Navigating Weight Prediction with Diet Diary

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-04 07:52:31

Abstract: Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series forecasting framework. Specifically,
we introduce a Unified Meal Representation Learning (UMRL) module to extract representations for each meal. Additionally, we design a diet-aware loss function to associate food intake with weight variations. By conducting experiments on the DietDiary dataset with two state-of-the-art time series forecasting models, NLinear and iTransformer, we demonstrate that our proposed framework achieves superior performance compared to the original models. We will make our dataset, code, and models publicly available.

Main paper: 
 introduction: 
 Food plays a vital role in human existence, affecting the quality of life and various physiological indicators, with weight being one of the most fundamental and critical aspects. [24] indicates that the relative balance between energy expenditure and dietary intake determines weight gain or loss. Notably, the energy expenditure in non-exercise activities (such as sitting) usually accounts for a significantly larger portion of total energy expenditure than exercise [21] . Motivated by these insights, we aim to investigate the impact of food intake on weight prediction.Numerous studies have focused on various tasks within the food domain, such as food classification [11, 25, 35, 37] , ingredients recognition [1, 5, 6] , recipe retrieval [19, 46, 48, 53, 54, 63, 64] , food volume prediction [32, 34, 49] as well as calorie and nutritional information estimation [36, 50, 52] . Nevertheless, these existing methods typically analyze each food image independently and do Figure 1 : The overview of the proposed weight prediction with diet diary task. Given a historical food intake and corresponding weight measurement in the past 𝐿 days, the task aims to predict the weights in the following 𝑇 days.not investigate the impact of food intake on physiological indicators (e.g., weight) over a period of time.To address this gap, this paper navigates weight prediction with dietary diary. On the one hand, we construct a novel dataset named DietDiary, which includes daily dietary records and corresponding weight measurements for 611 participants from a health management system. Different from existing datasets in the food domain such as [2, 5, 38] , DietDiary is the first dataset to provide both food intake and corresponding weight measurements over a period of time. On the other hand, as illustrated in Figure 1 , we define a new task that aims to leverage historical weight and food intake data to predict future weights. This task poses two major challenges:(1) Meal representation learning. Given that food intake can be represented in various forms, such as textual ingredient labels or food images, developing an effective method for learning meal representations for weight prediction is crucial. (2) Modeling the correlation between food intake and weight changes. Since food intake is closely related to weight, weight prediction requires more than just exploring the temporal relationships between historical and future weights. Understanding the complex correlation and dependencies between dietary intake and weight fluctuations presents significant challenges to this task.To address the aforementioned challenges, we propose a novel framework that integrates food intake information for weight prediction. Specifically, we introduce a Unified Meal Representation Learning (UMRL) module that leverages CLIP [43] text or image encoders to extract a unified feature representation of a meal from various forms of historical food intake. Additionally, we propose a diet-aware loss function to enable the model to capture the correlation and dependencies between food and weight changes. Importantly, our framework is designed to be compatible with any existing time series forecasting model for weight prediction that incorporates food intake information. We evaluate our framework on the DietDiary dataset using two representative advanced time series forecasting models, NLinear [59] and iTransformer [31] . The superior performance of our framework over these models demonstrates its effectiveness in leveraging food intake information for weight prediction.The contributions of this paper can be summarized as follows:• We construct a new DietDiary dataset, which is the first dataset providing food intake and corresponding weight measurements over a period of time. • We introduce a novel task of weight prediction using dietary information. This task uniquely treats food intake as temporal data and investigates its potential to improve weight prediction.• We propose a model-agnostic time series forecasting framework for weight prediction with food intake. Our framework demonstrates significant improvements over existing methods in weight prediction performance. 
