What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Haibin Ling mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Eye tracking and Video tracking.
His study in Sparse approximation, Particle filter, Robustness, Benchmark and Discriminative model falls within the category of Artificial intelligence.
His research on Computer vision often connects related topics like Visualization.
Haibin Ling studied Pattern recognition and Shape context that intersect with Representation, Embedding, Scale-invariant feature transform and Similarity.
His research in Eye tracking intersects with topics in Transfer of learning, Tracking and Deep learning.
His Video tracking research incorporates themes from Frame and Latent variable.
His most cited work include:
- Shape Classification Using the Inner-Distance (859 citations)
- Robust Visual Tracking and Vehicle Classification via Sparse Representation (785 citations)
- Real time robust L1 tracker using accelerated proximal gradient approach (761 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Video tracking and Benchmark.
His work in Feature extraction, Eye tracking, Discriminative model, Object and Feature are all subfields of Artificial intelligence research.
The various areas that he examines in his Eye tracking study include Particle filter and Sparse approximation.
His studies deal with areas such as Visualization and Robustness as well as Computer vision.
He has researched Pattern recognition in several fields, including Contextual image classification and Image.
His Video tracking research is multidisciplinary, incorporating perspectives in Ground truth, Frame and Drone.
He most often published in these fields:
- Artificial intelligence (85.33%)
- Computer vision (50.27%)
- Pattern recognition (32.88%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (85.33%)
- Computer vision (50.27%)
- Object (11.41%)
In recent papers he was focusing on the following fields of study:
Haibin Ling focuses on Artificial intelligence, Computer vision, Object, Pattern recognition and Benchmark.
His work is connected to Segmentation, Deep learning, Feature, Video tracking and Image, as a part of Artificial intelligence.
His Feature research is multidisciplinary, incorporating elements of Pyramid and Object detection.
His biological study spans a wide range of topics, including Frame and Drone.
His work on Minimum bounding box as part of general Object research is frequently linked to Focus, thereby connecting diverse disciplines of science.
His studies in Pattern recognition integrate themes in fields like RGB color model and Visualization.
Between 2019 and 2021, his most popular works were:
- Revisiting Video Saliency Prediction in the Deep Learning Era (92 citations)
- Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection (61 citations)
- CBNet: A Novel Composite Backbone Network Architecture for Object Detection (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Haibin Ling spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Visualization and Object.
In Artificial intelligence, Haibin Ling works on issues like Computer vision, which are connected to Overfitting.
His work on Feature extraction as part of his general Pattern recognition study is frequently connected to Generative model, thereby bridging the divide between different branches of science.
In the field of Segmentation, his study on Image segmentation overlaps with subjects such as Task analysis.
Haibin Ling combines subjects such as Matching, Eye tracking, Graph, Salience and Visual saliency with his study of Visualization.
His Object research incorporates elements of Ranking, Classifier and Relation.
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