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Xin Wang
Researcher at University of California, Berkeley
Publications - 36
Citations - 3735
Xin Wang is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Object detection & Computer science. The author has an hindex of 16, co-authored 31 publications receiving 1812 citations.
Papers
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Proceedings ArticleDOI
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Fisher Yu,Haofeng Chen,Xin Wang,Wenqi Xian,Yingying Chen,Fangchen Liu,Vashisht Madhavan,Trevor Darrell +7 more
TL;DR: This work constructs BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving and shows that special training strategies are needed for existing models to perform such heterogeneous tasks.
Proceedings ArticleDOI
Few-Shot Object Detection via Feature Reweighting
TL;DR: In this article, a few-shot object detector is proposed that can learn to detect novel objects from only a few annotated examples, using a meta feature learner and a reweighting module within a one-stage detection architecture.
Book ChapterDOI
SkipNet: Learning Dynamic Routing in Convolutional Networks
TL;DR: This work introduces SkipNet, a modified residual network, that uses a gating network to selectively skip convolutional blocks based on the activations of the previous layer, and proposes a hybrid learning algorithm that combines supervised learning and reinforcement learning to address the challenges of non-differentiable skipping decisions.
Proceedings Article
Clipper: a low-latency online prediction serving system
TL;DR: Clipper is introduced, a general-purpose low-latency prediction serving system that introduces a modular architecture to simplify model deployment across frameworks and applications and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks.
Posted Content
Frustratingly Simple Few-Shot Object Detection.
TL;DR: This work finds that fine-tuning only the last layer of existing detectors on rare classes is crucial to the few-shot object detection task, and establishes a new state of the art on the revised benchmarks.