BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification

Text-based person re-identification (TBPReID) aims to retrieve person images represented by a given textual query. In this task, how to effectively align images and texts globally and locally is a crucial challenge. Recent works have obtained high performances by solving Masked Language Modeling (MLM) to align image/text parts. However, they only performed uni-directional (i.e., from image to text) local-matching, leaving room for improvement by introducing opposite-directional (i.e., from text to image) local-matching. In this work, we introduce Bidirectional Local-Matching (BiLMa) framework that jointly optimize MLM and Masked Image Modeling (MIM) in TBPReID model training. With this framework, our model is trained so as the labels of randomly masked both image and text tokens are predicted by unmasked tokens. In addition, to narrow the semantic gap between image and text in MIM, we propose Semantic MIM (SemMIM), in which the labels of masked image tokens are automatically given by a state-of-the-art human parser. Experimental results demonstrate that our BiLMa framework with SemMIM achieves state-of-the-art Rank@1 and mAP scores on three benchmarks.Comment: Accepted at ICCVW 202

Photonic-crystal nano-photodetector with ultrasmall capacitance for on-chip light-to-voltage conversion without an amplifier

The power consumption of a conventional photoreceiver is dominated by that of the electric amplifier connected to the photodetector (PD). An ultralow-capacitance PD can overcome this limitation, because it can generate sufficiently large voltage without an amplifier when combined with a high-impedance load. In this work, we demonstrate an ultracompact InGaAs PD based on a photonic crystal waveguide with a length of only 1.7 μm and a capacitance of less than 1 fF. Despite the small size of the device, a high responsivity of 1 A/W and a clear 40 Gbit/s eye diagram are observed, overcoming the conventional trade-off between size and responsivity. A resistor-loaded PD was actually fabricated for light-to-voltage conversion, and a kilo-volt/watt efficiency with a gigahertz bandwidth even without amplifiers was measured with an electro-optic probe. Combined experimental and theoretical results reveal that a bandwidth in excess of 10 GHz can be expected, leading to an ultralow energy consumption of less than 1 fJ/bit for the photoreceiver. Amplifier-less PDs with attractive performance levels are therefore feasible and a step toward a densely integrated photonic network/processor on a chip

Automated Sperm Assessment Framework and Neural Network Specialized for Sperm Video Recognition

Infertility is a global health problem, and an increasing number of couples are seeking medical assistance to achieve reproduction, at least half of which are caused by men. The success rate of assisted reproductive technologies depends on sperm assessment, in which experts determine whether sperm can be used for reproduction based on morphology and motility of sperm. Previous sperm assessment studies with deep learning have used datasets comprising images that include only sperm heads, which cannot consider motility and other morphologies of sperm. Furthermore, the labels of the dataset are one-hot, which provides insufficient support for experts, because assessment results are inconsistent between experts, and they have no absolute answer. Therefore, we constructed the video dataset for sperm assessment whose videos include sperm head as well as neck and tail, and its labels were annotated with soft-label. Furthermore, we proposed the sperm assessment framework and the neural network, RoSTFine, for sperm video recognition. Experimental results showed that RoSTFine could improve the sperm assessment performances compared to existing video recognition models and focus strongly on important sperm parts (i.e., head and neck).Comment: Accepted at Winter Conference on Applications of Computer Vision (WACV) 202

Phylogeography of Infraspecific Differentiation of Hydrangea serrata (Hydrangeaceae), a Widespread Shrub Species in the Japanese Archipelago

departmental bulletin pape

Molecular Phylogenetic Analyses of Geographical and Ecological Speciation of Anaphalis margaritacea

departmental bulletin pape

Chimeric Anti-PDPN Antibody ChLpMab-2

Human podoplanin (hPDPN ), a platelet aggregation‐inducing transmembrane glycoprotein, is expressed in different types of tumors, and it binds to C‐type lectin‐like receptor 2 (CLEC ‐2). The overexpression of hPDPN is involved in invasion and metastasis. Anti‐hPDPN monoclonal antibodies (mAbs) such as NZ ‐1 have shown antitumor and antimetastatic activities by binding to the platelet aggregation‐stimulating (PLAG ) domain of hPDPN . Recently, we developed a novel mouse anti‐hPDPN mAb, LpMab‐2, using the cancer‐specific mAb (CasMab) technology. In this study we developed chLpMab‐2, a human–mouse chimeric anti‐hPDPN antibody, derived from LpMab‐2. chLpMab‐2 was produced using fucosyltransferase 8‐knockout (KO ) Chinese hamster ovary (CHO )‐S cell lines. By flow cytometry, chLpMab‐2 reacted with hPDPN ‐expressing cancer cell lines including glioblastomas, mesotheliomas, and lung cancers. However, it showed low reaction with normal cell lines such as lymphatic endothelial and renal epithelial cells. Moreover, chLpMab‐2 exhibited high antibody‐dependent cellular cytotoxicity (ADCC ) against PDPN ‐expressing cells, despite its low complement‐dependent cytotoxicity. Furthermore, treatment with chLpMab‐2 abolished tumor growth in xenograft models of CHO /hPDPN , indicating that chLpMab‐2 suppressed tumor development via ADCC . In conclusion, chLpMab‐2 could be useful as a novel antibody‐based therapy against hPDPN ‐expressing tumors