Large language models (LLMs) are increasingly used to support creative tasks such as research idea generation. While recent work has shown that structured dialogues between LLMs can improve the novelty and feasibility of generated ideas, the optimal design of such interactions remains unclear. In this study, we conduct a comprehensive analysis of multi-agent LLM dialogues for scientific ideation. We compare different configurations of agent roles, number of agents, and dialogue depth to understand how these factors influence the novelty and feasibility of generated ideas. Our experimental setup includes settings where one agent generates ideas and another critiques them, enabling iterative improvement. Our results show that enlarging the agent cohort, deepening the interaction depth, and broadening agent persona heterogeneity each enrich the diversity of generated ideas. Moreover, specifically increasing critic-side diversity within the ideation–critique–revision loop further boosts the feasibility of the final proposals. Our findings offer practical guidelines for building effective multi-agent LLM systems for scientific ideation.

This paper proposes a novel algorithm of discovering the structure of a kaleidoscopic imaging system that consists of multiple planar mirrors and a camera. The kaleidoscopic imaging system can be recognized as the virtual multi-camera system and has strong advantages in that the virtual cameras are strictly synchronized and have the same intrinsic parameters. In this paper, we focus on the extrinsic calibration of the virtual multi-camera system. The problems to be solved in this paper are two-fold. The first problem is to identify to which mirror chamber each of the 2D projections of mirrored 3D points belongs. The second problem is to estimate all mirror parameters, i.e., normals, and distances of the mirrors. The key contribution of this paper is to propose novel algorithms for these problems using a single 3D point of unknown geometry by utilizing a kaleidoscopic projection constraint, which is an epipolar constraint on mirror reflections. We demonstrate the performance of the proposed algorithm of chamber assignment and estimation of mirror parameters with qualitative and quantitative evaluations using synthesized and real data.

We propose an automatic evaluation method of machine translation that uses source language sentences regarded as additional pseudo references.The proposed method evaluates a translation hypothesis in a regression model.The model takes the paired source, reference, and hypothesis sentence all together as an input.A pretrained large scale cross-lingual language model encodes the input to sentence-pair vectors, and the model predicts a human evaluation score with those vectors.Our experiments show that our proposed method using Crosslingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences.Additionally, using source sentences in our proposed method is confirmed to improve the evaluation performance.

This paper proposed the wind sensorless identification method for wind turbine using online parameter identification. In the proposed On-line parameter identification is used to identify the parameter kopt and improve the characteristics in optimal torque control that does not require wind speed information. The parameter identification is performed using the Recursive least squares method in the wind turbine using PMSG. Further, the identied optimal parameter kopt contributes to the detection of all parameter error of the wind turbine and characteristic change with aged deteriorations. The effectiveness of the proposed identification method was veried by simulations with using MATLAB / SIMULINK.

This paper aims to establish a novel VR system for evaluating the performance of baseball batters. Existing VR systems for sports have been utilized as a tool for image training. In order to move such VR systems to the next stage, we introduce functions that sense the users' reaction to the VR stimulus. Our VR system has three features; (a) it synthesizes highly realistic VR video from the data captured in actual games, (b) it estimates the reaction of the user to the VR stimulus by capturing the 3D positions of full body parts, and (c) it consists of off-the-shelf devices and is easy to use. Our demonstration provides users with a chance to experience our VR system and give them some quick feedback by visualizing the estimated 3D positions of their body parts.

This paper proposes a learning-based quality evaluation framework for inpainted results that does not require any subjectively annotated training data. Image inpainting, which removes and restores unwanted regions in images, is widely acknowledged as a task whose results are quite difficult to evaluate objectively. Thus, existing learning-based image quality assessment (IQA) methods for inpainting require subjectively annotated data for training. However, subjective annotation requires huge cost and subjects’ judgment occasionally differs from person to person in accordance with the judgment criteria. To overcome these difficulties, the proposed framework generates and uses simulated failure results of inpainted images whose subjective qualities are controlled as the training data. We also propose a masking method for generating training data towards fully automated training data generation. These approaches make it possible to successfully estimate better inpainted images, even though the task is quite subjective. To demonstrate the effectiveness of our approach, we test our algorithm with various datasets and show it outperforms existing IQA methods for inpainting.

This paper proposes an image quality assessment (IQA) method for image inpainting, aiming at selecting the best one from a plurality of results. It is known that inpainting results vary largely with the method used for inpainting and the parameters set. Thus, in a typical use case, users need to manually select the inpainting method and the parameters that yield the best result. This manual selection takes a great deal of time and thus there is a great need for a way to automatically estimate the best result. Unlike existing IQA methods for inpainting, our method solves this problem as a learning-based ordering task between inpainted images. This approach makes it possible to introduce auto-generated training sets for more effective learning, which has been difficult for existing methods because judging inpainting quality is quite subjective. Our method focuses on the following three points: (1) the problem can be divided into a set of “pairwise preference order estimation” elemental problems, (2) this pairwise ordering approach enables a training set to be generated automatically, and (3) effective feature design is enabled by investigating actually measured human gazes for order estimation.

This study aims at ascertaining the applicability of a virtual reality environment (VRE) to sports training. Hitting an incoming object is one of the most common actions in various ball games, in which players are required to move to a suitable position and hit the object in a split second; this is a complicated task requiring spatio-temporal reaction to the object. Due to this complexity, how a VRE can serve as a training environment still remains an open question. In the work reported in this paper, we investigated the idea of substituting a VRE for an actual environment for training on the task of hitting a baseball. By focusing on the batter's temporal behavior with real and virtual environments, we clarified factors that contribute to the batter's reaction. This helped us understand how training VREs can be effectively utilized and the VRE requirements needed for sports training.

