All Outputs (15)

SID-NERF: Few-Shot Nerf Based on Scene Information Distribution (2024)
Presentation / Conference Contribution
Li, Y., Wan, F., & Long, Y. (2024, July). SID-NERF: Few-Shot Nerf Based on Scene Information Distribution. Presented at 2024 IEEE International Conference on Multimedia and Expo (ICME), Niagara Falls, ON, Canada

The novel view synthesis from a limited set of images is a significant research focus. Traditional NeRF methods, relying mainly on color supervision, struggle with accurate scene geometry reconstruction when faced with sparse input images, leading to... Read More about SID-NERF: Few-Shot Nerf Based on Scene Information Distribution.

Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance (2023)
Presentation / Conference Contribution
Aduragba, T. O., Yu, J., Cristea, A. I., & Long, Y. (2023, April). Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance. Presented at WWW '23: The ACM Web Conference 2023, Austin, Texas

People often use disease or symptom terms on social media and online forums in ways other than to describe their health. Thus the NLP health mention classification (HMC) task aims to identify posts where users are discussing health conditions literal... Read More about Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance.

A General Transductive Regularizer for Zero-Shot Learning (2019)
Presentation / Conference Contribution
Mao, H., Zhang, H., Long, Y., Wang, S., & Yang, L. (2019, December). A General Transductive Regularizer for Zero-Shot Learning. Presented at BMVC

Order Matters: Shuffling Sequence Generation for Video Prediction (2019)
Presentation / Conference Contribution
Wang, J., Hu, B., Long, Y., & Guan, Y. (2019, December). Order Matters: Shuffling Sequence Generation for Video Prediction. Presented at BMVC

Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding (2019)
Presentation / Conference Contribution
Huang, Y., Long, Y., & Wang, L. (2019, December). Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding. Presented at Thirty-Second AAAI Conference on Artificial Intelligence

Word similarity and word relatedness are fundamental to natural language processing and more generally, understanding how humans relate concepts in semantic memory. A growing number of datasets are being proposed as evaluation benchmarks,however, the... Read More about Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding.

Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification (2018)
Presentation / Conference Contribution
Long, Y., Tan, Y., Organisciak, D., Yang, L., & Shao, L. (2018, December). Towards light-weight annotations: Fuzzy interpolative reasoning for zero-shot image classification. Presented at BMVC

Enhancing apparel data based on fashion theory for developing a novel apparel style recommendation system (2018)
Presentation / Conference Contribution
Guan, C., Qin, S., Ling, W., & Long, Y. (2018, December). Enhancing apparel data based on fashion theory for developing a novel apparel style recommendation system. Presented at World Conference on Information Systems and Technologies Springer, Cham

Adaptive Visual-Depth Fusion Transfer (2018)
Presentation / Conference Contribution
Cai, Z., Long, Y., & Shao, L. (2018, December). Adaptive Visual-Depth Fusion Transfer. Presented at ACCV

Towards Universal Representation for Unseen Action Recognition (2018)
Presentation / Conference Contribution
Zhu, Y., Long, Y., Guan, Y., Newsam, S., & Shao, L. (2018, December). Towards Universal Representation for Unseen Action Recognition. Presented at 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) IEEE; CVF; IEEE Comp Soc, 345 E 47TH ST, NEW YORK, NY 10017 USA

Towards affordable semantic searching: Zero-shot retrieval via dominant attributes (2018)
Presentation / Conference Contribution
Long, Y., Liu, L., Shen, Y., & Shao, L. (2018, December). Towards affordable semantic searching: Zero-shot retrieval via dominant attributes. Presented at Thirty-Second AAAI Conference on Artificial Intelligence

Instance-level retrieval has become an essential paradigm to index and retrieves images from large-scale databases. Conventional instance search requires at least an example of the query image to retrieve images that contain the same object instance.... Read More about Towards affordable semantic searching: Zero-shot retrieval via dominant attributes.

From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis (2017)
Presentation / Conference Contribution
Long, Y., Liu, L., Shao, L., Shen, F., Ding, G., & Han, J. (2017, December). From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis. Presented at Computer Vision and Pattern Recognition IEEE

Learning to recognise unseen classes by a few similes (2017)
Presentation / Conference Contribution
Long, Y., & Shao, L. (2017, December). Learning to recognise unseen classes by a few similes. Presented at Proceedings of the 25th ACM international conference on Multimedia ACM

Towards fine-grained open zero-shot learning: Inferring unseen visual features from attributes (2017)
Presentation / Conference Contribution
Long, Y., Liu, L., & Shao, L. (2017, December). Towards fine-grained open zero-shot learning: Inferring unseen visual features from attributes. Presented at 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) IEEE

Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble (2017)
Presentation / Conference Contribution
Long, Y., & Shao, L. (2017, December). Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble. Presented at 2017 IEEE winter conference on applications of computer vision (WACV) IEEE

Attribute embedding with visual-semantic ambiguity removal for zero-shot learning (2016)
Presentation / Conference Contribution
Long, Y., Liu, L., & Shao, L. (2016, December). Attribute embedding with visual-semantic ambiguity removal for zero-shot learning. Presented at BMVC