Pruning unnecessary regions from weight kernels and filters used in convolutional nerual networks has proven to be effective for model compression. However, prior studies have often over-optimized for generic application areas, which are not ideal for specific problems in salient object detection. In this study, a novel pruning strategy denoted advantage rank (ARank) is developed for multiple salient

Occluded person re-identification (ReID) is a challenging task, which aims at retrieving an occluded person across multiple non-overlapping cameras. To address this issue, we propose a novel framework named Shrinking and Reweighting Network (SRNet) that jointly learns global features by shrinking and reweights part features for matching in an end-to-end framework. Specifically, we use a strong backbone

Unsupervised domain adaptation (UDA) for person re-identication (ReID) remains a challenging task, as the trained ReID system often fails to adapting to a new dataset. Due to the lack of supervision of real labels, the performance of the UDA models suffers from inefficient feature learning and inevitable pseudo label noise. In this work, we tackle the problems by designing an effective dual-path mutual-learning

For reality applications of semantic segmentation, inference speed and memory usage are two important factors. To address these challenges, we propose a lightweight feature pyramid encoding network (FPENet) for semantic segmentation with a good trade-off between accuracy and speed. We use a series of feature pyramid encoding (FPE) blocks to encode context at multiple scales in the encoder. Each FPE

Facial alignment is an essential task for many higher level facial analysis applications, such as animation, human activity recognition and human - computer interaction. Although the recent availability of big datasets and powerful deep-learning approaches have enabled major improvements on the state of the art accuracy, the performance of current approaches can severely deteriorate when dealing with

In the recent years, Convolutional Neural Network-based object detection has experienced impressive progress. Despite these improvements, the performance on the small object detection leaves much to be desired, small objects are often missed or wrongly detected. The reasonable explanation is that the supervisory signals on the small objects are insufficient, and through the analysis of loss distribution

In this paper, we consider the problem of domain adaptation for multi-class classification, where we are provided a labeled set of examples in a source dataset and target dataset with no supervision. We tackle the mode collapse problem in adapting the classifier across domains. In this setting, we propose an adversarial learning-based approach using an informative discriminator. Our observation relies

In recent years, discriminative correlation filters (DCF) and Siamese networks based trackers have significantly advanced the performance in tracking. However, the problem of accurate target state estimation is not fully solved yet. Therefore, in this paper, we propose a Criss-Cross Location Attention (CCLA) module, which pays more concerns to global and local contextual information and is used for

Raw point clouds obtained from real-world scanning are always incomplete and ununiformly distributed, which would result in structural losses of object shapes and bring about difficulties in further high-level 3D vision tasks. Therefore, a learning-based method called CRA-Net is proposed in this paper to repair partial point clouds and predict complete object shapes. Compared with most existing networks

Text-based person re-identification (Re-id) is an important task in video surveillance, which consists of retrieving the corresponding person's image given a textual description from a large gallery of images. It is difficult to directly match visual contents with the textual descriptions due to the modality heterogeneity. On the one hand, the textual embedding are not discriminative enough, which

We present a new network architecture able to take advantage of spatio-temporal information available in videos to boost object detection precision. First, box features are associated and aggregated by linking proposals that come from the same anchor box in the nearby frames. Then, we design a new attention module that aggregates short-term enhanced box features to exploit long-term spatio-temporal

Classification and regression-based trackers (CAR) are widely adopted to tackle the short-term visual tracking task. However, the existing CAR tackers either employ offline-trained regression models based on predefined anchor-boxes, or online update their models in a rough and inflexible way, which leads to the lack of long-term adaptability for target deformations and appearance variations. To overcome

This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed

An effective semantic segmentation approach should contain fine object boundaries and continuous regions. However, recent mask-based segmentation cannot extract boundary features well on the coarse prediction, which causes obvious problems of blurry edges. Although several segmentation methods embed boundary detection branch to calculate contours directly, this type of architecture will lead to the

The per-frame segmentation methods have a high computational cost, thereby, these methods are insufficient to cope with the fast inference need of semantic video segmentation. To efficaciously reuse the extracted features by feature propagation, in this paper, we present distortion-aware feature rectification and online selection of keyframes for fast and accurate video segmentation. The proposed dynamic

Fully convolutional network (FCN) based salient object detection methods have shown their advantages in highlighting salient regions because they can obtain global semantic information. And the high-level semantics are usually passed in a top-down pathway. However, the semantic information would be diluted progressively among different level features. To alleviate this issue, we propose a novel edge-aware

