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Yina Tang

dblp:40/10613 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0003-1497-8213ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 2 · 1 first-authorSystems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Image recognition and object detection · 40% Learning paradigms · 21% Representation and self-supervised learning · 21%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › representation learning › embedding learning
image embedding
0.412020
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce · KDD 2020
Machine learning › Learning paradigms
multi-task learning
0.412020
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce · KDD 2020
Computer vision › Image recognition and object detection › image classification
large-scale image classification
0.412019
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data · KDD 2019
Natural language and speech › Information extraction and text analysis › text classification
weakly supervised text classification
0.412019
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data · KDD 2019
Information retrieval
e-commerce search
0.112019
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data · KDD 2019
Information retrieval
image retrieval
0.112019
MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data · KDD 2019

Methods — techniques the papers use, named apart from their topics

weakly supervised learning · 0.8search log mining · 0.8multi-task learning · 0.4embedding loss · 0.4categorical loss · 0.4
YearPublicationVenuePosition
2020 GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
abstract
In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition system. We achieve this by training on 7 datasets across several commerce verticals, using 80 categorical loss functions and 3 embedding losses. We share our experience of combining diverse sources with wide-ranging label semantics and image statistics, including learning from human annotations, user-generated tags, and noisy search engine interaction data. GrokNet has demonstrated gains in production applications and operates at Facebook scale.
Sean Bell, Yiqun Liu 0006, Sami Alsheikh, Yina Tang, Edward Pizzi, M. Henning, Karun Singh, Omkar Parkhi, Fedor Borisyuk
KDD4
2019 MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data
abstract
In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace. In this paper, we present techniques we used to develop efficient large-scale image classifiers using weakly supervised search log data. We perform extensive evaluation of presented techniques, explain practical experience of developing large-scale classification systems and discuss challenges we faced. Our system, MSURU out-performed current state of the art system developed at Facebook [23] by 16% in e-commerce domain. MSURU is deployed to production with significant improvements in search success rate and active interactions on Facebook Marketplace.
Yina Tang, Fedor Borisyuk, Siddarth Malreddy, Yixuan Li 0001, Yiqun Liu 0006, Sergey Kirshner
KDD1
2012 A novel overlapping coil structure for dual band telemetry system
abstract
In this paper, a novel overlapping coil structure for dual band power and data telemetry systems is proposed. Through carefully choosing the coil structural parameters, overlapping structure can achieve both low power interference and ease of manufacturing at the same time. Two design examples are presented and analyzed. Compared with conventional structures which set coils on the same plane, the overlapping structure shows higher power interference rejection and better overall signal quality.
Peijun Wang, Yina Tang, Hui Wang 0023, Guoxing Wang
ISCAS2