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- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
- Correlation-Aware Deep Tracking200
This work proposes a novel target-dependent feature network inspired by the self-/cross-attention scheme that deeply embeds cross-image feature correlation in multiple layers of the feature network and is able to suppress non-target features, resulting in instance-varying feature extraction.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- Statistical behavior analysis and precision optimization for the laser stripe center detector based on Steger's algorithm89
In this paper, a statistical behavior analysis for the laser stripe center extractor based on Steger method has been carried out and relationships between center extraction precision, image quality and stripe characteristics have been examined analytically.
- Learning Tracking Representations via Dual-Branch Fully Transformer Networks76
A Siamese-like Dual-branch network based on solely Transformers for tracking, which outperforms the state-of-the-art methods on the GOT-10k and VOT2020 benchmarks and achieves real-time inference speed (about 40 fps) on one GPU.
- Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions51
This work applies Convolutional AutoEncoder to learn a stock representation, based on which a novel portfolio construction strategy is proposed, using the deeply learned representation and modularity optimisation to cluster stocks and identify diverse sectors.
- A New Signal Processing Method Based on Notch Filtering and Wavelet Denoising in Wire Rope Inspection50
A combined signal processing method based on notch filtering and wavelet denoising to process detected wire rope signals is proposed, which can not only distinguish steel wire rope defect signal and strand signal effectively but also with high detection accuracy, even for the inner defect.
- Automatic velocity picking from semblances with a new deep-learning regression strategy: Comparison with a classification approach49
A tutorial overview of two state-of-the-art ML implementations of classification and regression neural networks for the extraction of apparent rms velocity trajectories from semblance data.
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- Automatic evaluation of facial nerve paralysis by dual-path LSTM with deep differentiated network24
The key idea behind DP-LSTM-DDN is that the diagnosis results are sensitive to the facial asymmetry and the patterns of facial muscular movements when the patients were doing the diagnostic facial actions, and a deep differentiated network is designed to analyze the difference between two sides of patients’ faces.
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- Human body and posture recognition system based on an improved thinning algorithm23
Experimental results demonstrate that the human body and posture estimation algorithm have a robust and real-time performance, and is useful for the discrimination of human postures.
- Representation learning from noisy user-tagged data for sentiment classification22
A novel Weakly-supervised Anti-noise Contrastive Learning framework for sentiment classification, and name it as WACL, which can learn rich contrastive sentiment patterns in the case of label noise and is applicable to a variety of deep encoders.
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- A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints21
The key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy that is more robust and accurate than state-of-the-art methods.
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- Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images18
A novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images and results show the effectiveness and advancement of the proposed method.
- P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds17
This paper proposes to perform part-to-part motion modeling for consecutive point clouds and introduces a novel tracking framework, termed P2P, which fuses each corresponding part information between consecutive point clouds, effectively exploring detailed information changes and thus modeling accurate target-related motion cues.
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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 2026-10-15. 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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