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Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition

Published: 29 October 2023 Publication History

Abstract

In the field of fine-grained food recognition, subset learning-based methods offer a strategic approach that groups classes into subsets to guide the training process. Our study introduces a novel approach, referred to as the Dining on Details (DoD), an innovative expert learning framework for food classification. This method ingeniously harnesses the power of large language models to construct subsets of classes within the dataset. The Dining on Details's efficacy is rooted in the robustness of the ImageBind multi-modality embedding space, which can identify meaningful similarities across varied categories. Trained through an end-to-end multi-task learning process, this method enhances performance in the fine-grained food recognition task, showing exceptional prowess with highly similar classes. A key advantage of DoD is its universal compatibility, allowing it to be applied seamlessly to any existing classification architecture. Our comprehensive validation of this method on various food datasets and backbones, both convolutional and transformer-based, reveals competitive results with significant performance gains ranging from 0.5% to 1.61%. Notably, it achieves state-of-the-art results on the Food-101 dataset.

References

[1]
Eduardo Aguilar, Beatriz Remeseiro, Marc Bola nos, and Petia Radeva. 2018. Grab, pay, and eat: Semantic food detection for smart restaurants. IEEE Transactions on Multimedia, Vol. 20, 12 (2018), 3266--3275.
[2]
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. (2023).
[3]
Ardhendu Behera, Zachary Wharton, Pradeep RPG Hewage, and Asish Bera. 2021. Context-Aware Attentional Pooling (CAP) for Fine-Grained Visual Classification. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 929--937.
[4]
Marc Bola nos, Marc Valdivia, and Petia Radeva. 2019. Where and what am i eating image-based food menu recognition. In Computer Vision--ECCV 2018 Workshops: Munich, Germany, September 8--14, 2018, Proceedings, Part VI 15. Springer, 590--605.
[5]
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. 2014. Food-101 -- Mining Discriminative Components with Random Forests. In European Conference on Computer Vision.
[6]
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023).
[7]
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al. 2018. Universal Sentence Encoder for English. In Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations. 169--174.
[8]
Dongliang Chang, Kaiyue Pang, Yixiao Zheng, Zhanyu Ma, Yi-Zhe Song, and Jun Guo. 2021. Your" Flamingo" is my" Bird": fine-grained, or not. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11476--11485.
[9]
Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, and Chun Chen. 2020. Online Knowledge Distillation with Diverse Peers. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 3430--3437.
[10]
Joachim Dehais, Marios Anthimopoulos, and Stavroula Mougiakakou. 2016. Gocarb: A smartphone application for automatic assessment of carbohydrate intake. In Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management. 91--91.
[11]
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In 2009 IEEE conference on computer vision and pattern recognition. IEEE, 248--255.
[12]
Yao Ding, Yanzhao Zhou, Yi Zhu, Qixiang Ye, and Jianbin Jiao. 2019. Selective Sparse Sampling for Fine-Grained Image Recognition. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 6598--6607. https://doi.org/10.1109/ICCV.2019.00670
[13]
Abhimanyu Dubey, Otkrist Gupta, Pei Guo, Ramesh Raskar, Ryan Farrell, and Nikhil Naik. 2018a. Pairwise Confusion for Fine-Grained Visual Classification. In Proceedings of the European conference on computer vision (ECCV). 70--86.
[14]
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik. 2018b. Maximum-Entropy Fine Grained Classification. Advances in neural information processing systems, Vol. 31 (2018).
[15]