This paper proposes a novel algorithm that calibrates multiple cameras scattered across a broad area. The key idea of the proposed method is “using the position of an omnidirectional camera as a reference point.” The common approach to calibrating multiple cameras assumes that the cameras capture at least some common points. This means calibration becomes quite difficult if there are no shared points in each camera's field of view (FOV). The proposed method uses the position of an omnidirectional camera to determine point correspondence. The position of an omnidirectional camera relative to the calibrated camera is estimated by the theory of epipolar geometry, even if the omnidirectional camera is placed outside the camera's FOV. This property makes our method applicable to multiple cameras scattered across a broad area. Qualitative and quantitative evaluations using synthesized and real data, e.g., a sports field, demonstrate the advantages of the proposed method.

This paper proposes a new extrinsic calibration of kaleidoscopic imaging system by estimating normals and distances of the mirrors. The problem to be solved in this paper is a simultaneous estimation of all mirror parameters consistent throughout multiple reflections. Unlike conventional methods utilizing a pair of direct and mirrored images of a reference 3D object to estimate the parameters on a per-mirror basis, our method renders the simultaneous estimation problem into solving a linear set of equations. The key contribution of this paper is to introduce a linear estimation of multiple mirror parameters from kaleidoscopic 2D projections of a single 3D point of unknown geometry. Evaluations with synthesized and real images demonstrate the performance of the proposed algorithm in comparison with conventional methods.

This paper proposes a novel framework for image and video completion that removes and restores unwanted regions inside them. Most existing works fail to carry out the completion processing when similar regions do not exist in undamaged regions. To overcome this, our approach creates similar regions by projecting a low dimensional space from the original space. The approach comprises three stages. First, input images/videos are converted to a lower dimensional feature space. Second, a damaged region is restored in the converted feature space. Finally, inverse conversion is performed from the lower dimensional space to the original space. This generates two advantages: (1) it enhances the possibility of applying patches dissimilar to those in the original color space and (2) it enables the use of many existing restoration methods, each having various advantages, because the feature space for retrieving the similar patches is the only extension. The framework’s effectiveness was verified in experiments using various methods, the feature space for restoration in the second stage, and inverse conversion methods.

This paper proposes a method that blindly predicts preference order between inpainted images, aiming at selecting the best one from a plurality of results. Image inpainting, which removes unwanted regions and restores them, has attracted recent attention. However, it is known that the inpainting result varies largely with the method used for inpainting and the parameters set. Thus, in a typical use case, users need to manually select the inpainting method and the parameter that yields the best one. This manual selection takes a great deal of time and thus there is a great need for a way to automatically estimate the best result. Although some methods, such as estimating perceptual preference score from image features, have been proposed in recent years, none of them are considered very promising approaches. Our method focuses on the following two points: (1) what we essentially need is a preference order relation rather than an absolute score, and (2) we consider that image features for order estimation can be effectively designed by using actually measured human visual attention. Comparison with other image quality assessment methods shows that our method estimates the preference order with high accuracy.

A novel framework for image/video content completion comprising three stages is proposed. First, input images/videos are converted to a lower dimensional feature space, which is done to achieve effective restoration even in cases where a damaged region includes complex structures and changes in color. Second, a damaged region is restored in the converted feature space. Finally, an inverse conversion from the lower dimensional feature space to the original feature space is performed to generate the completed image in the original feature space. This three-step solution generates two advantages. First, it enhances the possibility of applying patches dissimilar to those in the original color space. Second, it enables the use of many existing restoration methods, each having various advantages, because the feature space for retrieving the similar patches is the only extension. Experiments verify the effectiveness of the proposed framework.

A method for blindly predicting inpainted image quality is proposed for enhancing the robustness of diminished reality (DR), which uses inpainting to remove unwanted objects by replacing them with background textures in real time. The method maps from inpainted image features to subjective image quality scores without the need for reference images. It enables more complex background textures to be applied to DR.

A system providing visual feedback of a trainee's motions for effectively enhancing motor learning is presented. It provides feedback in synchronization with a reference motion from multiple view angles automatically with only a few seconds delay. Because the feedback is provided automatically, a trainee can obtain it without performing any operations while the memory of the motion is still clear. By employing features with low computational cost, the system achieves synchronized video feedback with four cameras connected to a consumer tablet PC.

This paper is aimed at calibrating the relative posture and position, i.e. extrinsic parameters, of a stationary camera against a 3D reference object which is not directly visible from the camera. We capture the reference object via a mirror under three different unknown poses, and then calibrate the extrinsic parameters from 2D appearances of reflections of the reference object in the mirrors. The key contribution of this paper is to present a new algorithm which returns a unique solution of three P3P problems from three mirrored images. While each P3P problem has up to four solutions and therefore a set of three P3P problems has up to 64 solutions, our method can select a solution based on an orthogonality constraint which should be satisfied by all families of reflections of a single reference object. In addition we propose a new scheme to compute the extrinsic parameters by solving a large system of linear equations. These two points enable us to provide a unique and robust solution. We demonstrate the advantages of the proposed method against a state-of-the-art by qualitative and quantitative evaluations using synthesized and real data.