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REVIEW 4 major objections 5 minor 1 cited by

CaLoRAify: Calorie Estimation with Visual-Text Pairing and LoRA-Driven Visual Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read CaLoRAify: one food photo yields ingredient and calorie estimates

desk verdict A well-intentioned fine-tuning exercise with a useful open-source dataset, but the calorie-accuracy claim is unmeasured and the dataset numbers do not add up. read the letter →

arxiv 2412.09936 v1 pith:KIXJJLU4 submitted 2024-12-13 cs.CV

Dongyu Yao , Keling Yao , Junhong Zhou , Yinghao Zhang This is my paper · ORCID
classification cs.CV
keywords CalorieestimationVision-languagemodelLow-rankadaptationRetrieval-augmentedgenerationFoodimagerecognitionIngredientanalysisImage-textdatasetUSDAnutritiondatabase
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to show that calorie estimation can be reduced to a single-image task by pairing vision-language training with retrieval. It introduces CaLoRAify, a MiniGPT-v2-based system fine-tuned with LoRA on CalData, a dataset of 330K image-text pairs built from Recipe1M+ and nutrition instructions, so that at inference a user supplies one food photo and receives ingredient quantities and calorie estimates. The authors argue this approach replaces traditional multi-step pipelines that depend on reference objects, depth maps, or multi-view cameras, and that retrieval-augmented generation grounded in the USDA database reduces hallucinated nutrition values. If the central claim holds, food-photo calorie tracking could become practical on ordinary mobile hardware without extra metadata.

What carries the argument

The central object is CalData, a dataset of 330K image-text pairs created by sampling 5,801 recipes from Recipe1M+, pairing each with up to five images and five instruction sets, and adding nutrition facts. The carrying mechanism is the CaLoRAify inference chain: a frozen Vision Transformer encodes the image, the LLaMA-2 backbone under a [vqa] task identifier generates ingredient and quantity predictions, LoRA (rank 64, alpha 16) adapts the model efficiently, and a Sentence-BERT encoder plus a USDA vector database retrieves nutrition facts that are concatenated back into the generation context.

What would settle it

Compute per-sample calorie error on a held-out set of food images with ground-truth kilocalorie values, for example mean absolute error and correlation between predicted and true calories, for both the baseline and fine-tuned models; if fine-tuning improves text-overlap metrics but not calorie error, the claimed accuracy improvement is not about calorie estimation.

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Extended reading notes

Core claim

The paper claims that a vision-language model fine-tuned on the CalData image-text pairs can accept a single monocular food image at inference and output ingredient names, quantities, and a calorie estimate. Ingredient recognition is used as an intermediate step, and a retrieval-augmented generation module then queries the USDA nutritional database before the model produces its final answer. The paper reports that fine-tuning improves text-overlap metrics over the unfine-tuned MiniGPT-4 baseline, with the reported aggregate metric rising 8.16%, and takes this as evidence of improved accuracy for calorie estimation.

Load-bearing premise

The central claim depends on text-overlap metrics standing in for calorie accuracy, because the paper's only quantitative evidence is ROUGE, BLEU, and BERTScore against reference text and includes no comparison between predicted and true calorie values.

