Search Results

Large-scale point cloud semantic segmentation is a challenging task in 3D computer vision. A key challenge is how to resolve ambiguities arising from locally high inter-class similarity. In this study, we introduce a solution by modeling long-distance ...

In this paper, we present SasWOT, the first training-free Semantic segmentation Architecture Search (SAS) framework via an auto-discovery proxy. Semantic segmentation is widely used in many real-time applications. For fast inference and memory efficiency,...

The aim of the reference-based image super-resolution (RefSR) is to reconstruct high-resolution (HR) when a reference (Ref) image with similar content as that of the low-resolution (LR) input is given. In the task, the quality of existing ...

Random-needle embroidery is a kind of traditional Chinese embroidery where artists reproduce the original color image using multilayered intersecting stitches and overlapped deliberately. In other words, artists embroider intersecting stitches ...

Predicting future scenes based on historical frames is challenging, especially when it comes to the complex uncertainty in nature. We observe that there is a divergence between spatial-temporal variations of active patterns and non-active patterns in a ...

In this paper, we propose a method for fine-grained vehicle recognition in traffic surveillance video. Compared with general theory about single image fine-grained recognition, this method focuses on multi-frame information combination and the ...

Learning with weak supervision already becomes one of the research trends in fine-grained image recognition. These methods aim to learn feature representation in the case of less manual cost or expert knowledge. Most existing weakly supervised methods ...

Recovering the three-dimensional shape of an object from a two-dimensional image is an important research topic in computer vision. Traditional methods use stereo vision or inter-image matching to obtain geometric information about the object, but ...

As a long-standing ill-posed problem, 3D reconstruction from a single image is an important research topic in computer vision. The information in a single image can represent an infinite number of possible three-dimensional shapes. To recover ...

Creating different textile patterns to generate printable fabric images is a difficult image processing task. To accomplish this task, we propose a novel framework for dyeing creation, which allows non-professionals to design individual fabric ...

We propose an encoder–decoder CNN framework to predict depth from one single image in a self-supervised manner. To this aim, we design three kinds of encoder based on the recent advanced deep neural network and one kind of decoder which can ...

Surveillance videos which record crowd behaviors have dramatically increased due to the wide applications. A quick view of such crowd surveillance video in a constrained time is an increasing demand because it always contain a huge number of ...

Point cloud segmentation is a key task of shape analysis with various applications. Existing segmentation methods usually apply a single segmentation network to compute point-wise loss for network training. The point-wise loss, which will lose ...

As an interesting and emerging topic, multiple foreground cosegmentation (MFC) aims at extracting a finite number of common objects from an image collection, which is useful to variety of visual media applications. Although a number of approaches ...

As a very important research issue in digital media art, neural learning based video style transfer has attracted more and more attention. A lot of recent works import optical flow method to original image style transfer framework to preserve frame-...

To parse images into fine-grained semantic parts, the complex elements will put it in trouble when using off-the-shelf semantic segmentation networks, because it is difficult for them to utilize the contextual information of fine-grained parts. In ...

Depth estimation from single image by deep learning is a hot topic of research nowadays. Existing methods mainly focus on learning neural network supervised by ground truth. This paper proposes a method for single view depth estimation based on ...

Neural learning methods have been shown to be effective in style transfer. These methods, which are called NST, aim to synthesize a new image that retains the high-level structure of a content image while keeps the low-level features ...

As an interesting and emerging topic, co-saliency detection aims at discovering common and salient objects in a group of related images, which is useful to variety of visual media applications. Although a number of approaches have been proposed to ...

Effective feature representations which should not only express the image's individual properties, but also reflect the interaction among group images are essentially crucial for robust co-saliency detection. This paper proposes a novel deep learning co-...