Cationic liposomes overcome neutralizing antibodies and enhance reovirus efficacy in ovarian cancer.

Reovirus (Reo) has shown promising potential in specifically killing tumor cells, and offering new possibilities for ovarian cancer (OC) treatment. However, neutralizing antibodies in the ascites from OC patients greatly limit the further application of Reo. In this study, we employed cationic liposomes (Lipo) to deliver Reo, significantly enhancing its ability to enter OC cells and its effectiveness in killing these cells under ascitic conditions.

Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval.

The composed image retrieval (CIR) task aims to retrieve the desired target image for a given multimodal query, i.e., a reference image with its corresponding modification text.

Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification.

Cloth-changing person reidentification (ReID) is a newly emerging research topic aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g.

A Semantic-Aware Attention and Visual Shielding Network for Cloth-Changing Person Re-Identification.

Cloth-changing person re-identification (ReID) is a newly emerging research topic that aims to retrieve pedestrians whose clothes are changed. Since the human appearance with different clothes exhibits large variations, it is very difficult for existing approaches to extract discriminative and robust feature representations. Current works mainly focus on body shape or contour sketches, but the human semantic information and the potential consistency of pedestrian features before and after changing clothes are not fully explored or are ignored.

The Effects of the Type of Information Played in Environmentally Themed Short Videos on Social Media on People's Willingness to Protect the Environment.

This study used a 2 × 2 experimental design to explore the effects of message type (non-narrative vs. narrative information) and social media metrics (high vs. low numbers of plays) of low-carbon-themed social media short videos on people's willingness to protect the environment.

A Temporal-Aware Relation and Attention Network for Temporal Action Localization.

Temporal action localization is currently an active research topic in computer vision and machine learning due to its usage in smart surveillance. It is a challenging problem since the categories of the actions must be classified in untrimmed videos and the start and end of the actions need to be accurately found. Although many temporal action localization methods have been proposed, they require substantial amounts of computational resources for the training and inference processes.

Partially Supervised Compatibility Modeling.

Fashion Compatibility Modeling (FCM), which aims to automatically evaluate whether a given set of fashion items makes a compatible outfit, has attracted increasing research attention. Recent studies have demonstrated the benefits of conducting the item representation disentanglement towards FCM. Although these efforts have achieved prominent progress, they still perform unsatisfactorily, as they mainly investigate the visual content of fashion items, while overlooking the semantic attributes of items (e.

ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning.

In recent years, remarkable progress in zero-shot learning (ZSL) has been achieved by generative adversarial networks (GAN). To compensate for the lack of training samples in ZSL, a surge of GAN architectures have been developed by human experts through trial-and-error testing. Despite their efficacy, however, there is still no guarantee that these hand-crafted models can consistently achieve good performance across diversified datasets or scenarios.

A Pairwise Attentive Adversarial Spatiotemporal Network for Cross-Domain Few-Shot Action Recognition-R2.

- Action recognition is a popular research topic in the computer vision and machine learning domains. Although many action recognition methods have been proposed, only a few researchers have focused on cross-domain few-shot action recognition, which must often be performed in real security surveillance. Since the problems of action recognition, domain adaptation, and few-shot learning need to be simultaneously solved, the cross-domain few-shot action recognition task is a challenging problem.

Chinese Image Caption Generation via Visual Attention and Topic Modeling.

Automatic image captioning is to conduct the cross-modal conversion from image visual content to natural language text. Involving computer vision (CV) and natural language processing (NLP), it has become one of the most sophisticated research issues in the artificial-intelligence area. Based on the deep neural network, the neural image caption (NIC) model has achieved remarkable performance in image captioning, yet there still remain some essential challenges, such as the deviation between descriptive sentences generated by the model and the intrinsic content expressed by the image, the low accuracy of the image scene description, and the monotony of generated sentences.

Cooperation Learning From Multiple Social Networks: Consistent and Complementary Perspectives.

GWI survey has highlighted the flourishing use of multiple social networks: the average number of social media accounts per Internet user is 5.54, and among them, 2.82 are being used actively.

Neural Multimodal Cooperative Learning Toward Micro-Video Understanding.

The prevailing characteristics of micro-videos result in the less descriptive power of each modality. The micro-video representations, several pioneer efforts proposed, are limited in implicitly exploring the consistency between different modality information but ignore the complementarity. In this paper, we focus on how to explicitly separate the consistent features and the complementary features from the mixed information and harness their combination to improve the expressiveness of each modality.