Editorial extensions

If this is right

  • Users can photograph a dish with an ordinary phone and receive ingredient names, quantities, and a calorie estimate, with no reference object, depth map, or multi-view rig.
  • Because the RAG module retrieves nutrition facts from the USDA database using the predicted ingredients, the final calorie number is grounded in external data rather than generated purely from memory.
  • Fine-tuning with LoRA on 330K image-text pairs improves the model's text-overlap scores against the unfine-tuned baseline, including ROUGE-L, BLEU, and BERTScore.
  • The same visual-text training and retrieval pipeline can be adapted to other food-related vertical tasks, such as recipe generation or personalized dietary advice.
  • The system retains conversational interaction, so users can ask follow-up questions about the estimated meal rather than receiving only a static number.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: if the ingredient-recognition step misnames an ingredient, the retrieval step will faithfully return nutrition facts for the wrong food, so calorie error will inherit ingredient error; a direct end-to-end calorie-error evaluation would reveal this coupling.
  • Editorial extension: because ROUGE and BLEU reward phrase overlap with template reference answers, part of the reported gain may reflect the model learning answer formatting rather than better visual calorie perception; testing on paraphrased calorie answers would disentangle the two.
  • Editorial extension: real phone photos have varied lighting, angles, and occlusions, so the relative gains observed here could be re-tested on a naturally captured meal dataset to see whether they persist outside Recipe1M+ imagery.
  • Editorial extension: the two-stage design uses one retriever for nutrition facts, but a comparative test of different retrievers or different nutrition databases could show how much of the final calorie estimate depends on retrieval quality versus the vision-language model's ingredient prediction.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces CaLoRAify, a vision-language model framework for ingredient recognition and calorie estimation from a single monocular food image. The authors curate CalData, an image-text dataset built from Recipe1M+ with nutritional instructions and question paraphrases, and fine-tune MiniGPT-v2 using LoRA, with a RAG component that retrieves nutritional facts from a USDA database. The paper claims that the fine-tuned system improves accuracy over the baseline, citing text-overlap metrics in Table 1. The reported experiments, however, contain only ROUGE, BLEU, SacreBLEU, and BERTScore values, with an aggregate metric defined as a weighted average of ROUGE-L and BLEU. No calorie-level ground-truth evaluation is reported, and the dataset description contains internal numerical inconsistencies.

Significance. If validated, the system would address a practical need: calorie estimation from a single food image with conversational flexibility, supported by an openly released dataset and a RAG component grounded in USDA data. The authors are to be credited for curating a domain-specific dataset, for describing a clean LoRA fine-tuning recipe, and for committing to open-source code and data. However, the central claim of accurate calorie estimation is not supported by the evidence presented. The quantitative evaluation measures text overlap between generated and reference strings, not the correctness of predicted calorie values, and no ablation isolates the contributions of LoRA, RAG, or the dataset. The dataset size statements are mutually inconsistent, further weakening confidence in the evaluation. As presented, the paper does not establish its headline contribution.

major comments (4)
  1. [§5.3, Table 1 and Eq. (1)] The only quantitative evidence for the paper's central claim is Table 1, which reports ROUGE, BLEU, SacreBLEU, and BERTScore between generated text and reference text, plus an aggregate Lagg = λ_rouge L_rouge + λ_bleu L_bleu with unspecified weights. These are text-overlap and embedding-similarity metrics; they do not measure whether the predicted calorie number matches the true calorie content of the depicted dish. A model could reproduce reference wording while returning wrong calorie values, or return correct calories while paraphrasing poorly. The paper never reports mean absolute error, percentage error, correlation, or any calorie-level accuracy metric against ground-truth nutritional values. Consequently, both the abstract's claim of 'accurate calorie estimation' and Section 5.3's statement that fine-tuning 'improves the accuracy of the model' are unsupported by the reported data.
  2. [§3, dataset construction] The dataset size statements are internally inconsistent. The abstract and Section 3 state that CalData contains 330K image-text pairs, but Section 3 also reports 5,801 unique samples, an initial pool of 76,767 images, and a design with at most five images and five instruction sets per sample, which implies roughly 145K pairs. The stated split sizes sum to 191,433 + 63,811 + 63,811 = 319,055 pairs, which is neither 330K nor 145K. The sentence 'We split the dataset within each sample (recipe)' is also ambiguous and could imply that images from the same recipe appear in multiple splits, creating a leakage risk. Because CalData provides the reference texts used for all reported metrics, these unresolved numerical and procedural inconsistencies undermine the evaluation.
  3. [§4.2, RAG component] The paper claims that the RAG module grounds calorie estimates in USDA nutritional data and mitigates hallucinations, but no experiment isolates or evaluates this component. There is no retrieval accuracy measure, no end-to-end comparison with and without RAG, and no ablation of the retrieved context's effect on the final output. As a result, the reported aggregate improvement cannot be attributed to RAG, LoRA, or the training data, and the specific contribution of retrieval to calorie accuracy is unverified.
  4. [§5.3, evaluation protocol] The term 'accuracy' is used without definition, and the baseline is described only as 'before fine-tuning' while the intervention bundles dataset curation, LoRA adaptation, and RAG. The aggregate metric uses arbitrary weights λ_rouge and λ_bleu that are never specified, and no statistical significance, confidence intervals, or comparisons to prior calorie-estimation methods are provided. The qualitative results in Figure 3 are illustrative but lack ground truth and error analysis. The evaluation protocol therefore does not support the conclusion that the system is accurate or that the proposed method improves over a meaningful baseline.
minor comments (5)
  1. [Throughout] There are several typos and formatting errors: 'receipt dataset' should be 'recipe dataset', 'recovered generation' should be 'Retrieval-Augmented Generation', and the heading 'Multi-Modal Large Language Models for Food.' is duplicated.
  2. [§5.3, Eq. (1)] The values of λ_rouge and λ_bleu in the aggregate metric are not reported; please specify them or justify why the weighted combination is meaningful.
  3. [§5.2] The input format template uses placeholders like [Task Identifier] and [/INST] without a concrete example; including one full training or inference example would clarify the method.
  4. [References] Some citations for market claims and related results are to non-archival or difficult-to-access sources (e.g., the CalAI revenue claim and the LIMA-based motivation), and should be replaced or supplemented with verifiable references.
  5. [§5.4] The qualitative examples in Figure 3 would be more informative if accompanied by the corresponding ground-truth nutritional values and a discussion of failure cases or error patterns.

