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Hailiang Zhu

dblp:284/6385 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0006-4550-3826ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

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.

Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 56% User interface design and tools · 44%
Artificial intelligence
1 paper
Autonomous driving · 56% Image recognition and object detection · 44%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection
object recognition
0.912025
ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones · ICCV 2025
Robotics › Autonomous driving
perception and planning
0.912025
ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones · ICCV 2025
Collaborative and social computing › collaborative learning
reflective thinking
0.312026
CommSense: Facilitating Bias-Aware and Reflective Navigation of Online Comments for Rational Judgment · CHI 2026
Robotics › Autonomous driving
driving scene understanding
0.312025
ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones · ICCV 2025

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

co-design workshops · 1.0between-subject evaluation · 1.0
YearPublicationVenuePosition
2026 CommSense: Facilitating Bias-Aware and Reflective Navigation of Online Comments for Rational Judgment
abstract
Online comments significantly influence users’ judgments, yet their presentation, often determined by platform algorithms, can introduce biases, such as anchoring effects, which distort reasoning. While existing research emphasizes mitigating individual cognitive biases, the evolution of user judgments during comment engagement remains overlooked. This study investigates how presentation cues impact reasoning and explores interface design strategies to mitigate bias. Through a preliminary experiment (N=18) and a co-design workshop, we identified key challenges users face across a four-stage process and distilled four design requirements: pre-engagement framing, interactive organization, reflective prompts, and synthesis support. Based on these insights, we developed CommSense, an on-the-fly plugin that enhances user engagement with online comments by providing visual overviews and lightweight prompts to guide reasoning. A between-subject evaluation (N=24) demonstrates that CommSense improves bias awareness and reflective thinking, helping users produce more comprehensive, evidence-based rationales while maintaining high usability.
Yang Ouyang, Ruichuan Wang, Hailiang Zhu, Yuheng Shao, Xiaoyu Gu, Quan Li 0002
CHI4
2025 ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones
Anurag Ghosh, Robert Tamburo, Khiem Vuong, Juan R. Alvarez-Padilla, Hailiang Zhu, Michael Cardei, Nicholas Dunn, Christoph Mertz, Srinivasa G. Narasimhan
ICCV6