We present a method to learn a diverse group of object categories from an unordered point set. We propose our Pyramid Point network, which uses a dense pyramid structure instead of the traditional 'U' shape, typically seen in semantic segmentation networks. This pyramid structure gives a second look, allowing the network to revisit different layers simultaneously, increasing the contextual information by creating additional layers with less noise. We introduce a Focused Kernel Point convolution (FKP Conv), which expands on the traditional point convolutions by adding an attention mechanism to the kernel outputs. This FKP Conv increases our feature quality and allows us to weigh the kernel outputs dynamically. These FKP Convs are the central part of our Recurrent FKP Bottleneck block, which makes up the backbone of our encoder. With this distinct network, we demonstrate competitive performance on three benchmark data sets. We also perform an ablation study to show the positive effects of each element in our FKP Conv.
Pohang University of Science and Technology (POSTECH)（浦项科技大学）
Salient object detection (SOD) has been in the spotlight recently, yet has been studied less for high-resolution (HR) images. Unfortunately, HR images and their pixel-level annotations are certainly more labor-intensive and time-consuming compared to low-resolution (LR) images and annotations. Therefore, we propose an image pyramid-based SOD framework, Inverse Saliency Pyramid Reconstruction Network (InSPyReNet), for HR prediction without any of HR datasets. We design InSPyReNet to produce a strict image pyramid structure of saliency map, which enables to ensemble multiple results with pyramid…
The Chinese University of Hong Kong（香港中文大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；HKUST（香港科技大学）；Sun Yat-Sen University（中山大学）
We present a flexible and high-performance framework, named Pyramid R-CNN, for two-stage 3D object detection from point clouds. Current approaches generally rely on the points or voxels of interest for RoI feature extraction on the second stage, but cannot effectively handle the sparsity and non-uniform distribution of those points, and this may result in failures in detecting objects that are far away. To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest. The pyramid RoI head consists of th…
University of Electronic Science and Technology of China（电子科技大学）；Shenzhen Institute for Advanced Study（深圳高等研究院）
Average precision (AP) loss has recently shown promising performance on the dense object detection task. However,a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed.In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples.Based on this observation, we propose two strategies to improve the AP loss. The first of these is a novel Adaptive Pairwise Error (APE) loss that focusing on ranking pairs in both positive and nega…
Revisiting Uncertainty Estimation and Calibration of Large Language Models
School of Computer Science University of Sydney（悉尼大学计算机科学学院）；City University of Hong Kong（香港城市大学）；Shanghai Jiao Tong University（上海交通大学）；Department of Engineering Science University of Oxford（牛津大学工程科学系）
Predicting the mutation status of genes in tumors is of great clinical significance. Recent studies have suggested that certain mutations may be noninvasively predicted by studying image features of the tumors from Computed Tomography (CT) data. Currently, this kind of image feature identification method mainly relies on manual processing to extract generalized image features alone or machine processing without considering the morphological differences of the tumor itself, which makes it difficult to achieve further breakthroughs. In this paper, we propose a pyramid focusing network (PFNet) fo…
The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）
Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted to address the problem using convolutional neural network (CNN) and have showed promising results. FlowNet2, the state-of-the-art CNN, requires over 160M parameters to achieve accurate flow estimation. Our LiteFlowNet2 outperforms FlowNet2 on Sintel and KITTI benchmarks, while being 25.3 times smaller in the model size and 3.1 times faster in the running speed. LiteFlowNet2 is built on the foundation laid by conventio…
Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization ta…
University of Chinese Academy of Sciences（中国科学院大学）；University of California at Berkeley（加州大学伯克利分校）；BeiHang University（北京航空航天大学）
Pedestrian detection in the wild remains a challenging problem especially when the scene contains significant occlusion and/or low resolution of the pedestrians to be detected. Existing methods are unable to adapt to these difficult cases while maintaining acceptable performance. In this paper we propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects: focusing on it again, at a finer scale, if the object can not be identified clearly for the first time. CircleNet is implement…
Peking University（北京大学）；Deepwise AI Lab（深智城人工智能实验室）；The University of Hong Kong（香港大学）
Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple adjacent slices, 3D context modeling is of great significance for developing automated lesion detection algorithms. In this work, we propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) that leverages depthwise separable convolutional filters and a group transform module (GTM) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. To facilitate faster convergence, a novel 3D network pre…
Department of Computer Science, Texas State University, USA（德克萨斯州立大学计算机科学系，美国（注：原机构名中Department of Computer Science属于系级别，按要求提取到大学级别，即Texas State University，美国德克萨斯州立大学））
Nowadays 360 video analysis has become a significant research topic in the field since the appearance of high-quality and low-cost 360 wearable devices. In this paper, we propose a novel LiteFlowNet360 architecture for 360 videos optical flow estimation. We design LiteFlowNet360 as a domain adaptation framework from perspective video domain to 360 video domain. We adapt it from simple kernel transformation techniques inspired by Kernel Transformer Network (KTN) to cope with inherent distortion in 360 videos caused by the sphere-to-plane projection. First, we apply an incremental transformation…
The City College of New York（纽约城市学院）；The City College（城市学院）；The Graduate Center, the City University of New York（纽约城市大学研究生中心）
The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation, especially for scenes in the wild with varieties of different objects. To alleviate this issue, we propose a novel deep graph convolutional network-based framework for large-scale semantic scene segmentation in point clouds with sole 2D supervision. Different with numerous preceding multi-view supervised approaches focusing on single object point clouds, we argue that 2D supervision is capable of providing sufficient guidance information for training 3D semantic segmentation models of n…
University of Freiburg（弗莱堡大学）；Toyota Research Institute（丰田研究院）
Learning to reliably perceive and understand the scene is an integral enabler for robots to operate in the real-world. This problem is inherently challenging due to the multitude of object types as well as appearance changes caused by varying illumination and weather conditions. Leveraging complementary modalities can enable learning of semantically richer representations that are resilient to such perturbations. Despite the tremendous progress in recent years, most multimodal convolutional neural network approaches directly concatenate feature maps from individual modality streams rendering t…
Rectified SpaAttn: Revisiting Attention Sparsity for Efficient Video Generation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
PLUG: Revisiting Amodal Segmentation with Foundation Model and Hierarchical Focus
National Engineering Research Center of Visual Technology（国家视觉技术工程研究中心）；National Key Laboratory for Multimedia Information Processing（多媒体信息处理国家重点实验室）；School of Computer Science, Peking University（北京大学计算机学院）；AI Innovation Center, School of Computer Science, Peking University（北京大学计算机学院人工智能创新中心）；Meituan Inc.（美团公司）；National Biomedical Imaging Center, Peking University（北京大学生物医学成像中心）
Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework
VCIP, School of Computer Science, Nankai University（VCIP，计算机科学学院，南开大学）；Huazhong University of Science and Technology（华中科技大学）；Nankai International Advanced Research Institute (Shenzhen Futian)（南开国际先进研究院（深圳福田））
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Dept. of Computer Science, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）；AI Center, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院人工智能中心）；Signal Processing Laboratory (LTS4), EPFL, Lausanne, Switzerland（日内瓦联邦理工学院信号处理实验室）；University of Basel, Basel, Switzerland（巴塞尔大学）；University Hospital of Basel, Basel, Switzerland（巴塞尔大学医院）；Idiap Research Institute, Martigny, Switzerland（伊迪普研究 institute）；Icahn School of Medicine at Mount Sinai, New York, United States（辛辛那提医学中心伊坎医学院）
Despite the growing success of Convolution neural networks (CNN) in the recent past in the task of scene segmentation, the standard models lack some of the important features that might result in sub-optimal segmentation outputs. The widely used encoder-decoder architecture extracts and uses several redundant and low-level features at different steps and different scales. Also, these networks fail to map the long-range dependencies of local features, which results in discriminative feature maps corresponding to each semantic class in the resulting segmented image. In this paper, we propose a n…
Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Institute of Sensors, Signals and Systems, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom（传感器、信号与系统研究所，赫里奥特-瓦特大学）；EPCC, University of Edinburgh, Potterrow, Edinburgh EH8 9BT, United Kingdom（爱丁堡大学埃平中心）
Human action recognition is an active research area in computer vision. Although great process has been made, previous methods mostly recognize actions based on depth data at only one scale, and thus they often neglect multi-scale features that provide additional information action recognition in practical application scenarios. In this paper, we present a novel framework focusing on multi-scale motion information to recognize human actions from depth video sequences. We propose a multi-scale feature map called Laplacian pyramid depth motion images(LP-DMI). We employ depth motion images (DMI) …
