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Video caption aims to generate descriptive sentences about the video, and the most critical problem is how to achieve accurate word prediction with standardized and coherent syntax structure, which requires the model to thoroughly understand video content and precisely map them into corresponding sentence components. Many existing methods usually fuse different video features into a single visual feature for generating sentences. However, they ignore the word dataset prior information in the annotations (such as Part-Of-Speech) and they also ignore the association between sentence components and types of visual features. To solve these problems, we propose a POS-trends dynamic-aware model (PDA) to fully exploit the word dataset prior information in the captions to predict POS tag, so as to assist generating captions. We propose a POS feature extraction (PFE) module to use different filters to extract different POS-trends features, predict POS tags and fuse visual features. Furthermore, we propose a visual-dynamic-aware (VDA) module to dynamically adjust the mapping way of words and supplement the visual information into the local features. The fusion features provide directional visual information to generate correct words, and the predicted POS tags to guide the decoding process to generate a more standardized and coherent syntax structure. A large number of experiments based on MSVD, MSR-VTT and VATEX demonstrated that our method outperforms the state-of-the-art methods in BLEU-4, ROUGE-L, METEOR, CIDEr. Code can be available at: https://github.com/WangLanxiao/PDA-for-video-caption. [ABSTRACT FROM AUTHOR]

Single image dehazing is the key to enhancing image visibility in outdoor scenes, which facilitates human observation and computer recognition. The existing approaches generally utilize a one-shot strategy that indiscriminately applies the same filters to all local regions. However, due to neglecting inhomogeneous illumination and detail distortion, their dehazed results easily suffer from underfiltering or overfiltering across different regions. To tackle this issue, we propose a region-adaptive two-shot network (RATNet) that follows a coarse-to-fine framework. First, a lightweight subnetwork is applied to execute regular global filtering and obtain an initially restored image. Then, a two-branch subnetwork is put forward whose branches separately refine its illumination and detail. Eventually, we derive the final prediction by adaptively aggregating the results after illumination modification and detail restoration, whose region-variant weights are jointly optimized by maximizing the similarity between our fused result and haze-free counterpart. Extensive experiments validate the superiority of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Object proposals are used in two-stage detectors, such as R-CNN, to generate detection results, including category predictions and refined bounding-boxes. As a result, classification scores are assigned to refined bounding-boxes rather than object proposals. However, this procedure ignores the discrepancy of data distribution between object proposals and refined bounding-boxes. We consider this discrepancy could limit the detection accuracy. Specifically, the foreground/background imbalance on object proposals and inaccurate information from low-IoU proposals could hinder the category prediction. In this paper, we propose a detector called the Multi-Path Detection Calibration Network (PDC-Net) to address this problem. The key idea behind PDC-Net is calibrating detection results from R-CNN by considering the statistical discrepancy between object proposals and refined bounding-boxes. PDC-Net is built on Faster R-CNN. The core component in PDC-Net is the multi-path detection head, in which the base detector (from Faster R-CNN) generates detection results from object proposals and multiple calibration detectors fix incorrect outputs from the base detector using refined bounding-boxes. Experiments reveal that PDC-Net can boost detection results. Our method could reach 83.1% and 43.3% mAP respectively on PASCAL VOC and MSCOCO benchmarks, which is comparable to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pixel-level segmentation has been widely used to improve object detection. Most of the existing methods refine detection features by adding the constraint of the segmentation branch or by simply embedding high-level segmentation features into detection features within the local receptive field. However, noisy segmentation features are unavoidable in real-word applications and can easily cause false positives. To address this problem, we propose a novel hierarchical context embedding module to effectively embed segmentation features into detection features. The idea of this module is to capture hierarchical context information that includes local objects or parts and nonlocal context features by learning multiple attention maps, and subsequently utilize interdependencies between features to recalibrate noisy segmentation features. Furthermore, we use this module in the proposed gated encoder-decoder network that adaptively aggregates feature maps of different resolutions based on the gate mechanism so that we can embed multiscale segmentation feature maps into detection features for more accurate detection of objects of all sizes. Experimental results demonstrate the effectiveness of the proposed method on the Pascal VOC 2012Seg dataset, the Pascal VOC dataset and the MS COCO dataset. [ABSTRACT FROM AUTHOR]

