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Image-text matching remains a challenging task due to heterogeneous semantic diversity across modalities and insufficient distance separability within triplets. Different from previous approaches focusing on enhancing multi-modal representations or ...

Correlation has a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion method that considers the similarity between the template and the search region. However, the ...

• Proxy token guided transformer-based baseline for vision-language tracking.
• Dense annotated long-term vision-language tracking dataset.
• Extensive experiments on a new long-term vision-language tracking dataset.

Tracking by vision-language is an emergent topic. Previous researchers mainly adopt CNN and sequential models for video and language encoding, however, their methods are limited by poor generalization performance. To address this problem, this ...

Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching. Generally, recent approaches first employ a cross-modal attention unit to capture latent region-word interactions, and then integrate ...

Weakly supervised temporal action localization (WTAL) is a challenging task as only video-level category labels are available during training stage. Without precise temporal annotations, most approaches rely on complementary RGB and optical flow ...

This paper proposes a hierarchical loss for monocular depth estimation, which measures the differences between the prediction and ground truth in hierarchical embedding spaces of depth maps. In order to find an appropriate embedding space, we ...

Most existing bottom-up algorithms measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead ...

Object proposals are a series of candidate segments containing the objects of interest, which are taken as preprocessing and widely applied in various vision tasks. However, most of existing saliency approaches only utilizes the proposals to compute a ...

In this paper, we propose a visual saliency detection algorithm to explore the fusion of various saliency models in a manner of bootstrap learning. First, an original bootstrapping model, which combines both weak and strong saliency models, is ...

In this paper, we propose a novel anomaly detection approach based on Locality Sensitive Hashing Filters (LSHF), which hashes normal activities into multiple feature buckets with Locality Sensitive Hashing (LSH) functions to filter out abnormal ...

Sketch-based object search is a challenging problem mainly due to two difficulties: (1) how to match the binary sketch query with the colorful image, and (2) how to locate the small object in a big image with the sketch query. To address the above ...

In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction error. The image boundaries are first extracted via superpixels as likely cues for background templates, from which dense and sparse appearance models ...

In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion ...

Sketch retrieval aims at retrieving the most similar sketches from a large database based on one hand-drawn query. Successful retrieval hinges on an effective representation of sketch images and an efficient search method. In this paper, we propose a ...

Previous saliency detection algorithms used to focus on low level features directly or utilize a bunch of sample images and manually labeled ground truth to train a high level learning model. In this paper, we propose a novel coding-based saliency ...

In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction errors. The image boundaries are first extracted via super pixels as likely cues for background templates, from which dense and sparse appearance ...

Shape descriptor plays very important role in shape retrieval system especially in the case of input shapes are drawn by hand. A good descriptor should be not only deformation tolerant but also compact and less memory consuming. With this in mind, we ...

Most existing bottom-up methods measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead ...

In this paper, we propose a novel method to segment human body in static images by graph cuts based on two deformable models at two-scale superpixel. In our study, body segmentation is decomposed into torso detection and lower body recovery. Based on ...

In this paper, we propose to represent an image as a local descriptor tensor and use a multilinear supervised neighborhood embedding (MSNE) for discriminant feature extraction, which is able to be used for subject or scene recognition. The contributions ...