8 citations · 21 across the 16 of their papers we have counts for
7 papers · 1 filter
LLMs-based Augmentation for Domain Adaptation in Long-tailed Food Datasets
Qing Wang, Chong-Wah Ngo, Ee-Peng Lim +1
Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured…
Towards Unbiased Cross-Modal Representation Learning for Food Image-to-Recipe Retrieval
Qing Wang, Chong-Wah Ngo, Ee-Peng Lim
This paper addresses the challenges of learning representations for recipes and food images in the cross-modal retrieval problem. As the relationship between a recipe and its cooke…
Mitigating Cross-modal Representation Bias for Multicultural Image-to-Recipe Retrieval
Qing Wang, Chong-Wah Ngo, Yu Cao +1
Existing approaches for image-to-recipe retrieval have the implicit assumption that a food image can fully capture the details textually documented in its recipe. However, a food i…
Class Agnostic Instance-level Descriptor for Visual Instance Search
Qi-Ying Sun, Wan-Lei Zhao, Hui-Ying Xie +2
Despite the great success of the deep features in content-based image retrieval, the visual instance search remains challenging due to the lack of effective instance-level feature…
Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion
Huiyan Qi, Bin Zhu, Chong-Wah Ngo +2
Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingre…
CookingDiffusion: Cooking Procedural Image Generation with Stable Diffusion
Yuan Wang, Bin Zhu, Yanbin Hao +3
Recent advancements in text-to-image generation models have excelled in creating diverse and realistic images. This success extends to food imagery, where various conditional input…
PosMLP-Video: Spatial and Temporal Relative Position Encoding for Efficient Video Recognition
Yanbin Hao, Diansong Zhou, Zhicai Wang +2
In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such a…