ZongYuan Ge, Chris McCool, Conrad Sanderson, Alex Bewley, Zetao Chen, and Peter Corke. 2015b. Fine-grained bird species recognition via hierarchical subset learning. In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 561--565.
[16]
ZongYuan Ge, Chris McCool, Conrad Sanderson, Alex Bewley, Zetao Chen, and Peter Corke. 2015c. Fine-Grained Bird Species Recognition via Hierarchical Subset Learning. In 2015 IEEE International Conference on Image Processing (ICIP). 561--565. https://doi.org/10.1109/ICIP.2015.7350861
[17]
ZongYuan Ge, Christopher McCool, Conrad Sanderson, and Peter Corke. 2015a. Subset Feature Learning for Fine-Grained Category Classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 46--52.
[18]
Andrea Gesmundo. 2022. A continual development methodology for large-scale multitask dynamic ml systems. arXiv preprint arXiv:2209.07326 (2022).
[19]
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. 2023. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 15180--15190.
[20]
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 580--587.
[21]
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021. Knowledge Distillation: A Survey. International Journal of Computer Vision, Vol. 129 (2021), 1789--1819.
[22]
Qiushan Guo, Xinjiang Wang, Yichao Wu, Zhipeng Yu, Ding Liang, Xiaolin Hu, and Ping Luo. 2020. Online Knowledge Distillation via Collaborative Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11020--11029.
[23]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.
[24]
Ming He, Guangyi Lv, Weidong He, Jianping Fan, and Guihua Zeng. 2021. DeepME: Deep Mixture Experts for Large-scale Image Classification. In IJCAI. 722--728.
[25]
Xiangteng He and Yuxin Peng. 2017. Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31, 1 (Feb. 2017). https://doi.org/10.1609/aaai.v31i1.11223
[26]
Shaoli Huang, Xinchao Wang, and Dacheng Tao. 2021. SnapMix: Semantically Proportional Mixing for Augmenting Fine-Grained Data. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 1628--1636.
[27]
Zixuan Huang and Yin Li. 2020. Interpretable and Accurate Fine-Grained Recognition via Region Grouping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8662--8672.
[28]
Qiu Jianing, Frank Po Wen Lo, Yingnan Sun, Siyao Wang, and Benny Lo. 2019. Mining discriminative food regions for accurate food recognition. In Proceedings of the British Machine Vision Conference. Cardiff, UK, Vol. 158.
[29]
Salaki Reynaldo Joshua, Seungheon Shin, Je-Hoon Lee, and Seong Kun Kim. 2023. Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients' Nutrition Control. Sensors, Vol. 23, 3 (2023), 1656.
[30]
Parneet Kaur, Karan Sikka, Weijun Wang, serge Belongie, and Ajay Divakaran. 2019. FoodX-251: A Dataset for Fine-grained Food Classification. arXiv preprint arXiv:1907.06167 (2019).
[31]
Rajdeep Kaur, Rakesh Kumar, and Meenu Gupta. 2023. Deep neural network for food image classification and nutrient identification: A systematic review. Reviews in Endocrine and Metabolic Disorders (2023), 1--21.
[32]
Xiao Ke, Yuhang Cai, Baitao Chen, Hao Liu, and Wenzhong Guo. 2023. Granularity-Aware Distillation and Structure Modeling Region Proposal Network for Fine-Grained Image Classification. Pattern Recognition, Vol. 137 (2023), 109305. https://doi.org/10.1016/j.patcog.2023.109305
[33]
Nadine Alvina Kong, Foong Ming Moy, Shu Hwa Ong, Ghalib Ahmed Tahir, and Choo Kiong Loo. 2023. MyDietCam: Development and usability study of a food recognition integrated dietary monitoring smartphone application. Digital Health, Vol. 9 (2023), 20552076221149320.
[34]
Fotios S Konstantakopoulos, Eleni I Georga, and Dimitrios I Fotiadis. 2023. A Review of Image-based Food Recognition and Volume Estimation Artificial Intelligence Systems. IEEE Reviews in Biomedical Engineering (2023).
[35]
Jonathan Krause, Hailin Jin, Jianchao Yang, and Li Fei-Fei. 2015. Fine-Grained Recognition without Part Annotations. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5546--5555. https://doi.org/10.1109/CVPR.2015.7299194
[36]
