverse distribution over input reference caption. The denoising process involves the explicit predictions of edit operations and corresponding content words, refining reference captions through iterative step-wise editing. To further efficiently implement our diffusion process and improve the inference speed, DECap discards the prevalent multi-stage design and directly generates edit operations and content words simultaneously. Extensive ablations have demonstrated the strong generalization ability of DECap in various scenarios. More interestingly, it even shows great potential in improving the quality and controllability of caption generation.
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies
Authors: Hongwei Bran Li, Matthew S. Rosen, Shahin Nasr, Juan Eugenio Iglesias
Date: 2023-11-25 00:00:00
Tasks: Image Augmentation, Super-Resolution

High-resolution fMRI provides a window into the brain's mesoscale organization. Yet, higher spatial resolution increases scan times, to compensate for the low signal and contrast-to-noise ratio. This work introduces a deep learning-based 3D super-resolution (SR) method for fMRI. By incorporating a resolution-agnostic image augmentation framework, our method adapts to varying voxel sizes without retraining. We apply this innovative technique to localize fine-scale motion-selective sites in the early visual areas. Detection of these sites typically requires a resolution higher than 1 mm isotropic, whereas here, we visualize them based on lower resolution (2-3mm isotropic) fMRI data. Remarkably, the super-resolved fMRI is able to recover high-frequency detail of the interdigitated organization of these sites (relative to the color-selective sites), even with training data sourced from different subjects and experimental paradigms -- including non-visual resting-state fMRI, underscoring its robustness and versatility. Quantitative and qualitative results indicate that our method has the potential to enhance the spatial resolution of fMRI, leading to a drastic reduction in acquisition time.