H
Houqiang Li
Researcher at University of Science and Technology of China
Publications - 612
Citations - 17591
Houqiang Li is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Motion compensation. The author has an hindex of 57, co-authored 520 publications receiving 12325 citations. Previous affiliations of Houqiang Li include China University of Science and Technology & Nanjing Medical University.
Papers
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Proceedings ArticleDOI
λ Domain based optimal bit allocation for scalable high efficiency video coding
Li Li,Houqiang Li +1 more
TL;DR: A λ-domain optimal bit allocation algorithm for SHVC is designed by taking the combined inter-layer and intra-layer dependency into consideration, and it is found that the λ ratio between different pictures in the enhancement layer should be proportional to the influence of the current picture to the other enhancement layer pictures.
Journal ArticleDOI
POST: POlicy-Based Switch Tracking
TL;DR: The proposed POST tracker consists of multiple weak but complementary experts (trackers) and adaptively assigns one suitable expert for tracking in each frame and maintains the performance merit of multiple diverse models while favorably ensuring the tracking efficiency.
Proceedings ArticleDOI
Unified 2D and 3D Pre-Training of Molecular Representations
TL;DR: This work proposes a new representation learning method based on a unified 2D and 3D pre-training that achieves state-of-the-art results on 10 tasks, and the average improvement on 2D-only tasks is 8.3%.
Proceedings ArticleDOI
Unregularized Auto-Encoder with Generative Adversarial Networks for Image Generation
TL;DR: A new Auto-Encoder Generative Adversarial Networks (AEGAN) is proposed, which takes advantages of both VAE and GAN and maps the random vector into the encoded latent space by adversarial training based on GAN.
Journal ArticleDOI
Modeling spatial and semantic cues for large-scale near-duplicated image retrieval
TL;DR: The geometric visual vocabulary which captures the spatial contexts by quantizing local features in bi-space, i.e., in descriptor space and orientation space and the contextual visual vocabulary, which combines both spatial and semantic clues outperforms the state-of-the-art bundled feature in both retrieval precision and efficiency.