Siyang Li

Orcid: 0000-0002-5991-649X

Affiliations:
  • Google AI, Mountain View, CA, USA
  • University of Southern California, Los Angeles, CA, USA (PhD)


According to our database1, Siyang Li authored at least 14 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Bibliography

2024
CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2021
Unsupervised video object segmentation with distractor-aware online adaptation.
J. Vis. Commun. Image Represent., 2021

The surprising impact of mask-head architecture on novel class segmentation.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2019
Interpretable convolutional neural networks via feedforward design.
J. Vis. Commun. Image Represent., 2019

2018
Unsupervised Clustering Guided Semantic Segmentation.
Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018

Representative Fashion Feature Extraction by Leveraging Weakly Annotated Online Resources.
Proceedings of the 2018 IEEE International Conference on Image Processing, 2018

Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks.
Proceedings of the Computer Vision - ECCV 2018, 2018

Instance Embedding Transfer to Unsupervised Video Object Segmentation.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation.
Proceedings of the Computer Vision - ACCV 2018, 2018

2017
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision.
CoRR, 2017

Semantic Segmentation with Reverse Attention.
CoRR, 2017

Box Refinement: Object Proposal Enhancement and Pruning.
Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision, 2017

Efficient segmentation-aided text detection for intelligent robots.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Multiple Instance Curriculum Learning for Weakly Supervised Object Detection.
Proceedings of the British Machine Vision Conference 2017, 2017


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