related work 2.1 time series forecasting: 
 Time series forecasting is a fundamental task in various fields, including finance, traffic and meteorology. Over the years, to address the inherent challenges of predicting future values based on past observations, numerous models are proposed, such as the famous ARIMA [3] . With the rise of deep learning, neural networkbased approaches have gained popularity in time series forecasting. Models such as Long Short-Term Memory (LSTM) networks [22] , DeepAR [44] and Prophet [51] are used to capture long-term dependencies and nonlinear relationships in the data. As Transformer [31] demonstrates powerful sequence modeling capabilities in natural language processing [14] , computer vision [4, 15] and other domains, models based on modifying the vanilla Transformer, especially the attention mechanism, tailored for time series forecasting tasks have been widely researched, such as Informer [60] , Autoformer [56] , Pyraformer [30] , and FEDformer [61] .While various research efforts are ongoing to modify attention structures to achieve better Transformer-based solutions, [59] questions the effectiveness of the Transformers and proposes a set of simple and efficient linear models including Linear, NLinear, and DLinear, which have led more researchers to focus on linear-based model. TiDE [13] designs a Multi-layer Perceptron (MLP) based encoder-decoder model to capture covariates and non-linear dependencies. TSMixer [16] patches the time series data and enhances the learning capability of simple MLP structures. Chen et al. [10] propose a Time-mixing MLP to model temporal dependency and a Feature-mixing MLP to analyze the cross-variate information. Influenced by the outstanding performance of linear models, several works continue to employ transformer-based architectures but seek performance enhancement from alternative perspectives. PatchTST [41] uses patched data as input and adopts a channelindependence design that makes a token only contains information from one channel, instead of changing the structure of the transformer. iTransformer [31] inverts the duties of the attention mechanism and the feed-forward layer to capture temporal information to achieve promoted performance and generalization.In contrast, we propose a model-agnostic time series forecasting framework to predict weight with food intake. To validate the effectiveness of our framework, state-of-the-art linear-based model NLinear [59] and transformer-based model iTransformer [31] are chosen as our time series forecasting models respectively.With the development of computer vision and the emergence of various food datasets, research methods and tasks in the food domain have gradually become more diverse. Some traditional visual tasks have been extended to the food domain, such as food classification [11, 25, 27, 35, 37] , ingredients recognition [1, 5, 6, 29] , and food segmentation [17, 23, 28, 57] . With the release of Recipe1M [47] , cross-modal food-related tasks have been extensively studied, such as the retrieval of finding the most relevant recipe for a food image or vice versa [8, 9, 19, 48, 63] . Some works focus on generating recipes from images [12, 20, 45] or corresponding images from recipes [42, 62] . These tasks all involve analyzing each image independently, whereas in this paper, we model temporal dietary data, which has not been explored in previous studies.Another research branch involves predicting the nutritional components and calorie of food from food images, which aids in monitoring intake patterns. Some works estimate volume first from voxel [36] , point cloud [18, 33] or 3D mesh [40] , then mapping is achieved through data on the calories contained per unit volume. With the release of Nutrition5K [52] , a dataset providing fine-grained nutritional attributes, food quality and food videos, some works predict nutrition directly from images by neural network [50, 52, 58] . However, these works only predict nutritional information without further linking food intake to weight. To the best of our knowledge, this paper is the first work to utilize dietary diary to predict weight.  
dataset construction: 
 We introduce a novel dataset, DietDiary, specifically for analyzing weight in relation to food intake. In contrast to datasets such as Food-101 [2] , VIREO Food-172 [5, 7] , Food2K [39] and Recipe1M [47] , DietDiary encompasses diet diary of three meals over a period of time, accompanied by daily weight measurement. To the best of our knowledge, DietDiary is the first dataset in the food domain to provide this kind of data, offering new opportunities for research in dietary pattern analysis and its impact on weight management. The DietDiary dataset was collected within a health management system 1 wherein participants were required to meticulously log their daily dietary intake, along with their corresponding weight measurements. The dietary log consists of images of three meals (i.e., breakfast, lunch, and supper) along with manually labeled ingredients for each meal. The dataset encompasses a total of 611 1 https://www.qiezilife.com/ participants, with over 5k daily records, nearly 30k images, and over 15k ingredient annotations.As depicted in Figure 2 , the duration of record-keeping among participants varies, ranging from a minimum of one week to over one month. A significant majority (91.6%) of the participants maintained their records for a duration of one to two weeks. However, there were also 9 participants recorded their diet and weight for more than a month. Figure 4 illustrates the distribution of daily weight fluctuations across all participants. Most users experience weight changes within ±1 kg per day, with weight loss records comprising the majority due to the data source. Additionally, a small portion of individuals experience changes exceeding 2 kg within a day. The annotation of ingredients in the dataset was standardized through a series of preprocessing steps. First, we unified the separators used between phrases, as different participants employed varying symbols (e.g., "-", " / ", " () ") to delineate ingredients. Second, the original annotations in Chinese were translated into English. Third, we adopted the approach as in inverse cooking [45] to aggregate and categorize ingredients based on common prefixes or suffixes, for example, "boiled eggs", "steamed eggs", and "egg slices" are all merged into the "egg" category. Fourth, ingredients that appeared less than five times were excluded from the dataset. After preprocessing, the total number of unique ingredients was 197. The ten most frequently occurring ingredients are depicted in Figure 5 , such as "milk", "egg" and "rice", all of which are common in everyday meals. Furthermore, Figure 6 provides a comprehensive example from the dataset, showcasing food images, annotated ingredients, and corresponding weight measurements for two participants with different weight fluctuation trends. On the one hand, user A maintained a healthy dietary habit, and the weight measurements consistently show a decreasing trend. On the other hand, user B consumed high-calorie foods (such as fried shrimp and sugar) on the third and fifth days, and also consumed a large amount of staple food (dumplings and sushi) during supper, resulting in an increase in corresponding weight records.  
