activity
20172020
most citedPrivacy-preserving Learning via Deep Net Pruning

8 citations · 11 across the 2 of their papers we have counts for

collaborators

7 papers

cs.CV2020

Cross-Domain Document Object Detection: Benchmark Suite and Method

Kai Li, Curtis Wigington, Chris Tensmeyer +6

Decomposing images of document pages into high-level semantic regions (e.g., figures, tables, paragraphs), document object detection (DOD) is fundamental for downstream tasks like…

cs.LG20208 cited

Privacy-preserving Learning via Deep Net Pruning

Yangsibo Huang, Yushan Su, Sachin Ravi +3

This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step to…

cs.CR2019

Cost-Effective Data Feeds to Blockchains via Workload-Adaptive Data Replication

Kai Li, Yuzhe Tang, Jiaqi Chen +3

Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications, notably stableco…

cs.CV2019

Visual Semantic Reasoning for Image-Text Matching

Kunpeng Li, Yulun Zhang, Kai Li +2

Image-text matching has been a hot research topic bridging the vision and language areas. It remains challenging because the current representation of image usually lacks global se…

cs.CV2019

Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective

Kai Li, Martin Renqiang Min, Yun Fu

Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely based on the semantic descriptions of the classes. Existing algorithms usually formulate it as a seman…

cs.NI2019

No Need of Data Pre-processing: A General Framework for Radio-Based Device-Free Context Awareness

Bo Wei, Kai Li, Chengwen Luo +2

Device-free context awareness is important to many applications. There are two broadly used approaches for device-free context awareness, i.e. video-based and radio-based. Video-ba…