Hongvasquez6690

Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images. Recently, researchers design various learning-based algorithms to undertake the co-segmentation task. The main difficulty in this task is how to effectively transfer information between images to make conditional predictions. In this paper, we present CycleSegNet, a novel framework for the co-segmentation task. Our network design has two key components a region correspondence module which is the basic operation for exchanging information between local image regions, and a cycle refinement module, which utilizes ConvLSTMs to progressively update image representations and exchange information in a cycle and iterative manner. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on four popular benchmark datasets - PASCAL VOC dataset, MSRC dataset, Internet dataset, and iCoseg dataset, by 2.6%, 7.7%, 2.2%, and 2.9%, respectively.Significant progress has been made for face detection from normal images in recent years; however, accurate and fast face detection from fisheye images remains a challenging issue because of serious fisheye distortion in the peripheral region of the image. To improve face detection accuracy, we propose a light-weight location-aware network to distinguish the peripheral region from the central region in the feature learning stage. To match the face detector, the shape and scale of the anchor (bounding box) is made location dependent. The overall face detection system performs directly in the fisheye image domain without rectification and calibration and hence is agnostic of the fisheye projection parameters. Experiments on Wider-360 and real-world fisheye images using a single CPU core indeed show that our method is superior to the state-of-the-art real-time face detector RFB Net.Gesture recognition has attracted considerable attention owing to its great potential in applications. Although the great progress has been made recently in multi-modal learning methods, existing methods still lack effective integration to fully explore synergies among spatio-temporal modalities effectively for gesture recognition. The problems are partially due to the fact that the existing manually designed network architectures have low efficiency in the joint learning of multi-modalities. In this paper, we propose the first neural architecture search (NAS)-based method for RGB-D gesture recognition. The proposed method includes two key components 1) enhanced temporal representation via the proposed 3D Central Difference Convolution (3D-CDC) family, which is able to capture rich temporal context via aggregating temporal difference information; and 2) optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities. The resultant multi-modal multi-rate network provides a new perspective to understand the relationship between RGB and depth modalities and their temporal dynamics. Comprehensive experiments are performed on three benchmark datasets (IsoGD, NvGesture, and EgoGesture), demonstrating the state-of-the-art performance in both single- and multi-modality settings. The code is available at https//github.com/ZitongYu/3DCDC-NAS.RGBT tracking has attracted increasing attention since RGB and thermal infrared data have strong complementary advantages, which could make trackers all-day and all-weather work. Existing works usually focus on extracting modality-shared or modality-specific information, but the potentials of these two cues are not well explored and exploited in RGBT tracking. In this paper, we propose a novel multi-adapter network to jointly perform modality-shared, modality-specific and instance-aware target representation learning for RGBT tracking. To this end, we design three kinds of adapters within an end-to-end deep learning framework. In specific, we use the modified VGG-M as the generality adapter to extract the modality-shared target representations. To extract the modality-specific features while reducing the computational complexity, we design a modality adapter, which adds a small block to the generality adapter in each layer and each modality in a parallel manner. Such a design could learn multilevel modality-specific representations with a modest number of parameters as the vast majority of parameters are shared with the generality adapter. We also design instance adapter to capture the appearance properties and temporal variations of a certain target. Moreover, to enhance the shared and specific features, we employ the loss of multiple kernel maximum mean discrepancy to measure the distribution divergence of different modal features and integrate it into each layer for more robust representation learning. https://www.selleckchem.com/products/ink128.html Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against the state-of-the-art methods.In Virtual Reality (VR), the requirements of much higher resolution and smooth viewing experiences under rapid and often real-time changes in viewing direction, leads to significant challenges in compression and communication. To reduce the stresses of very high bandwidth consumption, the concept of foveated video compression is being accorded renewed interest. By exploiting the space-variant property of retinal visual acuity, foveation has the potential to substantially reduce video resolution in the visual periphery, with hardly noticeable perceptual quality degradations. Accordingly, foveated image / video quality predictors are also becoming increasingly important, as a practical way to monitor and control future foveated compression algorithms. Towards advancing the development of foveated image / video quality assessment (FIQA / FVQA) algorithms, we have constructed 2D and (stereoscopic) 3D VR databases of foveated / compressed videos, and conducted a human study of perceptual quality on each database. Each database includes 10 reference videos and 180 foveated videos, which were processed by 3 levels of foveation on the reference videos. Foveation was applied by increasing compression with increased eccentricity. In the 2D study, each video was of resolution 7680×3840 and was viewed and quality-rated by 36 subjects, while in the 3D study, each video was of resolution 5376×5376 and rated by 34 subjects. Both studies were conducted on top of a foveated video player having low motion-to-photon latency (~50ms). We evaluated different objective image and video quality assessment algorithms, including both FIQA / FVQA algorithms and non-foveated algorithms, on our so called LIVE-Facebook Technologies Foveation-Compressed Virtual Reality (LIVE-FBT-FCVR) databases. We also present a statistical evaluation of the relative performances of these algorithms. The LIVE-FBT-FCVR databases have been made publicly available and can be accessed at https//live.ece.utexas.edu/research/LIVEFBTFCVR/index.html.