Composed image retrieval (CIR) utilizes a visual and a textual query to retrieve a target image. Prior work often abstracts the visual query into textual representations, which may discard instance-level visual cues; however, directly using these raw visual queries is empirically unreliable for improving the retrieval accuracy. Thus, we ask: why are visual cues difficult to leverage reliably in CIR, and when can their potential be realized? To answer this question, we conduct analysis with a reranking-based framework by reranking the top-K candidate pool and measuring per-query ground truth rank changes with different cues. Experiments on CIRR and CIRCO datasets show that raw query images frequently induce degradations, whereas controlled cues (e.g., edited images and generated captions) yield more reliable improvements and provide complementary evidence across modalities, with their impact being most pronounced when the initial ranking is uncertain.

Causal self-attention models such as SASRec are widely used in sequential recommendation, where learned attention weights are often assumed to provide crucial importance weighting over past interactions. Yet it is unclear when predictive performance truly depends on such non-uniform weighting. We study a controlled SASRec variant that replaces learned attention weights with uniform aggregation and is trained under an otherwise identical block structure and training recipe. Across fourteen benchmark datasets, this modification often yields performance comparable to the original model, with clear dataset-dependent exceptions. To explain this heterogeneity, we introduce a stage-wise norm-based decomposition that quantifies self-preserving vs. cross-position mixing within attention blocks. Across datasets, we find distinct regimes: low mixing yields robustness to uniformization; higher mixing tends to coincide with sensitivity, while some datasets exhibit substantial mixing without dependence on learned weighting. Our results provide a practical diagnostic for identifying when attention weighting is functionally utilized in sequential recommendation. The code is available at: https://github.com/keito0329/revisiting-sasrec.

A financial recommender system couples two objectives: ranking for preference alignment so that users actually adopt the recommendations, and ranking for outcome quality so that adoption translates into value. These objectives can conflict: return-driven lists may narrow diversification and miss user tastes, while relevance-only lists deliver weak realized returns. To address these problems, we propose Risk-aware Utility re-RAnking (RURA), a plug-in method that operates on the upstream top candidates and optimizes a user-specific expected-utility objective. RURA injects investor risk tolerance into the utility, includes a likelihood-aware variant that integrates calibrated adoption probabilities, and uses a single hyperparameter to control diversification to preserve upstream order while trading minimal nDCG loss for ROI gains. Experiments on a real-world dataset demonstrate that RURA outperforms risk-aware baselines in ROI while keeping nDCG within the range of a strong risk-aware baseline and delivering higher expected utility across risk groups.

We propose an image attribute editing method with the mask-based retention loss. Although conventional image attribute editing methods can edit a particular attribute, they cannot retain non-editing attributes including unknown attributes before and after editing, which causes unexpected changes in the edited images. We solve this problem by dividing the pre- and post-edited images into the editing and non-editing regions and increasing the image similarity in the non-editing regions. In this paper, we introduce the novel mask-based retention loss to retain the non-editing regions. To compute the mask-based retention loss, we divide the images into the editing and non-editing regions by using a binary mask generated from the difference between the pre- and post-edited images. Experimental results show that our proposed method is qualitatively and quantitatively superior to state-of-the-art methods.

In this paper, we address a novel social recommendation for users who have no interactions with items (unobserved users). This task can provide many applications such as recommendations for cold-start users after the first sign-up and targeted advertising, thus, it seems to be extremely meaningful. However, existing social recommendation methods are unsuitable for this task since they assume that all users have interactions with items or cannot recommend more effectively than MostPopular recommendation. Towards this end, we propose Unobserved user-oriented Graph Social Recommendation (UGSR), which learns the preferences of unobserved users and provides richer recommendations than MostPopular recommendation. The popularity-aware graph convolutional network, which is carefully designed for this task, simultaneously considers some user-item interactions, social relations, and item popularity for the effective user and item modeling.

For developing valuable rubber materials, machine learning-based computer-aided analysis systems have been attracting a lot of attention. However, these systems mainly focus on analyzing the table and textual data, and the electron microscope images including the rich material information have not been enough analyzed. By effectively using these electron microscope images, further support for the material discovery is realized. In this paper, we present a material information retrieval system via electron microscope image space. Our system aims to support visually and comprehensively grasping the relationships between various rubber materials and those properties. By effectively using the electron microscope image space for material information retrieval, it is expected that the advances in material development are further accelerated.