method: 
 In this section, we provide detailed definition of the task in Section 4.1 and elaborate on the details of our proposed framework in Sections 4.2 and 4.3.Given the historical weight sequence W = {𝑤 𝑡 } 𝐿 𝑡 =1 and corresponding food intake F = {𝐵𝑟𝑒𝑎𝑘 𝑓 𝑎𝑠𝑡 𝑡 , 𝐿𝑢𝑛𝑐ℎ 𝑡 , 𝑆𝑢𝑝𝑝𝑒𝑟 𝑡 } 𝐿 𝑡 =1 for the past 𝐿 days, the goal of our proposed task is to predict the future weight sequence Ŵ = {𝑤 𝑡 } 𝐿+𝑇 𝑡 =𝐿+1 over the next 𝑇 days, where 𝑤 𝑡 denotes the weight value of the t-th day, 𝐵𝑟𝑒𝑎𝑘 𝑓 𝑎𝑠𝑡 𝑡 , 𝐿𝑢𝑛𝑐ℎ 𝑡 and 𝑆𝑢𝑝𝑝𝑒𝑟 𝑡 represent sets of food images or ingredient labels for each meal on the t-th day. Note that the historical observation takes various data modalities, including textual ingredient labels or food images, and numerical weight. Unified Meal Representation Learning (UMRL) is a module to extract representation for each meal. Given the historical food intake (e.g., food images or ingredient labels), UMRL is designed to obtain representation for breakfast, lunch, and supper for each day respectively, denoted as 𝑓 = {𝑏 𝑡 , 𝑙 𝑡 , 𝑠 𝑡 } 𝐿 𝑡 =1 , where 𝑏 𝑡 , 𝑙 𝑡 and 𝑠 𝑡 are representation for breakfast, lunch, and supper on the t-th day. More details of UMRL are presented in Section 4.3.Model-agnostic weight prediction. After obtaining the representation of historical food intake from UMRL, we concatenate it with the historical weight W, denoted as X = {𝑏 𝑡 , 𝑙 𝑡 , 𝑠 𝑡 , 𝑤 𝑡 } 𝐿 𝑡 =1 , and feed into a time series forecasting model 𝑀. The weight prediction result is obtained as follows:EQUATIONwhere b𝑡 , l𝑡 , ŝ𝑡 , ŵ𝑡 are the predicted weights from breakfast, lunch, supper and weight respectively. Note that our proposed framework is agnostic to the time series forecasting model, which gives us the flexibility to utilize existing state-of-the-art models for our task. In this paper, we choose linear-based NLinear [59] and Transformerbased iTransformer [31] as 𝑀 in Equation 1.Diet-aware loss computation. We introduce a diet-aware loss to model the food intake and weight variations. Assume the weight variation Δ 𝑡 of the t-th day is calculated by the following formula:Δ 𝑡 = 𝑤 𝑡 -𝑤 𝑡 -1 .(EQUATIONAs each meal contributes to the weight variations, the diet loss is computed by combining all three meals as follows:EQUATIONIn addition, we compute a weight loss based on the weight prediction from historical weight as follows:EQUATION)The overall diet-aware loss to train the proposed framework is to combine both diet and weight losses: where 𝜆 is a hyper-parameter to balance the two losses.EQUATIONAs shown in Figure 8 , we propose Unified Meal Representation Learning (UMRL) Module to extract representation from historical food intake for each meal. As the food intake could be either food images or ingredient labels, we first employ CLIP [43] to encode both visual or textual inputs to unify the feature extraction. On the one hand, if the historical food intake is ingredient label, we adopt pretrained CLIP text encoder to extract the features. On the other hand, we adopt CLIP image encoder to extract the features from historical food images. The CLIP model is kept frozen during our training. Note that the users prefer taking pictures of their meals rather than typing the concrete ingredient annotations in practice.As a result, we also investigate employing a pre-trained ingredient recognition model to automatically obtain ingredient labels from images, which is illustrated in the bottom left of Figure 8 .The CLIP features of images or ingredient labels are subsequently averaged to get a feature vector for each meal. Finally, we feed this vector into a Multilayer Perceptron (MLP) to derive the final meal representation. Take breakfast food images as an example, denoted 𝐵𝑟𝑒𝑎𝑘 𝑓 𝑎𝑠𝑡 𝑡 = {𝑖𝑚𝑔 1 , ..., 𝑖𝑚𝑔 𝑛 } 𝑁 𝑛=1 as a set of breakfast food images on the t-th day, where 𝑁 is the number of images, the process of UMRL can be formalized as follows: EQUATIONwhere 𝐸 𝑛 is the CLIP image feature for 𝑖𝑚𝑔 𝑛 , Ē indicates the averaged CLIP image features and 𝑏 𝑡 is the breakfast representation. 
