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- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- VITAL: VIsual Tracking via Adversarial Learning577
The VITAL algorithm is presented, which uses a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes and identifies the mask that maintains the most robust features of the target objects over a long temporal span.
- Unpaired Learning of Deep Image Denoising197
A two-stage scheme to facilitate unpaired learning of denoising network by incorporating self-supervised learning and knowledge distillation is presented, which performs favorably on both synthetic noisy images and real-world noisy photographs.
- Learning Support Correlation Filters for Visual Tracking120
This paper derives an equivalent formulation of a SVM model with the circulant matrix expression and presents an efficient alternating optimization method for visual tracking and extends the SCF-based tracking algorithm with multi-channel features, kernel functions, and scale-adaptive approaches to further improve the tracking performance.
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- Joint Representation and Truncated Inference Learning for Correlation Filter Based Tracking54
This paper investigates the joint learning of deep representation and model adaptation, where an updater network is introduced for better tracking on future frame by taking current frame representation, tracking result, and last CF tracker as input.
- Learning pairwise SVM on hierarchical deep features for ear recognition41
The authors propose to transform the ear identification problem to the binary classification by composing pairwise samples and resolve it with the pairwise support vector machine (SVM) and exhibits decent performance compared with the state-of-the-art methods.
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- Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution29
A real-world ×4 VSR dataset is built, namely MVSR4×, where low- and high-resolution videos are captured with different focal length lenses of a smartphone, respectively, and an effective alignment method is proposed, namely EAVSR, which takes the proposed multi-layer adaptive spatial transform network to refine the offsets provided by the pre-trained optical flow estimation network.
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- DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models14
The proposed Latent Kernel Prediction Network (LKPN) outperforms state-of-the-art image deblurring methods on both benchmark and real-world images and iterative refinement enhances the accuracy and robustness of the deblurring process.
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- Learning Diverse Tone Styles for Image Retouching12
It is argued that learning in a one-dimensional style space could 1) disentangle the retouching styles from the image content, 2) lead to a stable style presentation form, and 3) avoid the spatial disharmony effects.
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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 2026-10-13. 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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