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Works218 from public data
- Practical elimination of near-duplicates from web video search353
The results of 24 queries in a data set of 12,790 videos retrieved from Google, Yahoo! and YouTube show that this hierarchical approach can dramatically reduce redundant video displayed to the user in the top result set, at relatively small computational cost.
- A survey on deep learning-based fine-grained object classification and semantic segmentation337
Four types of deep learning based fine-grained image classification approaches are reviewed, including the general convolutional neural networks (CNNs), part detection based, ensemble of networks based and visual attention based fine -grainedimage classification approaches.
- Multi-View Image Generation from a Single-View193
This paper proposes a novel image generation model termed VariGANs, which combines the merits of the variational inference and the Generative Adversarial Networks (GANs), and generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts.
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- Learning Spatial Awareness to Improve Crowd Counting151
A novel architecture called SPatial Awareness Network (SPANet) to incorporate spatial context for crowd counting is presented, and the Maximum Excess over Pixels (MEP) loss is proposed to achieve this by finding the pixel-level subregion with high discrepancy to the ground truth.
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- Rethinking Spatial Invariance of Convolutional Networks for Object Counting140
Inspired by previous work, this work proposes a low-rank approximation accompanied with translation invariance to favorably implement the approximation of massive Gaussian convolution to improve the spatial invariance of convolutional networks.
- DB-LSTM: Densely-connected Bi-directional LSTM for human action recognition140
A Densely-connected Bi-directional LSTM (DB-LSTM) network is proposed to model the visual and temporal associations in both forward and backward directions to boost the effectiveness and robustness of modeling long-range action recognition.
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- Comparison Study of Internet Recommendation System128
A comprehensive survey of the recommender system research aiming to facilitate readers to understand this field is made and most difficulties and future directions are concluded.
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- Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting97
Extensive experiments on four challenging benchmarks show that McML can significantly improve the original multi-column networks and outperform the other state-of-the-art approaches.
- Near-duplicate keyframe retrieval with visual keywords and semantic context89
A novel NDK retrieval approach is proposed by exploring both visual and textual cues from the visual vocabulary and semantic context respectively by linearly fusing both modalities, which is effective and efficient enough for online video search.
- Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images86
A novel deep neural network is proposed to explore a new cross-domain task, Video2Shop, targeting for matching clothes appeared in videos to the exactly same items in online shops, which outperforms the state-of-the-art methods.
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- Detection of bird nests in overhead catenary system images for high-speed rail71
This paper addresses the problem of automatic recognition of bird nests for OCS images with a novel framework, which is composed of five steps: adaptive binarization, trunk/branch detection, hovering point detection, streak extraction and pattern learning, for bird nest detection.
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- Personalized clothing recommendation combining user social circle and fashion style consistency52
Three social factors (i.e., personal interest, interpersonal interest similarity and interpersonal influence) and fashion style consistency are fused into a unified personalized recommendation model based on probabilistic matrix factorization (PMF).
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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