Circularity Check

0 steps flagged · score 0.0 of 10

No formal circularity: the paper's central evidence is a supervised fine-tuning evaluation; the evaluation-validity gaps are correctness concerns, not self-referential reductions.

full rationale

The paper contains no derivation chain in which an output quantity is defined in terms of the quantity it purports to predict, and no load-bearing argument is carried by a self-citation. The self-citations ([28]–[30]) are used for data augmentation, sampling, and related work, not to justify the central claim. The central evidence in Table 1 is a fine-tuned-versus-baseline comparison of text-overlap metrics (ROUGE, BLEU, SacreBLEU, BERTScore) on CalData references. That is a standard supervised-evaluation setup, not a circular reduction: the LoRA weights are fitted to training pairs and evaluated on the authors' stated training/validation/test split, so the reported metric values are not equal to the training objective by construction. The concern that ROUGE/BLEU text overlap does not measure calorie-estimation correctness is a correctness/external-validity problem, not a formal circularity: the paper never defines calorie accuracy as the aggregate metric, and no calorie-level ground-truth comparison is reported. Likewise, the ambiguous 'split the dataset within each sample (recipe)' and the inconsistent pair counts (29,005 vs 319,055 vs 330K) are data-construction and leakage risks, not self-referential reductions. For these reasons the circularity score is 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 1 invented entities

The central claim rests on two unvalidated assumptions: that recipe-derived images and text can support calorie estimation, and that text-overlap metrics measure calorie correctness. The aggregate metric weights are an unreported free parameter introduced to produce a headline number. No new entities are added.

free parameters (1)
  • Aggregate metric weights lambda_rouge and lambda_bleu = not reported
    Section 5.3 defines the aggregate metric as a weighted average of ROUGE-L and BLEU, but the weights are never specified. The 8.16% increase in Table 1 depends on these unreported hand-chosen values, so the headline improvement cannot be reproduced or independently checked.
assumptions (3)
  • domain assumption Recipe1M+ food images paired with recipe metadata are sufficient for calorie estimation training.
    CalData is built from Recipe1M+ with added nutrition facts, but the paper never validates that these recipe images and ingredient lists correspond to real plate portions with known calorie content. This enters in Section 3.
  • ad hoc to paper Text-overlap metrics such as ROUGE and BLEU measure calorie estimation accuracy.
    Section 5.3 reports only text-similarity metrics and never compares predicted calories with true calorie values. The assumption that word overlap proxies for nutritional correctness is not stated and is unlikely to hold.
  • domain assumption The USDA nutrition database contains accurate entries for all ingredients and quantities in the retrieved recipes.
    The RAG module in Section 4.2 queries USDA data to produce calorie estimates. If the database lacks matching entries or the ingredient names from the VLM do not align with USDA records, the retrieved nutrition facts will be wrong.
invented entities (1)
  • None
    purpose: Not applicable
    The paper introduces no new physical or conceptual entities, such as particles, forces, or conserved quantities. Its contribution is an empirical system and a dataset.

