Lala Li
Impact in
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- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
- Advanced Vision and Imaging
- Medical Image Segmentation Techniques
Papers in
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- Natural Language Processing Techniques 2
- Advanced Text Analysis Techniques 1
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- Advanced Image and Video Retrieval Techniques 2
- Multimodal Machine Learning Applications 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Geoffrey E. Hinton (1 shared paper)Saurabh Saxena (1 shared paper)Ting Chen (1 shared paper)Thao Nguyen (1 shared paper)Simon Kornblith (1 shared paper)Zi-Rui Wang (1 shared paper)Chitwan Saharia (1 shared paper)Jia Yan (1 shared paper)
- Journals
- International Conference on Machine Learning (1 paper)Advances in intelligent systems research (1 paper)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Lala Li
7 papers receiving 106 citations
Lala Li's Hit Papers
Peers
Comparison fields: 5 of 42
- Computer Vision and Pattern Recognition 62
- Computer Graphics and Computer-Aided Design 3
- Structural Biology 1
- Artificial Intelligence 22
- Media Technology 5
Countries citing papers authored by Lala Li
This map shows the geographic impact of Lala Li's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lala Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lala Li more than expected).
Fields of papers citing papers by Lala Li
This network shows the impact of papers produced by Lala Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lala Li. The network helps show where Lala Li may publish in the future.
Co-authors
The 19 scholars most cited alongside Lala Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A Generalist Framework for Panoptic Segmentation of Images and Videos Hit paper breakdown → | 2023 | 61 |
| 2 | 2022 | 23 | |
| 3 | 2023 | 10 | |
| 4 | 2014 | 6 | |
| 5 | Differentiable Product Quantization for End-to-End Embedding Compression | 2020 | 4 |
| 6 | 2022 | 2 | |
| 7 | 2019 | 1 | |
| 8 | 2009 | 0 |
About Lala Li
Lala Li is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Endocrine and Autonomic Systems, Pulmonary and Respiratory Medicine and Information Systems, having authored 8 papers that have together received 107 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (2 papers), Natural Language Processing Techniques (2 papers), Multimodal Machine Learning Applications (1 paper), Web Data Mining and Analysis (1 paper), Respiratory Support and Mechanisms (1 paper), Advanced Text Analysis Techniques (1 paper), Music and Audio Processing (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (62 citations), Computer Graphics and Computer-Aided Design (3 citations), Structural Biology (1 citation), Artificial Intelligence (22 citations) and Media Technology (5 citations). Lala Li has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Geoffrey E. Hinton, Saurabh Saxena, Ting Chen, Thao Nguyen, Simon Kornblith, Zi-Rui Wang, Chitwan Saharia, Jia Yan, Emily Denton and Jay Whang. Their work appears in journals such as International Conference on Machine Learning and Advances in intelligent systems research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.