Personality computing and affective computing, where the recognition of personality traits is essential, have gained increasing interest and attention in many research areas recently. We propose a novel approach to recognize the Big Five personality traits of people from videos. To this end, we use four different modalities, namely, ambient appearance (scene), facial appearance, voice, and transcribed

Visual Question Answering (VQA) has recently appeared as a hot research area in the field of computer vision and natural language processing. A VQA model uses both image and question features and fuses them to predict an answer for a given natural question related to an image. However, most VQA approaches using attention mechanism mainly concentrate on extraction of visual information from regions

Learning from limited supervision is a challenging problem that has recently attracted wide attention in the machine learning community. With scarce annotated samples available in target categories, so-called few-shot image recognition aims to transfer basic knowledge from a large-scale image set to recognize unseen classes. Many existing approaches tend to learn a general source data representation

A power substation is susceptible to intrusions of foreign objects. The intrusions can likely result in failures of power supplies. Therefore, recognizing foreign objects becomes important to ensure constant and stable power supplies. However, existing object recognition methods fail to achieve acceptable accuracy and performance. In this paper, we propose an efficient Foreign Objects Detection Network

Semantic segmentation and instance segmentation based on 3D point clouds involve significant challenges, specifically in the task of joint semantic and instance segmentation. The efficient and effective mutual assistance between semantic and instance segmentation is rarely considered and still remains an unaddressed research problem. To address this, herein, a novel and robust 3D point cloud segmentation

Network convergence as well as recognition accuracy are essential issues when applying Convolutional Neural Networks (CNN) to human action recognition. Most deep learning methods neglect model convergence when striving to improve the abstraction capability, thus degrading the performances sharply when computing resources are limited. To mitigate this problem, we propose a structure named 2D Progressive

We propose an approach for quickly building a visual representation of a full indoor building. Our goal is to enable intelligent systems which frequently and regularly monitor buildings to assist personnel operating remotely, a need of special importance in these days. Prior work in neural scene representations for view synthesis focuses on single objects and small scenes and does not scale to full

Efficient semantic segmentation of large-scale three-dimensional (3D) point clouds is an essential approach for intelligent robots to perceive the surrounding environment. However, due to the expensive sampling process or time-consuming pre/post-processing steps, most of the current solutions are inefficient or limited in scale. In this paper, we propose a novel framework, ThickSeg, to efficiently

In this paper, we propose a novel framework to segment 3D human motion capture data into distinct behaviors. First, in preprocessing, we build a normalized pose space by eliminating translation and orientation from the 3D poses. We then transform these normalized 3D poses into 2D RGB images, and as a result, we simplify the task of motion segmentation as image classification and recognition. Furthermore

Visual attention mechanism has been widely used by image captioning model in order to dynamically attend to the related visual region based on given language information. Such capability allows a trained model to carry out fine-grained level image understanding and reasoning. However, existing visual attention models only focus on the individual visual region in the image and the alignment between

This paper proposes a deep convolutional neural network with a concise and effective encoder-decoder architecture for saliency prediction. Local and global contextual features make a considerable contribution to saliency prediction. In order to integrate and exploit these features more thoroughly, in the proposed pithy architecture, we deploy a dense and global context connection structure between

The model pruning is one of the predominant model compression tasks to decrease the demands in computing power and memory footprint. However, most existing pruning methods have overly broad application areas, which defects in a sub-optimal solution specifically to solve certain specified difficult problems in the tasks of salient object detection. In this paper, we propose a novel solution, dubbed

Recently, many efforts have been made to model spatial–temporal features from human skeleton for action recognition by using graph convolutional networks (GCN). Skeleton sequence can precisely represent human pose with a small number of joints while there is still a lot of redundancies across the skeleton sequence in the term of temporal dependency. In order to improve the effectiveness of spatial–temporal

Estimating the 6D object pose based on a monocular RGB image is a challenging task in computer vision, which produces false positives under the influence of occlusion or cluttered environments. In addition, the prediction of translation is affected by changes of the image size. In this work, we present a novel two-stage method TGCPose6D for robust 6DoF object pose estimation which is composed of 2D

Data set of high quality is the cornerstone of the current data-driven machine learning models, and plays an important role in promoting the development of various application areas. At present, image analysis and processing techniques have intensively involved into the tasks of inheriting and protecting culture resources. However, currently there are few effective image data sets about the traditional

