Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation

Referring Image Segmentation (RIS) aims to segment target objects expressed in natural language within a scene at the pixel level. Various recent RIS models have achieved state-of-the-art performance by generating contextual tokens to model multimodal features from pretrained encoders and effectively fusing them using transformer-based cross-modal attention. While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes. To address this issue, we propose a novel bidirectional token-masking autoencoder (BTMAE) inspired by the masked autoencoder (MAE). The proposed model learns the context of image-to-language and language-to-image by reconstructing missing features in both image and language features at the token level. In other words, this approach involves mutually complementing across the features of images and language, with a focus on enabling the network to understand interconnected deep contextual information between the two modalities. This learning method enhances the robustness of RIS performance in complex sentences and scenes. Our BTMAE achieves state-of-the-art performance on three popular datasets, and we demonstrate the effectiveness of the proposed method through various ablation studies

Tiny Medicine: Nanomaterial-Based Biosensors

Tiny medicine refers to the development of small easy to use devices that can help in the early diagnosis and treatment of disease. Early diagnosis is the key to successfully treating many diseases. Nanomaterial-based biosensors utilize the unique properties of biological and physical nanomaterials to recognize a target molecule and effect transduction of an electronic signal. In general, the advantages of nanomaterial-based biosensors are fast response, small size, high sensitivity, and portability compared to existing large electrodes and sensors. Systems integration is the core technology that enables tiny medicine. Integration of nanomaterials, microfluidics, automatic samplers, and transduction devices on a single chip provides many advantages for point of care devices such as biosensors. Biosensors are also being used as new analytical tools to study medicine. Thus this paper reviews how nanomaterials can be used to build biosensors and how these biosensors can help now and in the future to detect disease and monitor therapies