experiment 5.1 experiment settings: 
 Dataset. We conduct experiments on our DietDiary dataset. Inspired by time series forcasting task [31, 59] , we extensively explore different combinations of using weight and food intake history in 𝐿 days to predict the weights in future 𝑇 days, denoted as setting L-T. The settings examined include {3-3, 3-5, 3-7, 5-5, 5-7, 7-3 and 7-7}. For instance, in the 7-3 setting (𝐿 = 7, 𝑇 = 3), we utilize the historical weight sequence and corresponding food intake from the past 7 days to predict the weight for the subsequent 3 days during training. The dataset is partitioned into training, validation, and test sets in a 7:1:2 ratio for each setting. It is crucial to ensure that each participant appears exclusively in one of these splits, thereby preventing any overlap of participants across training, validation, and testing sets. The settings determine the minimum number of days required for training. To maximize the utility of the dataset, we employ different data partitions for different settings. For example, the settings 5-5 and 7-3 necessitate a minimum of 10 days of dietary intake records per participant, whereas the 7-7 setting Evaluation metrics. We employ the Mean Squared Error (MSE) and Mean Absolute Error (MAE) as metrics for performance evaluation. Given that users are primarily concerned with long-term trends in weight prediction, we evaluate performance through autoregressive weight prediction. Specifically, during testing, given a history of 𝐿 days, we initially forecast the weights for the subsequent 𝑇 days. Subsequent predictions are then made autoregressively, using the outcomes of previous forecasts as input, until predictions have been generated for all recorded days of users in the test set.Implementation details. Given the remarkable performance of FoodLMM [58] , a Large Multi-modal Model tailored in the food domain, we choose FoodLMM as our ingredient prediction model. For convenience, we use "users" to refer to ingredients annotated by users and "LMM" to refer to ingredients predicted by the FoodLMM. We evaluate our framework on both the NLinear [59] model, representing linear time series forcasting models, and the iTransformer [31] model, representing transformer-based solutions. For iTransformer, the dimension of series representation 𝐷 is set to 16 when 𝐿 ∈ {3, 5}, 32 when 𝐿 ∈ {5, 7}. The dimension of feed-forward layer is twice that of 𝐷. The number of inverted Transformer blocks and attention heads are set to 1 and 2 respectively. Both NLinear and iTransformer are trained using Adam optimizer [26] with batch size of 32 until early-stopping criteria is met (validation loss does not decrease after 7 epochs). The learning rate is initially set to 0.005 and decays with each epoch. The 𝜆 in equation 5 is set to {0, 0.1, 0.25, 0.5, 0.75, 1} to explore the influence of the proportion of diet loss, which will be shown in Section 5.4.Tables 2 and 3 present the performance of our proposed method, which leverages state-of-the-art time series forecasting models NLinear [59] and iTransformer [31] , respectively. Compared to models that do not incorporate food intake information, our method consistently achieves superior performance over NLinear [59] and iTransformer [31] across all evaluated settings, with a significant margin of improvement. Notably, the improvements are observed consistently with three different types of food intake inputs: food images, user-provided ingredient labels, and ingredient labels predicted by FoodLMM. These results clearly demonstrate the effectiveness of incorporating food intake information in enhancing the accuracy of weight prediction.An inspiring observation is that the ingredient labels predicted by FoodLMM often perform comparably or even surpass