Head detection plays an important role in localizing and identifying persons from visual data. Most existing methods treat head detection as a specific form of object detection. Head detection is nontrivial due to the considerable difficulty in building the local and global information under conditions of unconstrained pose and orientation. To address these issues, this paper presents an effective adaptive relational network to capture context information, which is greatly helpful to suppress missed detection. We show that the fundamental contextual properties, such as the global shape priors from different heads and the local adjacent relationship between the head and shoulders, can be systematically quantified by visual operators. Specifically, we propose a two-step search algorithm to quantify the global intergroup conflict with adaptive scale, pose and viewpoint. Meanwhile, a structured feature module is introduced to capture the local relation of intraindividual stability. Finally, the global priors and local relation are integrated seamlessly into a single-stage head detector that is end-to-end trainable. An extensive ablation analysis demonstrates the effectiveness of our approach. We achieve state-of-the-art results on two challenging datasets, i.e., HollywoodHeads and Brainwash. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are an essential component in the realization of Industry 4.0. With drones helping to improve industrial safety and efficiency in utilities, construction, and communication, there is an urgent need for drone-based intelligent applications. In this paper, we develop a unified framework to simultaneously detect and count vehicles from drone images. We first explore why the state-of-the-art detectors fail in highly dense drone scenes, which provides more appropriate insights. Then, we propose an effective loss to push the anchors toward matching the ground-truth boxes as much as possible, specifically designed for scale-adaptive anchor generation. Inspired by attention mechanisms in the human visual system, we maximize the mutual information between object classes and features by combining bottom-up cues with top-down attention mechanisms specifically designed for feature extraction. Finally, we build a counting layer with a regularized constraint related to the number of vehicles. Extensive experiments demonstrate the effectiveness of our approach. For both tasks, our proposed method achieves state-of-the-art results on all four challenging datasets. In particular, our results reduce error by a larger factor than previous methods. [ABSTRACT FROM AUTHOR]

The ultra high-definition (UHD) video format, which has recently become popular, aims to provide high spatial resolution, high temporal frame rate, high sample bit-depth, and wide pixel color gamut. Despite the continued development of global network capacities, it inevitably causes the increased bandwidth cost of catering to the requirement of delivering UHD video services. To address such challenges, this paper presents an improved super coding unit (SCU) method for UHD video coding in High Efficiency Video Coding (HEVC). Initially, the medium coding unit (MCU) is proposed to avoid unnecessary brute-force coding unit (CU) partitions of SCU. Furthermore, the SCU is proposed to be encoded by Direct-MCU and SCU-to-MCU modes: the Direct-MCU mode is intended to better adapt to the texture-rich region, which guarantees the compression efficiency by avoiding extra-size CU partition; the SCU-to-MCU mode is designed for the homogeneous region of UHD content, which saves the encoding time by skipping fine-grained CU partition search. Moreover, a learning-based fast SCU decision approach is proposed to speed up the determination process of Direct-MCU and SCU-to-MCU, where three representative handcrafted features are extracted. Experimental results show that our method achieves an affordable complexity and excellent coding efficiency (up to 7.30% Bjøntegaard Delta rate savings) in UHD video coding compared to recent HEVC reference software. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spatial–spectral feature fusion model with a predictive feature weighting mechanism and demonstrate its applications to the problems of hyperspectral image classification and segmentation. To address these problems, we learn a set of 1-D convolutional local spectral filters and 2-D spatial–spectral filters that feed features into a fusion module, in an end-to-end fashion. We propose a lightweight predictive feature weighting component embedded in the fusion model and consider four design fusion options, i.e., by adding or concatenating features with equal or predicted weights. For the pixel classification task, the training input consists of image patches with labeled central pixels, whereas for the spatial segmentation task, it includes the label maps of image regions. The proposed networks have been evaluated on the Indian Pines, Pavia University, and Houston University for the classification problem and the SpaceNet data set for the spatial segmentation problem. The quantitative results favor the proposed approach over the state-of-the-art methods across all the four data sets. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Hierarchical Parsing Net (HPN) for semantic scene parsing. Unlike previous methods, which separately classify each object, HPN leverages global scene semantic information and the context among multiple objects to enhance scene parsing. On the one hand, HPN uses the global scene category to constrain the semantic consistency between the scene and each object. On the other hand, the context among all objects is also modeled to avoid incompatible object predictions. Specifically, HPN consists of four steps. In the first step, we extract scene and local appearance features. Based on these appearance features, the second step is to encode a contextual feature for each object, which models both the scene-object context (the context between the scene and each object) and the interobject context (the context among different objects). In the third step, we classify the global scene and then use the scene classification loss and a backpropagation algorithm to constrain the scene feature encoding. In the fourth step, a label map for scene parsing is generated from the local appearance and contextual features. Our model outperforms many state-of-the-art deep scene parsing networks on five scene parsing databases. [ABSTRACT FROM AUTHOR]

