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.

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.

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.

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/.

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.