Text-Only Image Captioning with Multi-Context Data Generation

Text-only Image Captioning (TIC) is an approach that aims to construct a model solely based on text that can accurately describe images. Recently, diffusion models have demonstrated remarkable capabilities in generating high-quality images that are semantically coherent with given texts. This presents an opportunity to generate synthetic training images for TIC. However, we have identified a challenge that the images generated from simple descriptions typically exhibit a single perspective with one or limited contexts, which is not aligned with the complexity of real-world scenes in the image domain. In this paper, we propose a novel framework that addresses this issue by introducing multi-context data generation. Starting with an initial text corpus, our framework employs a large language model to select multiple sentences that describe the same scene from various perspectives. These sentences are then summarized into a single sentence with multiple contexts. We generate simple images using the straightforward sentences and complex images using the summarized sentences through diffusion models. Finally, we train the model exclusively using the synthetic image-text pairs obtained from this process. Experimental results demonstrate that our proposed framework effectively tackles the central challenge we have identified, achieving the state-of-the-art performance on popular datasets such as MSCOCO, Flickr30k, and SS1M

CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation

To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals from unlabeled data. We show that these two approaches, at the two extreme ends of the task-specificity spectrum, are suboptimal for the task performance. Utilizing too little task-specific training signals causes underfitting to the ground-truth labels of downstream tasks, while the opposite causes overfitting to the ground-truth labels. To this end, we propose a novel Class-Agnostic Semi-Supervised Learning (CA-SSL) framework to achieve a more favorable task-specificity balance in extracting training signals from unlabeled data. CA-SSL has three training stages that act on either ground-truth labels (labeled data) or pseudo labels (unlabeled data). This decoupling strategy avoids the complicated scheme in traditional SSL methods that balances the contributions from both data types. Especially, we introduce a warmup training stage to achieve a more optimal balance in task specificity by ignoring class information in the pseudo labels, while preserving localization training signals. As a result, our warmup model can better avoid underfitting/overfitting when fine-tuned on the ground-truth labels in detection and segmentation tasks. Using 3.6M unlabeled data, we achieve a significant performance gain of 4.7% over ImageNet-pretrained baseline on FCOS object detection. In addition, our warmup model demonstrates excellent transferability to other detection and segmentation frameworks.Comment: Appeared in ECCV202

Spatial-Semantic Collaborative Cropping for User Generated Content

A large amount of User Generated Content (UGC) is uploaded to the Internet daily and displayed to people world-widely through the client side (mobile and PC). This requires the cropping algorithms to produce the aesthetic thumbnail within a specific aspect ratio on different devices. However, existing image cropping works mainly focus on landmark or landscape images, which fail to model the relations among the multi-objects with the complex background in UGC. Besides, previous methods merely consider the aesthetics of the cropped images while ignoring the content integrity, which is crucial for UGC cropping. In this paper, we propose a Spatial-Semantic Collaborative cropping network (S2CNet) for arbitrary user generated content accompanied by a new cropping benchmark. Specifically, we first mine the visual genes of the potential objects. Then, the suggested adaptive attention graph recasts this task as a procedure of information association over visual nodes. The underlying spatial and semantic relations are ultimately centralized to the crop candidate through differentiable message passing, which helps our network efficiently to preserve both the aesthetics and the content integrity. Extensive experiments on the proposed UGCrop5K and other public datasets demonstrate the superiority of our approach over state-of-the-art counterparts

Chronic E-Cigarette Use Impairs Endothelial Function on the Physiological and Cellular Levels.

BACKGROUND: The harmful vascular effects of smoking are well established, but the effects of chronic use of electronic cigarettes (e-cigarettes) on endothelial function are less understood. We hypothesized that e-cigarette use causes changes in blood milieu that impair endothelial function. METHODS: Endothelial function was measured in chronic e-cigarette users, chronic cigarette smokers, and nonusers. We measured effects of participants sera, or e-cigarette aerosol condensate, on NO and H2O2 release and cell permeability in cultured endothelial cells (ECs). RESULTS: E-cigarette users and smokers had lower flow-mediated dilation (FMD) than nonusers. Sera from e-cigarette users and smokers reduced VEGF (vascular endothelial growth factor)-induced NO secretion by ECs relative to nonuser sera, without significant reduction in endothelial NO synthase mRNA or protein levels. E-cigarette user sera caused increased endothelial release of H2O2, and more permeability than nonuser sera. E-cigarette users and smokers exhibited changes in circulating biomarkers of inflammation, thrombosis, and cell adhesion relative to nonusers, but with distinct profiles. E-cigarette user sera had higher concentrations of the receptor for advanced glycation end products (RAGE) ligands S100A8 and HMGB1 (high mobility group box 1) than smoker and nonuser sera, and receptor for advanced glycation end product inhibition reduced permeability induced by e-cigarette user sera but did not affect NO production. CONCLUSIONS: Chronic vaping and smoking both impair FMD and cause changes in the blood that inhibit endothelial NO release. Vaping, but not smoking, causes changes in the blood that increase microvascular endothelial permeability and may have a vaping-specific effect on intracellular oxidative state. Our results suggest a role for RAGE in e-cigarette-induced changes in endothelial function

Chronic E-Cigarette Use Impairs Endothelial Function on the Physiological and Cellular Levels

BackgroundThe harmful vascular effects of smoking are well established, but the effects of chronic use of electronic cigarettes (e-cigarettes) on endothelial function are less understood. We hypothesized that e-cigarette use causes changes in blood milieu that impair endothelial function.MethodsEndothelial function was measured in chronic e-cigarette users, chronic cigarette smokers, and nonusers. We measured effects of participants' sera, or e-cigarette aerosol condensate, on NO and H2O2 release and cell permeability in cultured endothelial cells (ECs).ResultsE-cigarette users and smokers had lower flow-mediated dilation (FMD) than nonusers. Sera from e-cigarette users and smokers reduced VEGF (vascular endothelial growth factor)-induced NO secretion by ECs relative to nonuser sera, without significant reduction in endothelial NO synthase mRNA or protein levels. E-cigarette user sera caused increased endothelial release of H2O2, and more permeability than nonuser sera. E-cigarette users and smokers exhibited changes in circulating biomarkers of inflammation, thrombosis, and cell adhesion relative to nonusers, but with distinct profiles. E-cigarette user sera had higher concentrations of the receptor for advanced glycation end products (RAGE) ligands S100A8 and HMGB1 (high mobility group box 1) than smoker and nonuser sera, and receptor for advanced glycation end product inhibition reduced permeability induced by e-cigarette user sera but did not affect NO production.ConclusionsChronic vaping and smoking both impair FMD and cause changes in the blood that inhibit endothelial NO release. Vaping, but not smoking, causes changes in the blood that increase microvascular endothelial permeability and may have a vaping-specific effect on intracellular oxidative state. Our results suggest a role for RAGE in e-cigarette-induced changes in endothelial function