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- 3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network489
A deep convolutional neural network for 3D human pose estimation from monocular images is proposed and empirically show that the network has disentangled the dependencies among different body parts, and learned their correlations.
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- Learning Dynamic Memory Networks for Object Tracking373
A dynamic memory network to adapt the template to the target's appearance variations during tracking, which can be easily enlarged as the memory requirements of a task increase, which is favorable for memorizing long-term object information.
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- Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network248
A heterogeneous multi-task learning framework for human pose estimation from monocular images using a deep convolutional neural network and it is shown that including the detection tasks helps to regularize the network, directing it to converge to a good solution.
- Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation237
This paper focuses on structured-output learning using deep neural networks for 3D human pose estimation from monocular images, and proposes an efficient recurrent neural network for performing inference with the learned image-embedding.
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- Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks—Counting, Detection, and Tracking209
Evaluating density maps generated by density estimation methods on a variety of crowd analysis tasks, including counting, detection, and tracking finds that the lower-resolution density maps sometimes have better counting performance, and proposes several metrics for measuring the quality of a density map.
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- Understanding eye movements in face recognition using hidden Markov models171
It is found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone.
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- Scanpath modeling and classification with hidden Markov models149
A turnkey method for scanpath modeling and classification based on variational hidden Markov models (HMMs) and discriminant analysis (DA), which allow to integrate bottom-up, top-down, and oculomotor influences into a single model of gaze behavior.
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- ROAM: Recurrently Optimizing Tracking Model145
To effectively adapt the model to appearance variations, this paper proposes to offline train a recurrent neural optimizer to update tracking model in a meta-learning setting, which improves the convergence speed of updating the tracking model while achieving better performance.
- A Comparative Survey of Deep Active Learning143
This work constructs a DAL toolkit, DeepAL+, by re-implementing 19 highly-cited DAL methods, and explores some factors that influence the efficacy of DAL, which provides better references for researchers to design their DAL experiments or carry out DAL-related applications.
- Automatic Musical Pattern Feature Extraction Using Convolutional Neural Network138
This work proposes a novel approach to extract musical pattern features in audio music using convolutional neural network (CNN), a model widely adopted in image information retrieval tasks.
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 2026-10-12. 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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