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Cite this review

Pith. "Pith review of CaLoRAify: Calorie Estimation with Visual-Text Pairing and LoRA-Driven Visual Language Models." pith.science (2026). https://pith.science/paper/KIXJJLU4

@misc{pith2026241209936,
  author       = {Pith},
  title        = {Pith review of: CaLoRAify: Calorie Estimation with Visual-Text Pairing and LoRA-Driven Visual Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KIXJJLU4}},
  note         = {Machine review of arXiv:2412.09936}
}
read the original abstract

The obesity phenomenon, known as the heavy issue, is a leading cause of preventable chronic diseases worldwide. Traditional calorie estimation tools often rely on specific data formats or complex pipelines, limiting their practicality in real-world scenarios. Recently, vision-language models (VLMs) have excelled in understanding real-world contexts and enabling conversational interactions, making them ideal for downstream tasks such as ingredient analysis. However, applying VLMs to calorie estimation requires domain-specific data and alignment strategies. To this end, we curated CalData, a 330K image-text pair dataset tailored for ingredient recognition and calorie estimation, combining a large-scale recipe dataset with detailed nutritional instructions for robust vision-language training. Built upon this dataset, we present CaLoRAify, a novel VLM framework aligning ingredient recognition and calorie estimation via training with visual-text pairs. During inference, users only need a single monocular food image to estimate calories while retaining the flexibility of agent-based conversational interaction. With Low-rank Adaptation (LoRA) and Retrieve-augmented Generation (RAG) techniques, our system enhances the performance of foundational VLMs in the vertical domain of calorie estimation. Our code and data are fully open-sourced at https://github.com/KennyYao2001/16824-CaLORAify.

Figures

Figures reproduced from arXiv: 2412.09936 by the authors.

Figure 1
Figure 1. A user interface example of Caloraify Traditional methods for calorie estimation from food im￾ages have followed a multi-step pipeline involving food classification, portion size estimation, and caloric calcu￾lation [10, 18, 21, 24]. These approaches often rely on reference objects of known size [10] or depth information [24] to estimate portion sizes. While effective under con￾trolled conditions, these methods face… view at source ↗
Figure 2
Figure 2. The workflow is similar to [7], beginning with the pre-trained Vision Transformer (ViT) processing the input dish image to extract tokenized visual representations, which capture key features of the dish. Guided by the [vqa] identifier, the LLaMA-2 module formulates a structured question, such as “What ingredients and quantities are required for this recipe?”, to direct subsequent tasks. This query is sent to the Re… view at source ↗
Figure 3
Figure 3. Qualitative results of the model output 6. Conclusion and Future Work In this paper, we introduced CaLoRAify, a novel frame￾work for accurate calorie estimation and ingredient analy￾sis using vision-language models. By leveraging the capa￾bilities of MiniGPT-v2 and enhancing them with Retrieval￾Augmented Generation (RAG) and Low-Rank Adaptation (LoRA), our approach demonstrated significant improve￾ments in both accu… view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models

    cs.LG 2025-05 conditional novelty 5.0 of 10

    KERL uses a food knowledge graph and three LoRA adapters on one LLM to recommend constrained recipes, generate cooking instructions, and produce micro-nutrition details.