manually provided ingredient labels by users in most settings. This finding is noteworthy considering that acquiring food images is generally easier than manually annotating ingredients for each meal. Figure 9 presents four examples comparing ingredient labels between users and FoodLMM. Typically, users are more prone to omitting ingredients rather than mislabeling them. For instance, "noodles", "Chinese parsleycoriander ", and "green onion" in Figure 9 (a), and "corn" and "Shanghai cabbage" in Figure 9 (b) are missing from the user-provided labels. Remarkably, FoodLMM successfully identifies these omitted ingredients. However, ingredient recognition remains a challenging task [7] , and the predictions from FoodLMM are still not perfect. For example, there are three incorrect ingredients predicted by FoodLMM in Figure 9 (b) and (c). In Figure 9 (d), although FoodLMM fails to accurately predict "tomato", the remaining ingredients are correctly identified. We further investigate multi-modal fusions of food intake from both images and ingredient labels by averaging the UMRs of the same meal across different modalities. Tables 4 and 5 present the fusion results of images with ingredient annotations from users and FoodLMM respectively. Except for the MSE of setting 5-5, the fusion results consistently outperform those of single modality, whether the ingredient labels are manually provided or predicted by FoodLMM. This demonstrates that food information from different modalities can complement each other, which leads to incorporating both of them achieving better performance. Additionally, the effectiveness of multi-modal fusion also validates that our proposed UMRL module can embed food images and ingredient annotations into a common space and learn unified representations of meals from different modalities.Impact of number of meals. Table 6 presents the impact of incorporating different numbers of daily meals on weight prediction performance. Generally, the inclusion of food intake information, regardless of the number of meals, manages to improve performance. When only one meal is considered, the inclusion of lunch data yields the most significant improvement under most settings. This is attributed to lunch meals providing the most diverse and distinct food information within the dataset. Breakfast and supper, often featuring less distinctive items like congee-like foods (e.g., meal replacement powders), do not contribute as significantly as lunch to the encoder's effectiveness. For example, the images in the last row of both breakfast and supper in Figure 3 . However, the combination of all three meals offers complementary benefits, leading to the best overall results for weight prediction. As illustrated in Figure 11 , we qualitatively compare the weight prediction visualization among ground-truth weight (blue), NLinear model (green), and our framework based on NLinear (orange) using images as dietary information for different users. It is evident that both the trend and the exact predicted values of our framework are closer to the ground truth than those of the NLinear model. For instance, in subplot (b), the ground truth weight trend shows an initial decrease, followed by a slight increase, and then another decrease. The orange line, representing our prediction results, exhibits the same trend and is numerically closer to the ground truth than the green line of the NLinear model. Similar trends can be observed in subplots (a), (d), (f), and (g) as well. However, in subplots (c) and (i), even though our prediction results are superior to the NLinear model, our framework's predictions do not align well with the ground truth trend. This discrepancy can be attributed to variable changes in weight and the accumulation of prediction errors in long-term predictions. This issue remains an unresolved challenge in time series forecasting tasks [55, 59] .  
