What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Motion estimation.
In general Artificial intelligence study, his work on Image segmentation and Video tracking often relates to the realm of Speedup, Reference frame and Graph, thereby connecting several areas of interest.
His work on Detection performance expands to the thematically related Computer vision.
His research in Pattern recognition intersects with topics in Histogram, Image and Object detection, Kadir–Brady saliency detector.
His Motion estimation study combines topics in areas such as Macroblock, Multiview Video Coding and Image processing.
His studies deal with areas such as Motion vector and Algorithmic efficiency as well as Algorithm.
His most cited work include:
- An Effective CU Size Decision Method for HEVC Encoders (351 citations)
- Saliency Tree: A Novel Saliency Detection Framework (202 citations)
- Effective CU Size Decision for HEVC Intracoding (166 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation.
Image, Saliency map, Object, Feature extraction and Pixel are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
In his research, Gaze and Salient is intimately related to Salience, which falls under the overarching field of Computer vision.
Zhi Liu works mostly in the field of Pattern recognition, limiting it down to topics relating to Feature and, in certain cases, RGB color model, as a part of the same area of interest.
His work deals with themes such as Video tracking, Pascal and Fixation, which intersect with Segmentation.
His work on Image texture is typically connected to Kernel density estimation as part of general Image segmentation study, connecting several disciplines of science.
He most often published in these fields:
- Artificial intelligence (87.02%)
- Computer vision (54.33%)
- Pattern recognition (53.85%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (87.02%)
- Pattern recognition (53.85%)
- Segmentation (30.77%)
In recent papers he was focusing on the following fields of study:
Zhi Liu mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Image and Convolutional neural network.
His research ties Computer vision and Artificial intelligence together.
Zhi Liu interconnects Salient, Benchmark and Salience in the investigation of issues within Computer vision.
His work on Feature extraction as part of general Pattern recognition study is frequently connected to Modal, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Segmentation research also works with subjects such as
- Pascal which connect with Training set,
- Fixation and related Representation.
His work on Color image as part of general Image research is often related to Structure and Division, thus linking different fields of science.
Between 2017 and 2021, his most popular works were:
- Cross-Modal Self-Attention Network for Referring Image Segmentation (63 citations)
- ICNet: Information Conversion Network for RGB-D Based Salient Object Detection (35 citations)
- Improving Video Saliency Detection via Localized Estimation and Spatiotemporal Refinement (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Zhi Liu mainly investigates Artificial intelligence, Pattern recognition, Convolutional neural network, Saliency map and Segmentation.
Zhi Liu has included themes like Machine learning and Computer vision in his Artificial intelligence study.
His Computer vision study frequently draws connections to adjacent fields such as Robustness.
His Pattern recognition research incorporates elements of Artificial neural network, Object and Image.
His Saliency map study combines topics from a wide range of disciplines, such as Motion and Salient objects.
His biological study focuses on Image segmentation.
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