Reference graph

Works this paper leans on

32 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [1]

    https://www.businessinsider.com/ calai-revenue-growth-2023

    Ai-based calorie estimation app calai reports $50 million revenue. https://www.businessinsider.com/ calai-revenue-growth-2023 . Accessed: 2024-10-

    work page 2023

  2. [2]

    https : / / www

    Loseit app reaches new milestone in calorie tracking. https : / / www . loseit . com / news / milestone. Accessed: 2024-10-11. 1

    work page 2024

  3. [3]

    https : / / www

    Myfitnesspal user statistics. https : / / www . myfitnesspal . com / statistics . Accessed: 2024-10-11. 1

    work page 2024

  4. [4]

    https : / / www

    National institutes of health overweight & obesity statistics. https : / / www . niddk . nih . gov / health - information / health - statistics / overweight-obesity. Accessed: 2024-10-11. 1 5

    work page 2024

  5. [5]

    https://fdc.nal.usda

    Usda food calorie database. https://fdc.nal.usda. gov/fdc-app.html. Accessed: 2024-10-10. 4

    work page 2024

  6. [6]

    Self-rag: Learning to retrieve, generate, and critique through self-reflection

    Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Han- naneh Hajishirzi. Self-rag: Learning to retrieve, generate, and critique through self-reflection. ArXiv, abs/2310.11511,

    arXiv

  7. [7]

    Minigpt-v2: large language model as a unified interface for vision-language multi-task learning

    Jun Chen, Deyao Zhu, et al. Minigpt-v2: large language model as a unified interface for vision-language multi-task learning. ArXiv, abs/2310.09478, 2023. 3, 4

    arXiv 2023

  8. [8]

    Gary Chan, and Hongyang Zhang

    Jierun Chen, Fangyun Wei, Jinjing Zhao, Sizhe Song, Bo- huai Wu, Zhuoxuan Peng, S.-H. Gary Chan, and Hongyang Zhang. Revisiting referring expression comprehension eval- uation in the era of large multimodal models. ArXiv, abs/2406.16866, 2024. 3

    arXiv 2024

Show all 32 references
  1. [9]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition ...

    2010 arXiv

  2. [10]

    Image-based estimation of real food size for accurate food calorie estimation

    Takumi Ege, Yoshikazu Ando, Ryosuke Tanno, Wataru Shi- moda, and Keiji Yanai. Image-based estimation of real food size for accurate food calorie estimation. In 2019 IEEE Con- ference on Multimedia Information Processing and Retrieval (MIPR), pages 274–279, 2019. 1, 2

    2019

  3. [11]

    Shan, and Xilin Chen

    Rui Hou, Hong Chang, Bingpeng Ma, S. Shan, and Xilin Chen. Cross attention network for few-shot classification. In Neural Information Processing Systems, 2019. 3

    2019

  4. [12]

    Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, and Lu Wang

    Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, and Lu Wang. Lora: Low- rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 2, 3

    2021 arXiv

  5. [13]

    Hudson and Christopher D

    Drew A. Hudson and Christopher D. Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. 2019 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 6693– 6702, 2019. 3

    2019

  6. [14]

    Dense passage retrieval for open-domain question answering

    Vladimir Karpukhin, Barlas O ˘guz, Sewon Min, Patrick Lewis, Ledell Yu Wu, Sergey Edunov, Danqi Chen, and Wen tau Yih. Dense passage retrieval for open-domain question answering. ArXiv, abs/2004.04906, 2020. 3

    2004 arXiv

  7. [15]

    Clustered- patch element connection for few-shot learning

    Jinxiang Lai, Siqian Yang, Junhong Zhou, et al. Clustered- patch element connection for few-shot learning. In Interna- tional Joint Conference on Artificial Intelligence, 2023. 3

    2023

  8. [16]

    Bart: Denoising sequence-to- sequence pre-training for natural language generation, trans- lation, and comprehension

    Mike Lewis, Yinhan Liu, et al. Bart: Denoising sequence-to- sequence pre-training for natural language generation, trans- lation, and comprehension. In Annual Meeting of the Asso- ciation for Computational Linguistics, 2019. 3

    2019

  9. [17]

    Retrieval-augmented generation for knowledge-intensive nlp tasks

    Patrick Lewis, Ethan Perez, Aleksandara Piktus, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. ArXiv, abs/2005.11401, 2020. 2, 3

    2005 arXiv

  10. [18]

    Computer vision-based food calorie estimation: dataset, method, and experiment, 2017