Solomon Kullback and Richard A Leibler. 1951. On Information and Sufficiency. The annals of mathematical statistics, Vol. 22, 1 (1951), 79--86.
[37]
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023).
[38]
Zijia Lin, Guiguang Ding, Jungong Han, and Ling Shao. 2017. End-to-End Feature-Aware Label Space Encoding for Multilabel Classification with Many Classes. IEEE transactions on neural networks and learning systems, Vol. 29, 6 (2017), 2472--2487.
[39]
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al. 2022. Swin Transformer V2: Scaling Up Capacity and Resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 12009--12019.
[40]
Merieme Mansouri, Samia Benabdellah Chaouni, Said Jai Andaloussi, and Ouail Ouchetto. 2023. Deep Learning for Food Image Recognition and Nutrition Analysis Towards Chronic Diseases Monitoring: A Systematic Review. SN Computer Science, Vol. 4, 5 (2023), 513.
[41]
Javier Marin, Aritro Biswas, Ferda Ofli, Nicholas Hynes, Amaia Salvador, Yusuf Aytar, Ingmar Weber, and Antonio Torralba. 2021. Recipe1m: A dataset for learning cross-modal embeddings for cooking recipes and food images. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, 1 (2021), 187--203.
[42]
Y. Matsuda, H. Hoashi, and K. Yanai. 2012. Recognition of Multiple-Food Images by Detecting Candidate Regions. In Proc. of IEEE International Conference on Multimedia and Expo (ICME).
[43]
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Grossberger. 2018. UMAP: Uniform Manifold Approximation and Projection. The Journal of Open Source Software, Vol. 3, 29 (2018), 861.
[44]
Weiqing Min, Shuqiang Jiang, Linhu Liu, Yong Rui, and Ramesh Jain. 2019a. A survey on food computing. ACM Computing Surveys (CSUR), Vol. 52, 5 (2019), 1--36.
[45]
Weiqing Min, Linhu Liu, Zhengdong Luo, and Shuqiang Jiang. 2019b. Ingredient-guided cascaded multi-attention network for food recognition. In Proceedings of the 27th ACM International Conference on Multimedia. 1331--1339.
[46]
Weiqing Min, Zhiling Wang, Yuxin Liu, Mengjiang Luo, Liping Kang, Xiaoming Wei, Xiaolin Wei, and Shuqiang Jiang. 2023. Large scale visual food recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023).
[47]
Bhalaji Nagarajan, Rupali Khatun, Marc Bola nos, Eduardo Aguilar, Leonardo Angelini, Mira El Kamali, Elena Mugellini, Omar Abou Khaled, Noemi Boqué, Lucia Tarro, et al. 2021. Nutritional monitoring in older people prevention services. In Digital Health Technology for Better Aging: A Multidisciplinary Approach. Springer, 77--102.
[48]
Changhwa Park, Junho Yim, and Eunji Jun. 2023. Mutual Learning for Long-Tailed Recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2675--2684.
[49]
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023 a. The RefinedWeb dataset for Falcon LLM: outperforming curated corpora with web data, and web data only. arXiv preprint arXiv:2306.01116 (2023). showeprint[arXiv]2306.01116 https://arxiv.org/abs/2306.01116
[50]
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023 b. The RefinedWeb dataset for Falcon LLM: outperforming curated corpora with web data, and web data only. arXiv preprint arXiv:2306.01116 (2023).
[51]
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763.
[52]
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. http://arxiv.org/abs/1908.10084
[53]
Javier Ródenas, Bhalaji Nagarajan, Marc Bola nos, and Petia Radeva. 2022. Learning Multi-Subset of Classes for Fine-Grained Food Recognition. In Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management. 17--26.
[54]
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. In Proceedings of the IEEE international conference on computer vision. 618--626.
[55]
Marcel Simon and Erik Rodner. 2015. Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks. In Proceedings of the IEEE international conference on computer vision. 1143--1151.
[56]
Leslie N Smith and Nicholay Topin. 2019. Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates. In Artificial intelligence and machine learning for multi-domain operations applications, Vol. 11006. SPIE, 369--386.
[57]
