Cross-modal recipe retrieval is vital for transforming visual food cues into actionable cooking guidance, making culinary creativity more accessible. Existing methods separately encode the recipe Title, Ingredient, and Instruction using different text encoders, then aggregate them to obtain recipe feature, and finally match it with encoded image feature in a joint embedding space. These methods perform well but require significant computational cost. In addition, they only consider matching the entire recipe and the image but ignore the fine-grained correspondence between recipe components and the image, resulting in insufficient cross-modal interaction. To this end, we propose U nified T ext E ncoder with F ine-grained C ontrastive L earning (UTE-FCL) to achieve a simple but efficient model. Specifically, in each recipe, UTE-FCL first concatenates each of the Ingredient and Instruction texts composed of multiple sentences as a single text. Then, it connects these two concatenated texts with the original single-phrase Title to obtain the concatenated recipe. Finally, it encodes these three concatenated texts and the original Title by a Transformer-based Unified Text Encoder (UTE). This proposed structure greatly reduces the memory usage and improves the feature encoding efficiency. Further, we propose fine-grained contrastive learning objectives to capture the correspondence between recipe components and the image at Title, Ingredient, and Instruction levels by measuring the mutual information. Extensive experiments demonstrate the effectiveness of UTE-FCL compared to existing methods.

Abstract Sound symbolism is a well-researched topic of psycholinguistics, which tries to comprehend the connection between the sound of a word and its meanings. The Bouba-Kiki effect , one form of sound symbolism, claims that people perceive the pronunciation of “Kiki” as pointier than that of “Bouba.” There is no research that focuses on modeling such perception, i.e., how pointy a pronunciation sounds to humans, through computational and data-driven approaches. To address this, this paper first proposes the novel concept of “phonetic pointiness” defined as how pointy a shape humans are most likely to associate with a given pronunciation. We then model this phonetic pointiness from computational and data-driven approaches to calculate a score for an arbitrary pronunciation. There are three proposed models: a referential model, an expressive model, and a combined model, which integrates the previous two. The idea comes from an existing psycholinguistic classification of two types of sound symbolisms: referential symbolism and expressive symbolism , where the former relates to vocabulary knowledge, while the latter is based on pure human intuition. The proposed models are constructed only with image and language data available on the Web, therefore not requiring task-specific human annotations. We evaluate these models through a crowd-sourced user study, finding a promising correlation between human perception and the phonetic pointiness calculated by the proposed models. The results indicate that human perception can be modeled better by combining both types of sound symbolisms. Furthermore, by observing the behaviors of the models, we show several possible use-cases, such as product naming and psycholinguistic research, which can be a useful insight to further studies and applications.

Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation.

Imageability is a concept from Psycholinguistics quantizing the human perception of words. However, existing datasets are created through subjective experiments and are thus very small. Therefore, methods to automatically estimate the imageability can be helpful. For an accurate automatic imageability estimation, we extend the idea of a psychological hypothesis called Dual-Coding Theory, that discusses the connection of our perception towards visual information and language information, and also focus on the relationship between the pronunciation of a word and its imageability. In this research, we propose a method to estimate imageability of words using both visual and language features extracted from corresponding data. For the estimation, we use visual features extracted from low- and high-level image features, and language features extracted from textual features and phonetic features of words. Evaluations show that our proposed method can estimate imageability more accurately than comparative methods, implying the contribution of each feature to the imageability.

The number of food photos posted to the Web has been increasing. Most of the users prefer to post delicious-looking food photos. They, however, do not always look delicious. A previous work proposed a method for estimating the attractiveness of food photos, that is, the degree of how much a food photo looks delicious, as an assistive technology for taking a delicious-looking food photo. This method extracted image features from the entire food photo to evaluate the impression. In our work, we conduct a preference experiment where subjects are asked to compare a pair of food photos and measure their gaze. The proposed method extracts image features from local regions selected based on the gaze information and estimates the attractiveness of a food photo by learning regression parameters. Experimental results showed the effectiveness of extracting image features from outside the gaze regions rather than inside them.

Since news videos are valuable sources of multimedia information on real-world events, there is a demand for viewing them efficiently. However, there is a problem that summarization methods based on auditory contents do not take into account the visual contents. In the case of news videos, due to its presentation style where audio contents and visual contents do not necessarily come from the same source, this could severely decrease the amount of informative visual contents included in the generated summarized video. Thus, we propose a method for summarizing a sequence of news videos considering the consistency of both auditory and visual contents. The proposed method first selects key-sentences from the auditory contents (Closed Caption) of each news story in the sequence, and then selects a shot within the news story whose "Visual Concepts" detected from the visual contents are the most consistent with the key-phrase. Finally, the audio segment corresponding to each key-phrase is overlapped onto the selected shot, and then concatenated to generate a summarized video. The effectiveness of the proposed method was confirmed on several news topics through a subjective experiment.

We are developing a cooking support system that coaches beginners. In this work, we focus on eye movement patterns while cooking meals because gaze dynamics include important information for understanding human behavior. The system first needs to classify typical cooking operations. In this paper, we propose a gaze-based classification method and evaluate whether or not the eye movement patterns have a potential to classify the cooking operations. We improve the conventional N-gram model of eye movement patterns, which was designed to be applied for recognition of office work. Conventionally, only relative movement from the previous frame was used as a feature. However, since in cooking, users pay attention to cooking ingredients and equipments, we consider fixation as a component of the N-gram. We also consider eye blinks, which is related to the cognitive state. Compared to the conventional method, instead of focusing on statistical features, we consider the ordinal relations of fixation, blink, and the relative movement. The proposed method estimates the likelihood of the cooking operations by Support Vector Regression (SVR) using frequency histograms of N-grams as explanatory variables.