    Yanchao Liang and Jianhua Li. Computer vision-based food calorie estimation: dataset, method, and experiment, 2017. 1, 2

    2017

  11. [19]

    Visual instruction tuning, 2023

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023. 1

    2023

  12. [20]

    Query rewriting for retrieval-augmented large lan- guage models

    Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. Query rewriting for retrieval-augmented large lan- guage models. ArXiv, abs/2305.14283, 2023. 3

    2023 arXiv

  13. [21]

    Kawashti, Mazen Mohamed, Mohamed Sabry, Hanan Hindy, Mazen Khaled, and Waleed Mohamed

    Bavlly Magid, Mohamed Ibrahim, Yomna A. Kawashti, Mazen Mohamed, Mohamed Sabry, Hanan Hindy, Mazen Khaled, and Waleed Mohamed. Calorieme: An image-based calorie estimator system. In 2023 Eleventh International Conference on Intelligent Computing and Information Sys- tems (ICIC...

    2023

  14. [22]

    Marin, P

    J. Marin, P. Karp, D. Parikh, and A. Farhadi. Recipe1m+: A dataset for learning cross-modal embeddings for cooking recipes and food images. arXiv preprint arXiv:1810.06553,

    arXiv

  15. [23]

    Fnu Mohbat and Mohammed J. Zaki. Llava-chef: A multi- modal generative model for food recipes. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, page 1711–1721. ACM, 2024. 2

    2024

  16. [24]

    Im2calories: Towards an automated mobile vision food di- ary

    Austin Myers, Nick Johnston, Vivek Rathod, Anoop Korat- tikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, and Kevin Murphy. Im2calories: Towards an automated mobile vision food di- ary. In 2015 IEEE International Conference on Compute...

    2015

  17. [25]

    Sentence-bert: Sentence embeddings using siamese bert-networks

    Nils Reimers et al. Sentence-bert: Sentence embeddings using siamese bert-networks. In Conference on Empirical Methods in Natural Language Processing, 2019. 4

    2019

  18. [26]

    A-okvqa: A benchmark for visual question answering using world knowl- edge

    Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. A-okvqa: A benchmark for visual question answering using world knowl- edge. In European Conference on Computer Vision, 2022. 3

    2022

  19. [27]

    Llama 2: Open foundation and fine-tuned chat models

    Hugo Touvron and Louis Martin others. Llama 2: Open foundation and fine-tuned chat models. ArXiv, abs/2307.09288, 2023. 3

    2023 arXiv

  20. [28]

    Transferring foundation models for generalizable robotic manipulation, 2024

    Jiange Yang, Wenhui Tan, Chuhao Jin, Keling Yao, Bei Liu, Jianlong Fu, Ruihua Song, Gangshan Wu, and Limin Wang. Transferring foundation models for generalizable robotic manipulation, 2024. 2

    2024

  21. [29]

    Dual-level interaction for do- main adaptive semantic segmentation

    Dongyu Yao and Boheng Li. Dual-level interaction for do- main adaptive semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pages 4527–4536, 2023. 3

    2023

  22. [30]

    Harris, and Marcel Carlsson

    Dongyu Yao, Jianshu Zhang, Ian G. Harris, and Marcel Carlsson. Fuzzllm: A novel and universal fuzzing frame- work for proactively discovering jailbreak vulnerabilities in large language models. In ICASSP 2024 - 2024 IEEE Inter- national Conference on Acoustics, Speech and Sign...

    2024

  23. [31]

    Lima: Less is more for alignment, 2023

    Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. Lima: Less is more for alignment, 2023. 3

    2023

  24. [32]

    Minigpt-4: Enhancing vision- language understanding with advanced large language mod- els

    Deyao Zhu, Jun Chen, Xiaoqian Li, Guangsheng Zhang, Jianfei Zhao, Yixiao Wei, Yuhao Wu, Lijun Zhang, Yan- wei Fu, Yuanliang Cao, et al. Minigpt-4: Enhancing vision- language understanding with advanced large language mod- els. arXiv preprint arXiv:2304.10592, 2023. 1, 5 6

    2023 arXiv

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