This paper proposes a method to segment object from the web images using logo detection. The method consists of three steps. In the first step, the logos are located from the original images by SIFT matching. Based on the logo location and the object shape model, the second step extracts the object boundary from the image. In the third step, we use the object boundary to model the object appearance, which is then used in the MRF based segmentation method to finally achieve the object segmentation. The key of our method is the object boundary extraction, which is achieved by searching a variation of the shape model that best fits the local edge of the image. Affine transform is used to consider the variations among the objects. Meanwhile, the Nelder-Mead simplex method with a simple initial rough search is used to run the boundary search. To verify the proposed method, we collect a LogoSeg dataset from the web such as Flickr and Google. The MOMI dataset is also used for the verification. The experimental results demonstrate that the proposed logo detection based segmentation method can improve the performance of the object segmentation. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel feature adaptive co-segmentation method that can learn adaptive features of different image groups for accurate common objects segmentation. We also propose image complexity awareness for adaptive feature learning. In the proposed method, the original images are first ranked according to the image complexities that are measured by superpixel changing cue and object detection cue. Then, the unsupervised segments of the simple images are used to learn the adaptive features, which are achieved using an expectation-minimization algorithm combining l 1-regularized least squares optimization with the consideration of the confidence of the simple image segmentation accuracies and the fitness of the learned model. The error rate of the final co-segmentation is tested by the experiments on different image groups and verified to be lower than the existing state-of-the-art co-segmentation methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel reduced-reference (RR) video quality assessment (VQA) is proposed by exploiting the spatial information loss and the temporal statistical characteristics of the interframe histogram. From the spatial perspective, an energy variation descriptor (EVD) is proposed to measure the energy change of each individual encoded frame, which results from the quantization process. Besides depicting the energy change, EVD can further simulate the texture masking property of the human visual system (HVS). From the temporal perspective, the generalized Gaussian density (GGD) function is employed to capture the natural statistics of the interframe histogram distribution. The city-block distance (CBD) is used to calculate the histogram distance between the original video sequence and the encoded one. For simplicity, the difference image between adjacent frames is employed to characterize the temporal interframe relationship. By combining the spatial EVD together with the temporal CBD, an efficient RR VQA is developed. Evaluation on the subjective quality video database demonstrates that the proposed method outperforms the representative RR video quality metric and the full-reference VQAs, such as peak signal-to-noise ratio and structure similarity index in matching subjective ratings. This means that the proposed metric is more consistent with the HVS perception. Furthermore, as only a small number of RR features are extracted for representing the original video sequence (each frame requires only one parameter for describing EVD and three parameters for recording GGD), the RR features can be embedded into the video sequences or transmitted through the ancillary data channel, which can be used in the video quality monitoring system. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a method to detect co-saliency from an image pair that may have some objects in common. The co-saliency is modeled as a linear combination of the single-image saliency map (SISM) and the multi-image saliency map (MISM). The first term is designed to describe the local attention, which is computed by using three saliency detection techniques available in literature. To compute the MISM, a co-multilayer graph is constructed by dividing the image pair into a spatial pyramid representation. Each node in the graph is described by two types of visual descriptors, which are extracted from a representation of some aspects of local appearance, e.g., color and texture properties. In order to evaluate the similarity between two nodes, we employ a normalized single-pair SimRank algorithm to compute the similarity score. Experimental evaluation on a number of image pairs demonstrates the good performance of the proposed method on the co-saliency detection task. [ABSTRACT FROM AUTHOR]

Most existing feature detectors assume no surface discontinuity within the keypoints' support regions and, hence, have little chance to match the keypoints located on or near the surface boundaries. These keypoints, though not many, are salient and representative. In this paper, we show that they can be successfully matched by using the proposed scale- and affine-invariant Fan features. Specifically, the image neighborhood of a keypoint is depicted by multiple fan subregions, namely Fan features, to provide robustness to surface discontinuity and background change. These Fan features are made scale-invariant by using the automatic scale selection method based on the Fan Laplacian of Gaussian (FLOG). Affine invariance is further introduced to the Fan features based on the affine shape diagnosis of the mirror-predicted surface patch. The Fan features are then described by Fan-SIFT, which is an extension of the famous scale-invariant feature transform (SIFT) descriptor. Experimental results of quantitative comparisons show that the proposed Fan feature has good repeatability that is comparable to the state-of-the-art features for general structured scenes. Moreover, by using Fan features, we can successfully match image structures near surface discontinuities despite significant scale, viewpoint, and background changes. These structures are complementary to those found by the traditional methods and are especially useful for describing weakly textured scenes, which is demonstrated in our experiments on image matching and object rendering. [ABSTRACT FROM AUTHOR]

In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by statistical modeling of the discrete cosine transform (DCT) coefficient distributions. In order to reduce the RR data rates and further exploit the identical nature of the coefficient distributions between adjacent DCT subbands, the DCT coefficients are reorganized into a three-level coefficient tree. Subsequently, generalized Gaussian density (GGD) is employed to model the coefficient distribution of each reorganized DCT subband. The city-block distance is employed to measure the difference between the two images. Experimental results demonstrate that only a small number of RR features is sufficient for representing the image perceptual quality. The proposed method outperforms the RR WNISM and even the full-reference (FR) quality metric PSNR. [ABSTRACT FROM PUBLISHER]