31 citations · 78 across the 8 of their papers we have counts for
8 papers
Multi-Domain Joint Training for Person Re-Identification
Lu Yang, Lingqiao Liu, Yunlong Wang +2
Deep learning-based person Re-IDentification (ReID) often requires a large amount of training data to achieve good performance. Thus it appears that collecting more training data f…
SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations
Yunlong Wang, Jiaying Liu, Homin Park +4
Self-tracking can improve people's awareness of their unhealthy behaviors and support reflection to inform behavior change. Increasingly, new technologies make tracking easier, lea…
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
Yunlong Wang, Priyadarshini Venkatesh, Brian Y. Lim
Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This i…
Learning Instance-level Spatial-Temporal Patterns for Person Re-identification
Min Ren, Lingxiao He, Xingyu Liao +3
Person re-identification (Re-ID) aims to match pedestrians under dis-joint cameras. Most Re-ID methods formulate it as visual representation learning and image search, and its accu…
Center Prediction Loss for Re-identification
Lu Yang, Yunlong Wang, Lingqiao Liu +5
The training loss function that enforces certain training sample distribution patterns plays a critical role in building a re-identification (ReID) system. Besides the basic requir…
Instance and Pair-Aware Dynamic Networks for Re-Identification
Bingliang Jiao, Xin Tan, Jinghao Zhou +3
Re-identification (ReID) is to identify the same instance across different cameras. Existing ReID methods mostly utilize alignment-based or attention-based strategies to generate e…
Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation
Samuel Rhys Cox, Yunlong Wang, Ashraf Abdul +2
Crowdsourcing can collect many diverse ideas by prompting ideators individually, but this can generate redundant ideas. Prior methods reduce redundancy by presenting peers' ideas o…
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Yue Zhao, Xiyang Hu, Cheng Cheng +11
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and…