Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with approximate top-$k$ ranking objectives, as thye naturally fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset outperforms the conventional visual features by about 10%, obtaining the best reported performance in the literature.
This work addresses the task of multilabel image classification. Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visual-semantic embedding, we exploit extending these models for multilabel images. Specifically, we propose an image-dependent ranking model, which returns a ranked list of labels according to its relevance to the input image. In contrast to conventional CNN models that learn an image representation (i.e. the image embedding vector), the developed model learns a mapping (i.e. a transformation matrix) from an image in an attempt to diffe…
Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. We build on this intuition to improve multilabel image annotation. Our model uses image metadata nonparametrically to generate neighborhoods of related images using Jaccard similarities, then uses a deep neural network to blend visual information from the image and its neighbors. Prior work typically models image metadata parametrically, in contrast, our nonparametric treatment allows our model to perform well even when the voca…
Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantita…
National Center for Biotechnology Information, National Library of Medicine（美国国家医学图书馆国家生物技术信息中心）
In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and describe them in the radiology report. In this paper, we study the lesion description or annotation problem. Given a lesion image, our aim is to predict a comprehensive set of relevant labels, such as the lesion's body part, type, and attributes, which may assist downstream fine-grained diagnosis. To address this task, we first design a deep learning module to extract relevant semantic labels from the radiology reports associated with the lesion images. With the images and t…
Guangdong Polytechnic Normal University（广东技术师范大学）；Guangdong University of Technology（广东工业大学）
In deep learning area, large-scale image datasets bring a breakthrough in the success of object recognition and retrieval. Nowadays, as the embodiment of innovation, the diversity of the industrial goods is significantly larger, in which the incomplete multiview, multimodal and multilabel are different from the traditional dataset. In this paper, we introduce an industrial goods dataset, namely PatentNet, with numerous highly diverse, accurate and detailed annotations of industrial goods images, and corresponding texts. In PatentNet, the images and texts are sourced from design patent. Within …
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；University of Copenhagen（哥本哈根大学）；Copenhagen Academy of Medical Education and Simulation (CAMES), Rigshospitalet（哥本哈根医学教育与模拟学院（CAMES），国立医院（Rigshospitalet））；Pioneer Centre for AI, Copenhagen（哥本哈根人工智能先锋中心）
Wuhan University（武汉大学）；Sun Yat-Sen University（中山大学）；University of South Carolina（南卡罗来纳大学）
Hash coding has been widely used in approximate nearest neighbor search for large-scale image retrieval. Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes. While some newly introduced images may contain undefined semantic labels, which we call unseen images, zeor-shot hashing techniques have been studied. However, existing zeor-shot hashing methods focus on the retrieval of single-label images, and cannot handle multi-label images. In this paper, for the first time, a novel t…
Most existing approaches to training object detectors rely on fully supervised learning, which requires the tedious manual annotation of object location in a training set. Recently there has been an increasing interest in developing weakly supervised approach to detector training where the object location is not manually annotated but automatically determined based on binary (weak) labels indicating if a training image contains the object. This is a challenging problem because each image can contain many candidate object locations which partially overlaps the object of interest. Existing appro…
Vehicle Re-identification is a challenging task due to intra-class variability and inter-class similarity across non-overlapping cameras. To tackle these problems, recently proposed methods require additional annotation to extract more features for false positive image exclusion. In this paper, we propose a model powered by adaptive attention modules that requires fewer label annotations but still out-performs the previous models. We also include a re-ranking method that takes account of the importance of metadata feature embeddings in our paper. The proposed method is evaluated on CVPR AI Cit…
Multilabel Classification for Lung Disease Detection: Integrating Deep Learning and Natural Language Processing
Mechanical Engineering Staten Island Tech High School（Staten Island Tech High School机械工程系）；Computer Science Monta Vista High School（Monta Vista High School计算机科学系）；Computer Science Lambert High School（Lambert High School计算机科学系）；Electrical Engineering Edison Academy Magnet School（Edison Academy Magnet School电气工程系）
Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking
Xiamen University（厦门大学）；Nanjing University Of Aeronautics and Astronautics（南京航空航天大学）；Tencent Technology（腾讯科技）；University of Cyprus（塞浦路斯大学）；Cardiff University（卡迪夫大学）
Data-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
School of Computer Science and Informatics, University of Liverpool（利物浦大学计算机科学与信息学学院）；German Aerospace Center (DLR), Remote Sensing Technology Institute（德国航天中心（DLR）遥感技术研究所）；Division of Geoinformatics, KTH Royal Institute of Technology（皇家理工学院地理信息系）；University College London（伦敦大学学院）；University of Exeter（埃克塞特大学）；Inria（法国国家信息与自动化技术研究院）；German Aerospace Center (DLR), Microwaves and Radar Institute（德国航天中心（DLR）微波与雷达研究所）
