In this paper, we propose a method for video reflection removal using a video restoration framework with enhanced deformable networks (EDVR). We examine the effect of each module in EDVR on video ...reflection removal and modify the models using 3D convolutions. The performance of each modified model is evaluated in terms of the RMSE between the structural similarity (SSIM) and the smoothed SSIM representing temporal consistency.
We introduce MOD-CL, a multi-label object detection framework that utilizes constrained loss in the training process to produce outputs that better satisfy the given requirements. In this paper, we ...use \(\mathrm{MOD_{YOLO}}\), a multi-label object detection model built upon the state-of-the-art object detection model YOLOv8, which has been published in recent years. In Task 1, we introduce the Corrector Model and Blender Model, two new models that follow after the object detection process, aiming to generate a more constrained output. For Task 2, constrained losses have been incorporated into the \(\mathrm{MOD_{YOLO}}\) architecture using Product T-Norm. The results show that these implementations are instrumental to improving the scores for both Task 1 and Task 2.
Detecting the actions of each object is detrimental to improving the usability of the model, but the risk of misrecognition increases as the number of label combinations increases. Therefore, we ...propose a framework that reduces the amount of misrecognition by utilizing the requirements that the set of labels has to satisfy. Specifically, we propose MODYOLO, a novel multilabel object detection model built upon the state-of-the-art object detection model YOLOv8, and develop our framework on top of it. We then assess the framework's effectiveness by applying it to the ROAD-R Challenge for NeurIPS 2023 competition. For Task 1, we introduce the Corrector Model and Blender Model, two new models that follow after the object detection process, aiming to generate a more constrained output. For Task 2, constrained losses have been incorporated into the training process of MODYOLO using Fuzzy Logic. The results show that using the above framework was instrumental to improving the scores for both Tasks 1 and 2, allowing us to place third and first in the subsequent tasks.
自動運転において各物体が行っている動作を認識することはモデルの利便性を向上させることにつながるが，細かい動作の組み合わせは非常に多く存在するため，誤認識のリスクが高まってしまう．そこで，本研究では各組み合わせが満たすべき性質を制約として書き起こし，モデルの学習時や推論時に制約に関する情報を活用することでモデルの性能や誤認知の頻度を低下させるフレームワークを提案する．具体的には物体検知における最先端モデルであるYOLOv8をベースとしてマルチラベル認識が可能なように拡張したMODYOLOを開発し，ROAD-R Challenge for NeurIPS 2023コンペティションへ適用した結果の効果について検討する．タスク１では物体検知モデルの推論結果を制御する機構としてコレクターモデルとブレンダーモデルと呼ばれる２つのモデルを新たに提案し，タスク２ではファジー論理を用いた制約項を損失に付加した上でMODYOLOの学習を行う．以上を採用した結果，タスク２では優勝，タスク１では3位入賞の功績が得られており，実データに対する本フレームワークの効果が示唆さている．
The peroxisome proliferator-activated receptor (PPAR) is one of the indispensable transcription factors for regulating lipid metabolism in various tissues. In our screening for natural compounds that ...activate PPAR using luciferase assays, a branched-carbon-chain alcohol (a component of chlorophylls), phytol, has been identified as a PPARα-specific activator. Phytol induced the increase in PPARα-dependent luciferase activity and the degree of in vitro binding of a coactivator, SRC-1, to GST-PPARα. Moreover, the addition of phytol upregulated the expression of PPARα-target genes at both mRNA and protein levels in PPARα-expressing HepG2 hepatocytes. These findings indicate that phytol is functional as a PPARα ligand and that it stimulates the expression of PPARα-target genes in intact cells. Because PPARα activation enhances circulating lipid clearance, phytol may be important in managing abnormalities in lipid metabolism.
The peroxisome proliferator-activated receptor (PPAR) is one of the indispensable transcription factors for regulating lipid metabolism in various tissues. In our screening for natural compounds that ...activate PPAR using luciferase assays, a branched-carbon-chain alcohol (a component of chlorophylls), phytol, has been identified as a PPARalpha-specific activator. Phytol induced the increase in PPARalpha-dependent luciferase activity and the degree of in vitro binding of a coactivator, SRC-1, to GST-PPARalpha. Moreover, the addition of phytol upregulated the expression of PPARalpha-target genes at both mRNA and protein levels in PPARalpha-expressing HepG2 hepatocytes. These findings indicate that phytol is functional as a PPARalpha ligand and that it stimulates the expression of PPARalpha-target genes in intact cells. Because PPARalpha activation enhances circulating lipid clearance, phytol may be important in managing abnormalities in lipid metabolism.
The peroxisome proliferator-activated receptor (PPAR) is one of the indispensable transcription factors for regulating lipid metabolism in various tissues. In our screening for natural compounds that ...activate PPAR using luciferase assays, a branched-carbon-chain alcohol (a component of chlorophylls), phytol, has been identified as a PPAR{alpha}-specific activator. Phytol induced the increase in PPAR{alpha}-dependent luciferase activity and the degree of in vitro binding of a coactivator, SRC-1, to GST-PPAR{alpha}. Moreover, the addition of phytol upregulated the expression of PPAR{alpha}-target genes at both mRNA and protein levels in PPAR{alpha}-expressing HepG2 hepatocytes. These findings indicate that phytol is functional as a PPAR{alpha} ligand and that it stimulates the expression of PPAR{alpha}-target genes in intact cells. Because PPAR{alpha} activation enhances circulating lipid clearance, phytol may be important in managing abnormalities in lipid metabolism.
