Food-101 - Mining Discriminative Components with Random Forests

By Lukas Bossard, Matthieu Guillaumin, Luc Van Gool. 2014

3,429 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Plant Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Yi Yang
2. Trevor Darrell
3. Chong-Wah Ngo
4. Jingjing Chen
5. Juergen Gall
6. Yoshiyuki Kawano
7. Keiji Yanai
8. Quoc V. Le

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Ming-Hsuan Yang
4. Trevor Darrell
5. Xiaogang Wang
6. Jitendra Malik
7. Andrew Zisserman
8. Andrea Vedaldi

In Plant Science

1. Quoc V. Le
2. Keiji Yanai
3. Yoshiyuki Kawano
4. Phillip Isola
5. Chuang Gan
6. Song Han
7. Ligeng Zhu
8. Tinne Tuytelaars

Citations in the corpus

• Do Better ImageNet Models Transfer Better? (2018, background): “These datasets covered a wide range of image classification tasks, including superordinate-level object classification (CIFAR-10 [37], CIFAR-100 [37], PASCAL VOC 2007 [19], Caltech-101 [20]); fine-grained object classification (Food-101 [5], Birdsnap [4], Stanford Cars [36], FGVC Aircraft [48], Oxfo”
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019, neutral): “(Maji et al., 2013) 6,667 3,333 100 Oxford-IIIT Pets (Parkhi et al., 2012) 3,680 3,369 37 Food-101 (Bossard et al., 2014) 75,750 25,250 101”
• Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020, methodology): “[89] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool.”
• Grafit: Learning fine-grained image representations with coarse labels (2020, methodology): “Flowers-102 [41] 2,040 6,149 102 Stanford Cars [35] 8,144 8,041 196 Food101 [7] 75,750 25,250 101”
• Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time (2022, methodology): “, 2014), Food-101 (Bossard et al., 2014), SUN397 (Xiao et al.”
• Learning to Prompt for Vision-Language Models (2021, background): “We observe that a few words are somewhat relevant to the tasks, such as “enjoyed” for Food101, “fluffy” and “paw” for OxfordPets, and “pretty” for DTD.”
• Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement (2023, methodology): “…ImageNet + dataset, we evaluate on following tasks: (1) semantic segmentation with DeepLabv3 [11] on the ADE20K dataset [74], (2) object detection with Mask-RCNN [20] on the MS-COCO dataset [34], and (3) fine-grained classification on the CIFAR-100 [31], Flowers-102 [45], and Food-101 [8] datasets.”
• LAION-5B: An open large-scale dataset for training next generation image-text models (2022, methodology): “We evaluate performance on Food101 [7], Cars [35], CIFAR-10 & 100 [37], DTD [12] and SUN397 [85].”
• Generative Negative Text Replay for Continual Vision-Language Pretraining (2022, methodology): “Concretely, we evaluate models on eight representative datasets including CIFAR10 [26], CIFAR-100 [26], Caltech101 [17], Oxford 102 Flower(Flowers102) [32], Food101 [6], SUN397 [5], Describable Textures Dataset (DTD) [12], and ImageNet [14].”
• How Well Does Self-Supervised Pre-Training Perform with Streaming Data? (2021, methodology): “Following (Chen et al., 2020a), we consider 12 diverse image classification datasets including Food-101 (Bossard et al., 2014), CIFAR10 (Krizhevsky et al., 2009), CIFAR100 (Krizhevsky et al., 2009), Birdsnap (Berg et al., 2014), SUN397 (Xiao et al., 2010), Standard Cars (Krause et al., 2013), FGVC Ai”
• Incremental Learning in Online Scenario (2020, methodology): “Finally, we demonstrate how our complete framework can be implemented for food image classification using the Food-101 [1] dataset.”