conclusion: 
 We have investigated weight prediction using diet diary. A new dataset named DietDiary is constructed which comprises dietary intake and corresponding daily weight measurements over a period. We introduce a new task of predicting weights by leveraging historical food intake. To address this task, we propose a modelagnostic time series forecasting framework that achieves significant improvements in weight prediction. Our experiments highlight the effectiveness of the proposed UMRL module and diet-aware loss in learning unified intake representation and establishing a correlation between food intake and weight change. Furthermore, we examine three forms of food intake for weight prediction, including images, ingredient annotations provided by users, and ingredient labels predicted from a pre-trained ingredient prediction model. Experimental results on two representative time series prediction models, NLinear [59] and iTransformer [31] , demonstrate that incorporating food intake leads to improved accuracy in weight prediction across all three forms. We also investigate multi-modal fusions of dietary information from different modalities (image and text) and achieve better performance, which demonstrates that multi-modal food information can complement each other and provide more effective food information. Through ablation studies, we further demonstrate the necessity of using all three meals for accurate weight prediction. While encouraging, this paper primarily focuses on weight prediction from food intake, exploring the impact of food to other physiological indicator such as blood glucose, will be our future work.  
 
 Appendix:  


Decision: All reviewers vote to accept the paper. The AC agrees with the reviewers and recommend acceptance.

Reviews: review: paper_summary: paper_summary: This paper addresses the gap in research exploring the impact of food intake on physiological indicators, such as weight, over time. It introduces the DietDiary dataset, which contains daily dietary diaries and corresponding weight measurements of 611 participants. The primary contribution of this research is the proposal of a novel task for weight prediction using dietary diaries. The task leverages historical food intake and weight data to predict future weights through a model-agnostic time series forecasting framework.

The proposed framework features a Unified Meal Representation Learning (UMRL) module to extract representations for each meal and a diet-aware loss function to associate food intake with weight variations. The framework is designed to be compatible with existing time series forecasting models. The performance of the framework is evaluated using two state-of-the-art time series forecasting models, NLinear and iTransformer, demonstrating superior performance compared to the original models.
strength_weakness: strength_weakness: This paper introduced a new task into food-related multimedia computing. Of course, human weight change does not depend on only dietary. It must depend on other enegry-comsumption activities. However, as the first step of this new task, this work is interesting, and the proposed dataset must be helpful for the community.

Since UMRL employs CLIP, both images and ingredient texts can be accepted as inputs. It is an interesting idea.

Basically the paper is written well and easy to read.
limitations: limitations: It is hard to evaluate if the estimate results are meaningfull or not. The statics of the dataset such as the average weight change over 3,5,7 days should be written. In this sense, adding MAPE (Mean Absolute Percentage Error) is helpful for the readers.
paper_summary: paper_summary: The paper "Navigating Weight Prediction with Diet Diary" embarks on a novel exploration into the predictive relationship between dietary habits and weight changes over time. It introduces the DietDiary dataset, which uniquely captures daily dietary logs and weight measurements, presenting a fresh avenue for empirical analysis in this domain. The central contribution is the innovative task of predicting future weight based on historical dietary data and the development of a model-agnostic time series forecasting framework. This framework is distinguished by its Unified Meal Representation Learning (UMRL) module, which adeptly extracts meal representations from diverse dietary inputs, and a diet-aware loss function that meticulously links dietary intake with weight fluctuations. The efficacy of this approach is validated through rigorous experiments, demonstrating its superiority over existing models by utilizing two advanced forecasting models, the NLinear and iTransformer.
strength_weakness: strength_weakness: The findings underscore the critical influence of integrating meal-specific data into weight prediction models, highlighting the improvement in predictive accuracy when dietary information is considered. The introduction of a multi-modal fusion strategy further refines the prediction capability, suggesting that combining different types of dietary data (images and textual ingredient labels) can significantly enhance the model's performance. The paper's methodical approach, from dataset creation through model innovation to detailed experimental validation, sets a high standard for future research in the intersection of diet tracking and weight prediction. This work not only advances the field of dietary data analysis but also opens up new possibilities for personalized health management and intervention strategies based on predictive modeling of weight changes.
limitations: limitations: The data looks sensitive. The author may declare how user privacy has been taken care of.

The data diversity should consider to be increased. It looks the image semantics are very similar related to time, which may cause a easy task for ML to solve.

