Query: attention object detection

title: Faster R CNN: Towards Real Time Object Detection with Region Proposal Networks
abstract: State of the art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full image convolutional features with the detection network, thus enabling nearly cost free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end to end to generate high quality region proposals, which are used by Fast R CNN for detection. We further merge RPN and Fast R CNN into a single network by sharing their convolutional features using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG 16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state of the art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R CNN and RPN are the foundations of the 1st place winning entries in several tracks. Code has been made publicly available

title: Multi Attention Object Detection Model in Remote Sensing Images Based on Multi Scale
abstract: Ground object detection, based on remote sensing satellite imagery, provides the groundwork of numerous applications, so the detection accuracy is of vital importance. The background of remote sensing images is complex, the object size is various, and there are many small objects. In view of the above problems, a multi attention object detection method (MA FPN) based on multi scale is proposed in this paper, which can effectively make the network pay attention to the location of the object and reduce the loss of small object information. According to feature pyramid network (FPN) we firstly put forward a global spatial attention module, which extracts spatial location related information from shallow features and fuses it with deep features to enhance the position expression ability of deep features. Besides, the paper provides a pixel feature attention module: the multi scale convolution kernel is employed to generate the feature map of the same size as the input, as well as channel attention is used to assign weights to each layer of feature maps to obtain pixel level attention maps with good details. Experiments on NWPU, RSOD, and DOTA datasets show that the proposed algorithm outperforms state of the art methods.

title: Few Shot Object Detection With Attention RPN and Multi Relation Detector
abstract: Conventional methods for object detection typically require a substantial amount of training data and preparing such high quality training data is very labor intensive. In this paper, we propose a novel few shot object detection network that aims at detecting objects of unseen categories with only a few annotated examples. Central to our method are our Attention RPN, Multi Relation Detector and Contrastive Training strategy, which exploit the similarity between the few shot support set and query set to detect novel objects while suppressing false detection in the background. To train our network, we contribute a new dataset that contains 1000 categories of various objects with high quality annotations. To the best of our knowledge, this is one of the first datasets specifically designed for few shot object detection. Once our few shot network is trained, it can detect objects of unseen categories without further training or fine tuning. Our method is general and has a wide range of potential applications. We produce a new state of the art performance on different datasets in the few shot setting. The dataset link is https:/github.com/fanq15/Few Shot Object Detection Dataset.

title: Prime Sample Attention in Object Detection
abstract: It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP. Our study suggests that the samples in each mini batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily result in higher mAP. Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance. We further develop a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) that directs the focus of the training process towards such samples. Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector. Particularly, on the MSCOCO dataset, PISA outperforms the random sampling baseline and hard mining schemes, \eg~OHEM and Focal Loss, consistently by around 2\ on both single stage and two stage detectors, even with a strong backbone ResNeXt 101. Code is available at: \url{https:/github.com/open mmlab/mmdetection}

title: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention
abstract: In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g. Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse to Fine Regression (CFR) module. By considering the channel wise, point wise and voxel wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e. adding additional random noisy points around each object,the presented approach goes far beyond state of the art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.

title: Embedding Attention and Residual Network for Accurate Salient Object Detection
abstract: Salient object detection is usually used as a preprocessing step to facilitate a variety of subsequent applications which should take little time cost. With the quick development of deep learning recently, profound progresses have been made to achieve a new state of the art performance. However, the learned features of the existing deep learning based methods are not accurate enough thus leading to unsatisfactory detection in complex scenes, such as low contrast or very similar between salient object and background region and multiple (small) salient objects with diverse characteristics. In addition, some post processing techniques are usually needed for refinement, which is time consuming. To address these issues, this paper presents an efficient fully convolutional salient object detection network. Specifically, we first introduce a visual attention mechanism to guide feature learning in side output layers. In detail, attention weight is employed in a top down manner which can bridge high level semantic information to help shallow layers better locate salient objects and also filter out noisy response in the background region. Second, we propose a residual refinement network to fuse the learned multilevel features gradually. Not to simply add or concatenate them step by step as previous works, we introduce a second order term into element wise addition to learn stage wise residual features for refinement. Such a second order term not only benefits efficient gradient propagation but also increases network nonlinearity. Extensive experiments on seven standard benchmarks demonstrate that the proposed approach achieves consistently superior performance and performs well on small salient object detection in comparison with the very recent state of the arts, especially in the metric of structure measure.

