A Critical Look at the Current Usage of Foundation Model for Dense Recognition Task

In recent years large model trained on huge amount of cross-modality data, which is usually be termed as foundation model, achieves conspicuous accomplishment in many fields, such as image recognition and generation. Though achieving great success in their original application case, it is still unclear whether those foundation models can be applied to other different downstream tasks. In this paper, we conduct a short survey on the current methods for discriminative dense recognition tasks, which are built on the pretrained foundation model. And we also provide some preliminary experimental analysis of an existing open-vocabulary segmentation method based on Stable Diffusion, which indicates the current way of deploying diffusion model for segmentation is not optimal. This aims to provide insights for future research on adopting foundation model for downstream task.Comment: This is a short report on the current usage of foundation model (mainly pretrained diffusion model) for downstream dense recognition task (e.g., open vocabulary segmentation). We hope this short report could give an insight to the future researc

Relationship between Pentosidine and Pyridinoline Levels in Human Diabetic Cataract Lenses

The relationship between the levels of two different crosslink compounds, pentosidine and pyridinoline, in human diabetic cataract lenses was investigated to elucidate the pathogenic mechanism of diabetic cataract. Subjects were classified into diabetes mellitus (DM) group and non-DM group according to the presence or absence of DM. The levels of the crosslink compounds were determined using high-performance liquid chromatography and spectrofluorometry after acid hydrolysis. In the non-DM group the pentosidine level was significantly and positively correlated with the pyridinoline level and age. In the DM group the pentosidine level was not significantly correlated with either pyridinoline level or age. Pyridinoline levels and age were not significantly correlated in either group. The increase in crosslink compounds due to glycation and the relationship between the compounds are changed in DM lenses

WeaveNet for Approximating Two-sided Matching Problems

Matching, a task to optimally assign limited resources under constraints, is a fundamental technology for society. The task potentially has various objectives, conditions, and constraints; however, the efficient neural network architecture for matching is underexplored. This paper proposes a novel graph neural network (GNN), \textit{WeaveNet}, designed for bipartite graphs. Since a bipartite graph is generally dense, general GNN architectures lose node-wise information by over-smoothing when deeply stacked. Such a phenomenon is undesirable for solving matching problems. WeaveNet avoids it by preserving edge-wise information while passing messages densely to reach a better solution. To evaluate the model, we approximated one of the \textit{strongly NP-hard} problems, \textit{fair stable matching}. Despite its inherent difficulties and the network's general purpose design, our model reached a comparative performance with state-of-the-art algorithms specially designed for stable matching for small numbers of agents

図形領域の教材についての理解を深める算数的活動 : 「対称な図形」 を題材にした学生に対する実践授業を通して

本研究は、 大学生に算数的活動を通して 「対称な図形」 の実践授業をしたとき、 紋切り型の作品の制作の前後における、 対称な図形に対する学生の認識の変容を考察したものである。調査結果から、 学生は線対称な図形と点対称な図形の理解が深まるとともに、 興味を持って算数的活動に取り組むことができたことが明らかになった。学生自身が算数的活動を体験し楽しむことが将来教員になったときに、 その楽しさを子どもに伝えていくことになるものと考える。 算数的活動は、 学習指導要領の中でも強調されている大切な活動である。 紋切り型の作品制作は、 算数的活動の一例として、 学生が図形教材の理解を深める手段として有効であることを示唆している。In this paper, teaching practice was carried out by conducting mathematical activities involving symmetric figures with university students. Some changes in students\u27 understanding of symmetric figures were observed after "monkiri "(i.e. paper-cutting). The results of the questionnaire survey indicated the following. First, the university students deepened their understanding of line symmetry and point symmetry of geometrical figures. Second, the students seemed to be very interested in the mathematical activities.I believe that the university students can use this experience to interest elementary school children in mathematical activities in the near future

日本の文化を伝承する算数教育についての研究 : 曲尺を使った算数的活動の事例を通して

新学習指導要領では、算数的活動の大切さが強調され、例えば定規やコンパスを使った三角形の作図の活動は小学校第3学年の単元から見られる。そこで本研究では、ものさしの歴史について調べていく過程で、建築道具の1つとして使われている曲尺（かねじゃく）の仕組みを明らかにした。関連して、建築作品に見られる白銀比の仕組みについても文献にあたった。このような日本の文化を現職の小学校教員と学生が体験することによって、現在まで引き継がれている曲尺のよさを認識するとともに、算数的活動の楽しさを実感することができた。このことから、計測という活動は作業的・体験的な算数的活動の中でも大切な内容であることを示唆している。This study considers the importance of using a carpenter\u27s square for elementary school teachers and university students from a mathematical view point by conducting a classroom teaching experiment. The design of the carpenter\u27s square is as follows. The front side of the square has a normal scale; however, the length of one side of the square is engraved on its reverse side (e.g., 10cm is in fact 14.1cm). Further, results of a questionnaire revealed that the elementary school teachers and university students understood how to use a carpenter\u27s square and that they were able to recognize its intrinsic value. On the basis of this ewperiment, this study suggests that measuring devises such as a carpenter\u27s square are valuable in mathematical activities

Exo2EgoDVC: Dense Video Captioning of Egocentric Procedural Activities Using Web Instructional Videos

We propose a novel benchmark for cross-view knowledge transfer of dense video captioning, adapting models from web instructional videos with exocentric views to an egocentric view. While dense video captioning (predicting time segments and their captions) is primarily studied with exocentric videos (e.g., YouCook2), benchmarks with egocentric videos are restricted due to data scarcity. To overcome the limited video availability, transferring knowledge from abundant exocentric web videos is demanded as a practical approach. However, learning the correspondence between exocentric and egocentric views is difficult due to their dynamic view changes. The web videos contain mixed views focusing on either human body actions or close-up hand-object interactions, while the egocentric view is constantly shifting as the camera wearer moves. This necessitates the in-depth study of cross-view transfer under complex view changes. In this work, we first create a real-life egocentric dataset (EgoYC2) whose captions are shared with YouCook2, enabling transfer learning between these datasets assuming their ground-truth is accessible. To bridge the view gaps, we propose a view-invariant learning method using adversarial training in both the pre-training and fine-tuning stages. While the pre-training is designed to learn invariant features against the mixed views in the web videos, the view-invariant fine-tuning further mitigates the view gaps between both datasets. We validate our proposed method by studying how effectively it overcomes the view change problem and efficiently transfers the knowledge to the egocentric domain. Our benchmark pushes the study of the cross-view transfer into a new task domain of dense video captioning and will envision methodologies to describe egocentric videos in natural language