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Jason Wang

9 papers hereh-index 33.2k citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author5
  • last author2

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • cs.IR2
  • cs.CL1
  • cs.NI1
same name
  • Jason Wang — 10 papers
  • Jason Wang — 5 papers, h 5
  • Jason Wang — 3 papers, h 1
  • Jason Wang — 1 paper, h 2
  • Jason Wang — 1 paper, h 1
  • Jason Wang — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152022
most citedThe Effectiveness of Data Augmentation in Image Classification using Deep Learning

2.5k citations · 2.6k across the 9 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021★ 1 cited

EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation

Yanjun Gao, Lulu Liu, Jason Wang +3

Temporal grounding aims to predict a time interval of a video clip corresponding to a natural language query input. In this work, we present EVOQUER, a temporal grounding framework…

cs.CV2021

DeepWORD: A GCN-based Approach for Owner-Member Relationship Detection in Autonomous Driving

Zizhang Wu, Man Wang, Jason Wang +3

It's worth noting that the owner-member relationship between wheels and vehicles has an significant contribution to the 3D perception of vehicles, especially in the embedded enviro…

cs.IR2021

Hierarchical Topic Presence Models

Jason Wang, Robert E. Weiss

Topic models analyze text from a set of documents. Documents are modeled as a mixture of topics, with topics defined as probability distributions on words. Inferences of interest i…

cs.IR2021

Local and Global Topics in Text Modeling of Web Pages Nested in Web Sites

Jason Wang, Robert E. Weiss

Topic models are popular models for analyzing a collection of text documents. The models assert that documents are distributions over latent topics and latent topics are distributi…

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