title: SARPNET: Shape attention regional proposal network for liDAR based 3D object detection
abstract: Real time 3D object detection is a fundamental technique in numerous applications, such as autonomous driving, unmanned aerial vehicles (UAV) and robot vision. However, current LiDAR based 3D object detection algorithms allocate inadequate attention to the inhomogeneity of LiDAR point clouds and the shape encoding capability of regional proposal schemes. This paper introduces a novel 3D object detection network called the Shape Attention Regional Proposal Network (SARPNET) which deploys a new low level feature encoder to remedy the sparsity and inhomogeneity of LiDAR point clouds with an even sample method, and embodies a shape attention mechanism that learns the statistic 3D shape priors of objects and uses them to spatially enhance semantic embeddings. Experimental results show that the proposed one stage method outperforms state of the art one stage and even two stage methods on the KITTI 3D object detection benchmark. It achieved a BEV AP of (87.26% 62.80% 3D AP of (75.64% 60.43% and orientation AP of (88.86% 71.01% for the detection of cars and cyclists, respectively. Besides, the method is the third winner in the nuScenes 3D Detection challenge of CVPR2019 Workshop on Autonomous Driving (WAD)

title: Shifting More Attention to Video Salient Object Detection
abstract: The last decade has witnessed a growing interest in video salient object detection (VSOD) However, the research community long term lacked a well established VSOD dataset representative of real dynamic scenes with high quality annotations. To address this issue, we elaborately collected a visual attention consistent Densely Annotated VSOD (DAVSOD) dataset, which contains 226 videos with 23,938 frames that cover diverse realistic scenes, objects, instances and motions. With corresponding real human eye fixation data, we obtain precise ground truths. This is the first work that explicitly emphasizes the challenge of saliency shift, i.e. the video salient object(s) may dynamically change. To further contribute the community a complete benchmark, we systematically assess 17 representative VSOD algorithms over seven existing VSOD datasets and our DAVSOD with totally ~84K frames (largest scale) Utilizing three famous metrics, we then present a comprehensive and insightful performance analysis. Furthermore, we propose a baseline model. It is equipped with a saliency shift aware convLSTM, which can efficiently capture video saliency dynamics through learning human attention shift behavior. Extensive experiments open up promising future directions for model development and comparison.

title: Progressive Attention Guided Recurrent Network for Salient Object Detection
abstract: Effective convolutional features play an important role in saliency estimation but how to learn powerful features for saliency is still a challenging task. FCN based methods directly apply multi level convolutional features without distinction, which leads to sub optimal results due to the distraction from redundant details. In this paper, we propose a novel attention guided network which selectively integrates multi level contextual information in a progressive manner. Attentive features generated by our network can alleviate distraction of background thus achieve better performance. On the other hand, it is observed that most of existing algorithms conduct salient object detection by exploiting side output features of the backbone feature extraction network. However, shallower layers of backbone network lack the ability to obtain global semantic information, which limits the effective feature learning. To address the problem, we introduce multi path recurrent feedback to enhance our proposed progressive attention driven framework. Through multi path recurrent connections, global semantic information from the top convolutional layer is transferred to shallower layers, which intrinsically refines the entire network. Experimental results on six benchmark datasets demonstrate that our algorithm performs favorably against the state of the art approaches.

title: Three Stream Attention Aware Network for RGB D Salient Object Detection
abstract: Previous RGB D fusion systems based on convolutional neural networks typically employ a two stream architecture, in which RGB and depth inputs are learned independently. The multi modal fusion stage is typically performed by concatenating the deep features from each stream in the inference process. The traditional two stream architecture might experience insufficient multi modal fusion due to two following limitations: 1) the cross modal complementarity is rarely studied in the bottom up path, wherein we believe the cross modal complements can be combined to learn new discriminative features to enlarge the RGB D representation community and 2) the cross modal channels are typically combined by undifferentiated concatenation, which appears ambiguous to selecting cross modal complementary features. In this paper, we address these two limitations by proposing a novel three stream attention aware multi modal fusion network. In the proposed architecture, a cross modal distillation stream, accompanying the RGB specific and depth specific streams, is introduced to extract new RGB D features in each level in the bottom up path. Furthermore, the channel wise attention mechanism is innovatively introduced to the cross modal cross level fusion problem to adaptively select complementary feature maps from each modality in each level. Extensive experiments report the effectiveness of the proposed architecture and the significant improvement over the state of the art RGB D salient object detection methods.