As one tool for structuring a massive volume of archived news videos based on their semantic contents, this paper proposes a method to detect scene duplicates from news videos. A scene duplicate is a pair of video segments taken at the same event from different viewpoints. Referring to the audio channel is effective to detect scene duplicates regardless of viewpoints, but it cannot be relied on when external audio sources (e.g. Narrations, sound effects) overlap the original one. In contrast, the image channel can be useful in most cases, although significant difference in viewpoints affect the detection. The proposed method integrates the information from these two channels in order to improve the accuracy of scene duplicate detection from news videos. The performance of the proposed method was evaluated through an experiment with actual broadcast news videos. As a result, we obtained the higher detection accuracies in both recall and precision. Therefore, we confirmed the effectiveness of the proposed method.

This paper proposes a camera-based visibility estimation method for a traffic sign. The visibility here indicates how a visual target is easy to be detected and recognized by a human driver (not a machine). This research aims at realizing a nuisance-free driver assistance system which sorts out information depending on the visibility of a visual target, in order to prevent driver distraction. Our previous study on estimating the visibility of a traffic sign considered only the effect of the local region around a target, assuming the situation that a driver's gaze is around it. The proposed method integrates both the local features and global features in a driving environment without such an assumption. The global features evaluate the positional relationships between traffic signs and the appearance around the fixation point of a driver's gaze, which considers the effect of the driver's entire field of view. Experimental results showed the effectiveness of incorporating the global features for estimating the visibility of a traffic sign.

This paper presents a Twitter-based event detection method based on "Twitter Enthusiasm Degrees (TED)" toward generating a highlight video of a sports game. Existing methods not only depend on both languages and sports types but also often falsely detect non-target events. In contrast, the proposed method detects sports events using TEDs calculated from several kinds of string features independent of languages and sports. We applied the proposed method to actual sports games, and compared the detected events with the events present in broadcasted highlight videos, and confirmed the effectiveness and the language and sports type independencies of the proposed method.

This demo presents "Smart VideoCooKing" which is a multimedia cooking recipe browsing application on portable Android devices. A multimedia cooking recipe is a cooking recipe where each cooking operation is associated with a corresponding video clip describing it, aimed to facilitate the understanding of cooking operations. In combination with third-party applications, "Smart VideoCooKing" provides useful functions such as playing cooking video clips describing cooking operations quickly, searching information of ingredients easily, and reading aloud cooking directions.

We propose a method to detect the inconsistency between a subject and the speaker for extracting speech scenes from news videos. Speech scenes in news videos contain a wealth of multimedia information, and are valuable as archived material. In order to extract speech scenes from news videos, there is an approach that uses the position and size of a face region. However, it is difficult to extract them with only such approach, since news videos contain non-speech scenes where the speaker is not the subject, such as narrated scenes. To solve this problem, we propose a method to discriminate between speech scenes and narrated scenes based on the co-occurrence between a subject's lip motion and the speaker's voice. The proposed method uses lip shape and degree of lip opening as visual features representing a subject's lip motion, and uses voice volume and phoneme as audio feature representing a speaker's voice. Then, the proposed method discriminates between speech scenes and narrated scenes based on the correlations of these features. We report the results of experiments on videos captured in a laboratory condition and also on actual broadcast news videos. Their results showed the effectiveness of our method and the feasibility of our research goal.

This paper proposes an intelligent traffic sign detector using adaptive learning based on online gathering of training samples from in-vehicle camera image sequences. To detect traffic signs accurately from in-vehicle camera images, various training samples of traffic signs are needed. In addition, to reduce false alarms, various background images should also be prepared before constructing the detector. However, since their appearances vary widely, it is difficult to obtain them exhaustively by manual intervention. Therefore, the proposed method simultaneously obtains both traffic sign images and background images from in-vehicle camera images. Especially, to reduce false alarms, the proposed method gathers background images that were easily mis-detected by a previously constructed traffic sign detector, and re-trains the detector by using them as negative samples. By using retrospectively tracked traffic sign images and background images as positive and negative training samples, respectively, the proposed method constructs a highly accurate traffic sign detector automatically. Experimental results showed the effectiveness of the proposed method.

We propose a visibility estimation method for traffic signs considering temporal environmental changes, as a part of work for the realization of nuisance-free driver assistance systems. Recently, the number of driver assistance systems in a vehicle is increasing. Accordingly, it is becoming important to sort out appropriate information provided from them, because providing too much information may cause driver distraction. To solve such a problem, we focus on a visibility estimation method for controlling the information according to the visibility of a traffic sign. The proposed method sequentially captures a traffic sign by an in-vehicle camera, and estimates its accumulative visibility by integrating a series of instantaneous visibility. By this way, even if the environmental conditions may change temporally and complicatedly, we can still accurately estimate the visibility that the driver perceives in an actual traffic scene. We also investigate the performance of the proposed method and show its effectiveness.

We propose a visibility estimation method for traffic signs as part of work for realization of nuisance-free driving safety support systems. Recently, the number of driving safety support systems in a car has been increasing. As a result, it is becoming important to select appropriate information from them for safe and comfortable driving because too much information may cause driver distraction and may increase the risk of a traffic accident. One of the approaches to avoid such a problem is to alert the driver only with information which could easily be missed. Therefore, to realize such a system, we focus on estimating the visibility of traffic signs. The proposed method is a model-based method that estimates the visibility of traffic signs focusing on the difference of image features between a traffic sign and its surrounding region. In this paper, we investigate the performance of the proposed method and show its effectiveness.