More baseline models could be given and let us see how this dataset performs.
paper_summary: paper_summary: The paper introduces the DietDiary dataset, which comprises daily dietary diaries and corresponding weight measurements of real users. It introduces a novel task of weight prediction utilizing a dietary diary, aiming to leverage historical food intake and weight to predict future weights. To address this task, the paper proposes a model-agnostic time series forecasting framework. Specifically, it introduces a Unified Meal Representation Learning (UMRL) module to extract representations for each meal and designs a diet-aware loss function to associate food intake with weight variations. Through experiments on the DietDiary dataset using two state-of-the-art time series forecasting models, NLinear and iTransformer, the paper demonstrates that the proposed framework outperforms the original models.
strength_weakness: strength_weakness: 1. Novelty and Theoretical Approach: The paper onstruct a new DietDiary dataset, which is the first dataset providing food intake and corresponding weight measurements over a period of time. The paper introduce a novel task of weight prediction using dietary information. This task uniquely treats food intake as temporal data and investigates its potential to improve weight prediction. The paper propose a model-agnostic time series forecasting framework for weight prediction with food intake. Our framework demonstrates significant improvements over existing methods in weight prediction performance. 
2. Technical Correctness and Adequate Evaluation: The authors rigorously validate their framework by comparing it against established time series forecasting models, NLinear and iTransformer. This empirical assessment provides solid evidence of its improved performance, demonstrating technical soundness and practical relevance.
3. Clarity and Applications: The manuscript is well-structured and clearly articulates the problem, methodology, and findings. The DietDiary dataset creation adds significant value, facilitating further research in this domain.
limitations: limitations: 1. Lack of Depth in Certain Aspects: The motivation of the paper is not clear, and the value and purpose of navigates weight prediction with dietary diary is not reflected.
2. As shown in Figure 11, although the predicted weight of the proposed method is better than NLinear, there is still a large gap between the predicted weight and the actual weight change. Do the tasks and methods presented in the paper make sense?
3. Why does the ablation experiment not include relevant experimental studies on the effectiveness of Unified Meal Representation Learning (UMRL)?
paper_summary: paper_summary: This paper presents a simple but novel approach to weight prediction using dietary diaries. The authors introduce the DietDiary dataset, which contains daily dietary records and corresponding weight measurements from real users. They propose a model-agnostic time series forecasting framework incorporating a Unified Meal Representation Learning (UMRL) module and a diet-aware loss function to leverage historical food intake for predicting future weights. The framework is evaluated using two advanced time series forecasting models, NLinear and iTransformer, demonstrating superior performance over baseline models.
strength_weakness: strength_weakness: 1. The introduction of the DietDiary dataset and the task of weight prediction using dietary information are both interesting. This fills a gap in food analysis research, which has traditionally focused on tasks like food recognition and nutrition estimation from images.
2. The proposed UMRL module and diet-aware loss function are innovative components that effectively integrate food intake information into the weight prediction task. This approach is both model-agnostic and adaptable, making it a versatile solution for various time series forecasting models.
3. The comprehensive experimental setup and the use of state-of-the-art models (NLinear and iTransformer) for validation demonstrate the effectiveness of the proposed framework. The results show significant improvements in weight prediction accuracy when incorporating dietary information.
4. This research has practical implications for health management and dietary monitoring, potentially aiding individuals in better understanding the impact of their dietary habits on their weight over time.
limitations: limitations: 1. While the DietDiary dataset is novel, it is limited in size (611 participants) and duration (mostly 1-2 weeks). This may affect the generalizability of the results to larger and more diverse populations over longer periods. In addition, it is recommended to put the specific data size into abstract.
2. The evaluation primarily focuses on the accuracy of weight prediction. However, the paper could benefit from additional analysis, such as the impact of different types of meals (e.g., high-calorie vs. low-calorie) on weight prediction accuracy.
3. The paper is generally well-written and organized, but there are some areas where clarity could be improved, particularly in explaining more technical details of the UMRL module.
4. The literature review is comprehensive, but some key references on recent advancements in food analysis could be added to provide a more complete context. For example, Min, Weiqing, et al. "A survey on food computing." ACM Computing Surveys (CSUR) 52.5 (2019): 1-36; Zhou, Pengfei, et al. "Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection." IEEE Transactions on Image Processing (2024).
5. The references [37]-[39] are repeated, please double-check the presentation details in this paper.