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2020. Mpnet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, Vol. 33 (2020), 16857--16867.
[58]
Mohammed Ahmed Subhi, Sawal Hamid Ali, and Mohammed Abulameer Mohammed. 2019. Vision-based approaches for automatic food recognition and dietary assessment: A survey. IEEE Access, Vol. 7 (2019), 35370--35381.
[59]
Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International conference on machine learning. PMLR, 6105--6114.
[60]
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).
[61]
Hugo Touvron, Alexandre Sablayrolles, Matthijs Douze, Matthieu Cord, and Hervé Jégou. 2021. Grafit: Learning Fine-Grained Image Representations with Coarse Labels. In Proceedings of the IEEE/CVF international conference on computer vision. 874--884.
[62]
Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas. 2008. Effective and Efficient Multilabel Classification in Domains with Large Number of Labels. In Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD'08), Vol. 21. 53--59.
[63]
Hao Wang, Guosheng Lin, Steven CH Hoi, and Chunyan Miao. 2020b. Structure-aware generation network for recipe generation from images. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXVII 16. Springer, 359--374.
[64]
Xin Wang, Thomas E Huang, Trevor Darrell, Joseph E Gonzalez, and Fisher Yu. 2020a. Frustratingly Simple Few-Shot Object Detection. In Proceedings of the 37th International Conference on Machine Learning. 9919--9928.
[65]
Yunan Wang, Jing-jing Chen, Chong-Wah Ngo, Tat-Seng Chua, Wanli Zuo, and Zhaoyan Ming. 2019. Mixed Dish Recognition through Multi-Label Learning. In Proceedings of the 11th Workshop on Multimedia for Cooking and Eating Activities. 1--8.
[66]
Yaming Wang, Vlad I Morariu, and Larry S Davis. 2018. Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4148--4157.
[67]
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, and Serge Belongie. 2021. Fine-Grained Image Analysis with Deep Learning: A Survey. IEEE transactions on pattern analysis and machine intelligence, Vol. 44, 12 (2021), 8927--8948.
[68]
Matthew D Zeiler and Rob Fergus. 2014. Visualizing and Understanding Convolutional Networks. In European conference on computer vision. Springer, 818--833.
[69]
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068 (2022).
[70]
Xiaopeng Zhang, Hongkai Xiong, Wengang Zhou, Weiyao Lin, and Qi Tian. 2016b. Picking Deep Filter Responses for Fine-Grained Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1134--1142. https://doi.org/10.1109/CVPR.2016.128
[71]
Yu Zhang, Xiu-Shen Wei, Jianxin Wu, Jianfei Cai, Jiangbo Lu, Viet-Anh Nguyen, and Minh N. Do. 2016a. Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation. IEEE Transactions on Image Processing, Vol. 25, 4 (2016), 1713--1725. https://doi.org/10.1109/TIP.2016.2531289
[72]
Ying Zhang, Tao Xiang, Timothy M Hospedales, and Huchuan Lu. 2018. Deep Mutual Learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4320--4328.
[73]
Jiannan Zheng, Liang Zou, and Z Jane Wang. 2018. Mid-level deep Food Part mining for food image recognition. IET Computer Vision, Vol. 12, 3 (2018), 298--304.
[74]
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. 2016. Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2921--2929.
[75]
Feng Zhou and Yuanqing Lin. 2016. Fine-grained image classification by exploring bipartite-graph labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1124--1133.

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    cover image ACM Conferences
    MADiMa '23: Proceedings of the 8th International Workshop on Multimedia Assisted Dietary Management
    October 2023
    94 pages
    ISBN:9798400702846
    DOI:10.1145/3607828
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    Published: 29 October 2023

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    Author Tags

    1. fine-grained classification
    2. food image recognition
    3. large language models
    4. multi-modal learning
    5. subset learning

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