L
Liangliang Cao
Researcher at Google
Publications - 163
Citations - 7519
Liangliang Cao is an academic researcher from Google. The author has contributed to research in topics: Computer science & TRECVID. The author has an hindex of 39, co-authored 160 publications receiving 6515 citations. Previous affiliations of Liangliang Cao include IBM & Microsoft.
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Proceedings ArticleDOI
Learning Locally-Adaptive Decision Functions for Person Verification
TL;DR: The decision function for verification is proposed to be viewed as a joint model of a distance metric and a locally adaptive thresholding rule, and the inference on the decision function is formulated as a second-order large-margin regularization problem, and an efficient algorithm is provided in its dual from.
Proceedings ArticleDOI
Learning from Noisy Labels with Distillation
TL;DR: This work proposes a unified distillation framework to use “side” information, including a small clean dataset and label relations in knowledge graph, to “hedge the risk” of learning from noisy labels, and proposes a suite of new benchmark datasets to evaluate this task in Sports, Species and Artifacts domains.
Proceedings ArticleDOI
Large-scale image classification: Fast feature extraction and SVM training
Yuanqing Lin,Fengjun Lv,Shenghuo Zhu,Ming Yang,Timothee Cour,Kai Yu,Liangliang Cao,Thomas S. Huang +7 more
TL;DR: A parallel averaging stochastic gradient descent (ASGD) algorithm for training one-against-all 1000-class SVM classifiers and a Hadoop scheme that performs feature extraction in parallel using hundreds of mappers, which achieves state-of-the-art performance on the ImageNet 1000- class classification.
Proceedings ArticleDOI
Spatially Coherent Latent Topic Model for Concurrent Segmentation and Classification of Objects and Scenes
Liangliang Cao,Li Fei-Fei +1 more
TL;DR: Spatial-LTM represents an image containing objects in a hierarchical way by over-segmented image regions of homogeneous appearances and the salient image patches within the regions, enforcing the spatial coherency of the model.
Proceedings ArticleDOI
Geographical topic discovery and comparison
TL;DR: The results confirm the hypothesis that the geographical distributions can help modeling topics, while topics provide important cues to group different geographical regions.