This paper presents a zero-shot visual sentiment prediction method using semantic representation features of texts from tweets as the non-visual auxiliary data. Previous studies show that visual sentiment prediction methods can only predict the sentiment labels that are the same as the labels of the sentiment theory used in the training dataset, which means that they cannot predict the new sentiment label used in different sentiment theories. To solve the problem of predicting new labels, zero-shot learning has been proposed. The previous zero-shot visual sentiment prediction method uses Word2vec features and the adjective-noun pair features to obtain the semantical relationship between images and sentiment words to predict unseen sentiments. However, many adjective-noun pairs are not related to sentiments, which makes it difficult to compensate for an affective gap between low-level visual features and high-level sentiment semantics. Thus, to better compensate for the affective gap, it is considered to introduce the new non-visual auxiliary data. As people tend to share their feelings with both images and texts on social networking services, the texts from tweets are effective as the side information of the images in visual sentiment prediction. Thus, we introduce the semantic representations from tweets as the new non-visual auxiliary data to construct an affective embedding space, which makes a more effective zero-shot visual sentiment prediction model. Moreover, we propose a cross-dataset zero-shot task for visual sentiment prediction, which is more consistent with the real situation that the testing and training images may be in different domains. The contributions in this paper are to combine several semantic representation features for zero-shot visual sentiment prediction and the proposal of the cross-dataset zero-shot task for visual sentiment prediction. The experiments on several open datasets show the effectiveness of the proposed method.

Image retrieval from a given text query (text-to-image retrieval) is one of the most essential systems, and it is effectively utilized for databases (DBs) on the Web. To make them more versatile and familiar, a retrieval system that is adaptive even for personal DBs such as images in smartphones and lifelogging devices should be considered. In this paper, we present a novel text-to-image retrieval system that is specialized for personal DBs. With the cross-modal scheme and the question-answering scheme, the developed system enables users to obtain the desired image effectively even from personal DBs. Our demo is available at https://sites.google.com/view/ir-questioner/.

This paper presents a novel method to retrieve similar scenes in soccer videos with weak annotations via multimodal use of bidirectional long short-term memory (BiLSTM). The significant increase in the number of different types of soccer videos with the development of technology brings valid assets for effective coaching, but it also increases the work of players and training staff. We tackle this problem with a nontraditional combination of pre-trained models for feature extraction and BiLSTMs for feature transformation. By using the pre-trained models, no training data is required for feature extraction. Then effective feature transformation for similarity calculation is performed by applying BiLSTM trained with weak annotations. This transformation allows for highly accurate capture of soccer video context from less annotation work. In this paper, we achieve an accurate retrieval of similar scenes by multimodal use of this BiLSTM-based transformer trainable with less human effort. The effectiveness of our method was verified by comparative experiments with state-of-the-art using actual soccer video dataset.

Cross-modal retrieval methods retrieve desired images from a query text by learning relationships between texts and images. This retrieval approach is one of the most effective ways in the easiness of query preparation. Recent cross-modal retrieval is convenient and accurate when users input a query text that can uniquely identify the desired image. Meanwhile, users frequently input ambiguous query texts, and these ambiguous queries make it difficult to obtain the desired images. To alleviate these difficulties, in this paper, we propose a novel interactive cross-modal retrieval method based on question answering (QA) with users. The proposed method analyses candidate images and asks users about information that can narrow retrieval candidates effectively. By only answering the questions generated by the proposed method, users can reach their desired images even from an ambiguous query text. Experimental results show the effectiveness of the proposed method.

A new tourism category classification method through estimation of existence of reliable classification results is presented in this paper. The proposed method obtains two kinds of classification results by applying a convolutional neural network to tourism images and applying a Fuzzy K-nearest neighbor algorithm to geotags attached to the tourism images. Then the proposed method estimates existence of reliable classification results in the above two results. If the reliable result is included, the result is selected as the final classification result. If any reliable result is not included, the final result is obtained by another approach based on a multiple annotator logistic regression model. Consequently, the proposed method enables accurate classification based on the new estimation scheme.