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）
Ranking by pairwise comparisons has shown improved reliability over ordinal classification. However, as the annotations of pairwise comparisons scale quadratically, this becomes less practical when the dataset is large. We propose a method for reducing the number of pairwise comparisons required to rank by a quantitative metric, demonstrating the effectiveness of the approach in ranking medical images by image quality in this proof of concept study. Using the medical image annotation software that we developed, we actively subsample pairwise comparisons using a sorting algorithm with a human r…
Canadian Institute for Advanced Research（加拿大高等研究院）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Guelph（圭尔夫大学）；Apple Inc（苹果公司）
Deep neural networks require collecting and annotating large amounts of data to train successfully. In order to alleviate the annotation bottleneck, we propose a novel self-supervised representation learning approach for spatiotemporal features extracted from videos. We introduce Skip-Clip, a method that utilizes temporal coherence in videos, by training a deep model for future clip order ranking conditioned on a context clip as a surrogate objective for video future prediction. We show that features learned using our method are generalizable and transfer strongly to downstream tasks. For acti…
DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often used for clothes recognition and although it provides comprehensive annotations, the attributes distribution is unbalanced and repetitive specially for training fine-grained attribute recognition models. In this work, we tailored DeepFashion for fine-grained attribute recognition task by focusing on each category separately. After selecting categories with sufficient number of images for training, we remove very sca…
University of Michigan（密歇根大学）；Indian Institute of Technology - Bombay（印度理工学院孟买分校）
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved. This burden of annotation can be alleviated by exploiting weak inexpensive annotations such as bounding boxes and anatomical landmarks. However, it is very difficult to \textit{a priori} estimate the optimal balance between the number of annotations needed for each supervision type that leads to maximum performance with the least annotation cost. To optimize this cost-performa…
Label-Free Foundational Model Selection for Medical Image Classification under Distribution Shift via Pseudo Label Discrepancy
Universidad de Buenos Aires（布宜诺斯艾利斯大学）；CONICET（阿根廷国家科学技术研究委员会）；Universidad Nacional del Litoral（国立 littoral 大学）；Research Institute for Signals, Systems and Computational Intelligence（信号、系统与计算智能研究所）
A movie's key moments stand out of the screenplay to grab an audience's attention and make movie browsing efficient. But a lack of annotations makes the existing approaches not applicable to movie key moment detection. To get rid of human annotations, we leverage the officially-released trailers as the weak supervision to learn a model that can detect the key moments from full-length movies. We introduce a novel ranking network that utilizes the Co-Attention between movies and trailers as guidance to generate the training pairs, where the moments highly corrected with trailers are expected to …
University of Konstanz（康斯坦茨大学）；University of Stuttgart（斯图加特大学）
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation and then comparing the interpolated frames with the actual frames. In the latter case, objective quality measures such as the mean squared error are typically employed. However, it is well known that for image quality assessment, the actual quality experienced by the user cannot be fully deduced from such simple measures. Hence, we conducted a subjective quality assessment cro…
Disney Research Pittsburgh（迪士尼研究院匹兹堡分院）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Learning a joint language-visual embedding has a number of very appealing properties and can result in variety of practical application, including natural language image/video annotation and search. In this work, we study three different joint language-visual neural network model architectures. We evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1) Standard Ranking for video annotation and retrieval 2) Our proposed movie multiple-choice test. This test facilitate automatic evaluation of visual-language models for natural language video annotation based on human activitie…
Queen Mary University of London（伦敦玛丽女王大学）；Peking University（北京大学）
The problem of estimating subjective visual properties from image and video has attracted increasing interest. A subjective visual property is useful either on its own (e.g. image and video interestingness) or as an intermediate representation for visual recognition (e.g. a relative attribute). Due to its ambiguous nature, annotating the value of a subjective visual property for learning a prediction model is challenging. To make the annotation more reliable, recent studies employ crowdsourcing tools to collect pairwise comparison labels because human annotators are much better at ranking two …
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Indian Institute of Technology（印度理工学院）；Axogyan AI（Axogyan人工智能公司）
Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on the estimated 2D-pose. We show that CVAE-based 3D-pose sample set is consistent with the 2D-pose and helps tackling the inherent ambiguity in 2D-to-3D lifting. We propose two strategies for obtaining the final 3D pose- (a) depth-ordering/ordinal relations to score and weight-ave…
