The Application of OCTA in Assessment of Anti-VEGF Therapy for Idiopathic Choroidal Neovascularization

Purpose. To assess the morphology of idiopathic choroidal neovascularization (ICNV) by optical coherence tomography angiography (OCTA) and determine the therapeutic effects of intravitreal antivascular endothelial growth factor (anti-VEGF). Method. Patients with naive ICNV were assessed by spectral domain optical coherence tomography (SD-OCT) and OCTA in this observational study. The timing of observation was before treatment, 1 day after treatment with intravitreal anti-VEGF injection, and 1 month after the treatment. The central retina thickness (CRT) on SD-OCT, selected CNV area, and flow area on OCTA were measured. Results. A total of 17 eyes from 17 patients with ICNV were included in this study. OCTA showed visible irregular choroidal neovascularization with “tree-in-bud” form on outer retinal layer. After treatment, as well as in the 1-day follow-up, CNV decreased in size from the periphery, and the vessel density was reduced. As shown on OCTA, the selected CNV area and flow area were significantly reduced compared to pretreatment. The rate of CNV vessel area changes was higher on OCTA than the changes in CRT on SD-OCT at 1-day and 1-month follow-up. Conclusion. Intravitreal injection of anti-VEGF is effective for idiopathic choroidal neovascularization, and the treatment outcomes are observable after 1 day. OCTA provides a useful approach for monitoring and evaluating the treatment of intravitreal anti-VEGF for CNV

Big-model Driven Few-shot Continual Learning

Few-shot continual learning (FSCL) has attracted intensive attention and achieved some advances in recent years, but now it is difficult to again make a big stride in accuracy due to the limitation of only few-shot incremental samples. Inspired by distinctive human cognition ability in life learning, in this work, we propose a novel Big-model driven Few-shot Continual Learning (B-FSCL) framework to gradually evolve the model under the traction of the world's big-models (like human accumulative knowledge). Specifically, we perform the big-model driven transfer learning to leverage the powerful encoding capability of these existing big-models, which can adapt the continual model to a few of newly added samples while avoiding the over-fitting problem. Considering that the big-model and the continual model may have different perceived results for the identical images, we introduce an instance-level adaptive decision mechanism to provide the high-level flexibility cognitive support adjusted to varying samples. In turn, the adaptive decision can be further adopted to optimize the parameters of the continual model, performing the adaptive distillation of big-model's knowledge information. Experimental results of our proposed B-FSCL on three popular datasets (including CIFAR100, minilmageNet and CUB200) completely surpass all state-of-the-art FSCL methods.Comment: 9 pages 6 figure

CrossNER: Evaluating Cross-Domain Named Entity Recognition

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. Additionally, we also provide a domain-related corpus since using it to continue pre-training language models (domain-adaptive pre-training) is effective for the domain adaptation. We then conduct comprehensive experiments to explore the effectiveness of leveraging different levels of the domain corpus and pre-training strategies to do domain-adaptive pre-training for the cross-domain task. Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the NER domain adaptation, and our proposed method can consistently outperform existing cross-domain NER baselines. Nevertheless, experiments also illustrate the challenge of this cross-domain NER task. We hope that our dataset and baselines will catalyze research in the NER domain adaptation area. The code and data are available at https://github.com/zliucr/CrossNER.Comment: Accepted in AAAI-202

Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding

Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption. As a multimodal task, an essential aspect of PNG is the visual-linguistic interaction between image and caption. The previous two-stage method aggregates visual contexts from offline-generated mask proposals to phrase features, which tend to be noisy and fragmentary. The recent one-stage method aggregates only pixel contexts from image features to phrase features, which may incur semantic misalignment due to lacking object priors. To realize more comprehensive visual-linguistic interaction, we propose to enrich phrases with coupled pixel and object contexts by designing a Phrase-Pixel-Object Transformer Decoder (PPO-TD), where both fine-grained part details and coarse-grained entity clues are aggregated to phrase features. In addition, we also propose a PhraseObject Contrastive Loss (POCL) to pull closer the matched phrase-object pairs and push away unmatched ones for aggregating more precise object contexts from more phrase-relevant object tokens. Extensive experiments on the PNG benchmark show our method achieves new state-of-the-art performance with large margins.Comment: Accepted by IJCAI 202

Rock crevices determine woody and herbaceous plant cover in the karst critical zone

The study of the critical zones (CZs) of the Earth link the composition and function of aboveground vegetation with the characteristics of the rock layers, providing a new way to study how the unique rock and soil conditions in karst regions affect the aboveground vegetation. Based on survey results of the rocks, soils and vegetation in the dolomite and limestone distribution areas in the karst area of central Guizhou, it was found that woody plant cover increases linearly with the number of cracks with a width of more than 1 mm, while the cover of herbaceous plants shows the opposite trend (p<0.01). The dolomite distribution area is characterized by undeveloped crevices, and the thickness of the soil layer is generally less than 20 cm, which is suitable for the distribution of herbaceous plants with shallow roots. Due to the development of crevices in the limestone distribution area, the soil is deeply distributed through the crevices for the deep roots of trees, which leads to a diversified species composition and a complicated structure in the aboveground vegetation. Based on moderate resolution imaging spectroradiometer (MODIS) remote sensing data from 2001 to 2010, the normalized differentiated vegetation index (NDVI) and annual net primary productivity (NPP) results for each phase of a 16-day interval further indicate that the NDVI of the limestone distribution area is significantly higher than that in the dolomite distribution area, but the average annual NPP is the opposite. The results of this paper indicate that in karst CZs, the lithology determines the structure and distribution of the soil, which further determines the cover of woody and herbaceous plants in the aboveground vegetation. Although the amount of soil in the limestone area may be less than that in the dolomite area, the developed crevice structure is more suitable for the growth of trees with deep roots, and the vegetation activity is strong. At present, the treatment of rocky desertification in karst regions needs to fully consider the rock-soilvegetation- air interactions in karst CZs and propose vegetation restoration measures suitable for different lithologies