Domain adaptation is an important technology for transferring source domain knowledge to new, unseen target domains. Recently, domain adaptation models are applied to learn domain invariant representations by minimizing distribution distance or adversarial training in the feature space. However, existing adversarial domain adaptation methods fail to preserve the data structure in the feature space

Generative adversarial networks (GANs) have been extensively studied in recent years and have been used to address several problems in the fields of image generation and computer vision. Despite significant advancements in computer vision, applying GANs to real-world problems such as 3D face generation remains a challenge. Owing to the proliferation of fake images generated by GANs, it is important

Behavior recognition is a well-known computer vision mobile technology. It has been used in many applications such as video surveillance, motion detection on devices, human-computer interaction and sports video, etc. However, most of the existing works ignored the depth and spatio-temporal information so that they resulted in over-fitting and inferior performance. Consequently, a novel framework for

In the field of detection, there is a wide gap between the performance of small objects and that of medium, large objects. Some studies show that this gap is due to the contradiction between the classification-based backbone and localization. Although the reduction in the feature map size is beneficial for the extraction of abstract features, it will cause the loss of detailed features in the localization

Visible-Infrared person re-IDentification (VI-reID) aims to automatically retrieve the pedestrian of interest exposed to sensors in different modalities, such as visible camera v.s. infrared sensor. It struggles to learn both modality-invariant and discriminant representations. Unfortunately, existing VI-reID work mainly focuses on tackling the modality difference, which fine-grained level discriminant

Image captioning is the task of generating captions of images in natural language. The training typically consists of two phases, first minimizing the XE (cross-entropy) loss, and then with RL (reinforcement learning) over CIDEr scores. Although there are many innovations in neural architectures, fewer works are proposed for the RL phase. Motivated by one recent state-of-the-art architecture X-Transformer

Object detection is a crucial task in computer vision systems with a wide range of applications in autonomous driving, medical imaging, retail, security, face recognition, robotics, and others. Nowadays, neural networks-based models are used to localize and classify instances of objects of particular classes. When real-time inference is not required, ensembles of models help to achieve better results

Image composition aims to composite a material region into a target image. Using this technique, more images could be interactive, as there are millions of images created daily in modern life. However, it is difficult for the majority of traditional composition methods to retrieve relatively realistic semantically valid material images. Also, even if the minority of traditional composition methods

The increasing availability of extensive digitized fine art collections opens up new research directions. In particular, correctly identifying the artistic style or art movement of paintings is crucial for large artistic database indexing, painter authentication, and mobile recognition of painters. Even though the implementation of CNN on artwork classification improved the performance dramatically

Predicting future actions is a key in visual understanding, surveillance, and human behavior analysis. Current methods for video-based prediction are primarily using single-view data, while in the real world multiple cameras and produced videos are readily available, which may potentially benefit the action prediction tasks. However, it may bring up a new challenge: subjects in the videos are more

Predicting pedestrian trajectory is useful in many applications, such as autonomous driving and unmanned vehicles. However, it is a challenging task because of the complexity of the interactions among pedestrians and the environment. Most existing works employ long short-term memory networks to learn pedestrian behaviors, but their prediction accuracy is not good, and their computing speed is relatively

How to effectively utilize and fuse deep features has become a critical point for salient object detection. Most existing methods usually adopt the convolutional features based on U-shape structures and fuse multi-scale convolutional features without fully considering the different characteristics between high-level features and low-level features. Furthermore, existing salient object detection methods

As a practical pursuit of quantified uniqueness, biometrics explores the parameters that make us who we are and provides the tools we need to secure the integrity of that identity. In our culture of constant connectivity, an increasing reliance on biometrically secured mobile devices is transforming them into a target for bad actors. While no system will ever prevent all forms of intrusion, even state

In this paper, we propose a Motion Saliency based multi-stream Multiplier ResNets (MSM-ResNets) for action recognition. The proposed MSM-ResNets model consists of three interactive streams: the appearance stream, motion stream and motion saliency stream. Similar to conventional two-stream CNNs models, the appearance stream and motion stream are responsible for capturing the appearance information and

In the last few years, squared fiducial markers have become a popular and efficient tool to solve monocular localization and tracking problems at a very low cost. Nevertheless, marker detection is affected by noise and blur: small camera movements may cause image blurriness that prevents marker detection. The contribution of this paper is two-fold. First, it proposes a novel approach for estimating