Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks95 from public data
- 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.
- GradNet: Gradient-Guided Network for Visual Object Tracking334
A novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations and a template generalization training method is proposed to better use gradient information and avoid overfitting.
- 279
- Real-Time ‘Actor-Critic’ Tracking139
This work modifications the original deep deterministic policy gradient algorithm to effectively train the ‘Actor-Critic’ model for the tracking task and demonstrates that the proposed tracker performs favorably against many state-of-the-art methods, with real-time performance.
- GLiT: Neural Architecture Search for Global and Local Image Transformer107
This work introduces the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition and introduces a locality module that models the local correlations in images explicitly with fewer computational cost.
- Multi attention module for visual tracking75
This work proposes a new visual tracking algorithm leveraging multi-level visual attention to take full use of the information during tracking, and implements the attention network with long short term memory (LSTM) units, which are capable of capturing the historical context information to perform more reliable inference at the current time step.
- 62
- 54
- Evolving Search Space for Neural Architecture Search51
The proposed NSE scheme achieves 77.3% top-1 retrain accuracy on ImageNet with 333M FLOPs, which yielded a state-of-the-art performance among previous auto-generated architectures that do not involve knowledge distillation or weight pruning.
- BN-NAS: Neural Architecture Search with Batch Normalization37
BN-NAS can significantly reduce the time required by model training and evaluation in NAS and a BN-based indicator for predicting subnet performance at a very early training stage is proposed for fast evaluation.
- An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance36
A smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases using computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time is designed.
- Disruption of thalamic connectivity in Alzheimer’s disease: a diffusion tensor imaging study30
The DTI analyses indicate that the integrity of thalamic connectivity is progressively disrupted following cognitive decline in AD and that DTI parameters in the column and body of the fornix show promise as potential markers for the early diagnosis of AD and for monitoring disease progression.
- 29
- 26
- 20
- 18
- 17
- Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks13
A data-driven approach that uses a generative adversarial network to create the song transition by learning from real-world DJ mixes to achieve competitive results compared with a number of baselines is explored.
- 13
- 12
- 12
- 10
- 9
- 8
- 8
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 2026-10-14. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.