CLIP-Driven Fine-Grained Text-Image Person Re-Identification.

Text-Image Person Re-identification (TIReID) aims to retrieve the image corresponding to the given text query from a pool of candidate images. Existing methods employ prior knowledge from single-modality pre-training to facilitate learning, but lack multi-modal correspondence information. Vision-Language Pre-training, such as CLIP (Contrastive Language-Image Pretraining), can address the limitation.

Image-Specific Information Suppression and Implicit Local Alignment for Text-Based Person Search.

Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress, and state-of-the-art (SOTA) methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise.

[Isolation and antimicrobial activities of actinomycetes from vermicompost].

In this paper, actinomycetes were isolated from vermicompost by tablet coating method. Antimicrobial activities of actinomycetes were measured by the agar block method. Strains with high activity were identified based on morphology and biochemical characteristics, as well as 16S rDNA gene sequence analysis.