2 citations · 3 across the 28 of their papers we have counts for
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MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds
Jinge Ma, Xiaoyan Zhang, Gautham Vinod +3
Food portion estimation is crucial for monitoring health and tracking dietary intake. Image-based dietary assessment, which involves analyzing eating occasion images using computer…
High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
Ming Lu, Zhihao Duan, Wuyang Cong +3
The enhanced Deep Hierarchical Video Compression-DHVC 2.0-has been introduced. This single-model neural video codec operates across a broad range of bitrates, delivering not only s…
MetaFood3D: 3D Food Dataset with Nutrition Values
Yuhao Chen, Jiangpeng He, Gautham Vinod +11
Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets…
FMiFood: Multi-modal Contrastive Learning for Food Image Classification
Xinyue Pan, Jiangpeng He, Fengqing Zhu
Food image classification is the fundamental step in image-based dietary assessment, which aims to estimate participants' nutrient intake from eating occasion images. A common chal…
MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results
Jiangpeng He, Yuhao Chen, Gautham Vinod +16
The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. How…
On Efficient Neural Network Architectures for Image Compression
Yichi Zhang, Zhihao Duan, Fengqing Zhu
Recent advances in learning-based image compression typically come at the cost of high complexity. Designing computationally efficient architectures remains an open challenge. In t…