Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.Comment: 6 pages, 5 figure

Progress in Preparation and Application of Anthocyanin-Starch Complexes: A Review

Anthocyanins are natural colorants that have attracted increasing attention due to their wide color range, non-toxicity and health benefits. Although anthocyanins have great application potential in the food and pharmaceutical industries, their application is limited due to the relative instability. Starch is considered as a good protective agent for anthocyanins, which can improve the stability of anthocyanins during storage. In recent years, many studies have combined the two compounds by different methods such as physical and chemical methods. This can not only enhance the stability of anthocyanins, but also improve the mechanical properties of starch, which will result in better application of starch and anthocyanins in drug delivery, biomedicine, agriculture, and food production. The basic structural characteristics of anthocyanins and starch, and the various methods for preparing anthocyanin-starch complexes are summarized herein. Also, the effects of anthocyanin-starch interactions on anthocyanin stability, bioavailability and antioxidant activity and on starch crystallinity, gelatinization properties, mechanical properties and digestibility are reviewed, and the current progress in the application of anthocyanin-starch complexes is outlined. It is hoped that this review will provide a reference for future research on the preparation and application of anthocyanin-starch complexes

Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation

Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly focus on model-based adaptation in a self-training manner, such as predicting pseudo labels for new domain datasets. Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions. Motivated by the prompt learning in NLP, in this paper, we propose to learn an image-layer visual domain prompt for target domains while having the source model parameters frozen. During testing, the changing target datasets can be adapted to the source model by reformulating the input data with the learned visual prompts. Specifically, we devise two types of prompts, i.e., domains-specific prompts and domains-agnostic prompts, to extract current domain knowledge and maintain the domain-shared knowledge in the continual adaptation. Furthermore, we design a homeostasis-based adaptation strategy to suppress domain-sensitive parameters in domain-invariant prompts to learn domain-shared knowledge more effectively. This transition from the model-dependent paradigm to the model-free one enables us to bypass the catastrophic forgetting and error accumulation problems. Experiments show that our proposed method achieves significant performance gains over state-of-the-art methods on four widely-used benchmarks, including CIFAR-10C, CIFAR-100C, ImageNet-C, and VLCS datasets

Eco-Environment Quality Response to Climate Change and Human Activities on the Loess Plateau, China

Climate change and human activities have caused a range of impacts on the ecological environment. The Loess Plateau (LP) is critical to the stability and health of ecosystems in central and western China, but there is still a lack of research on spatial and temporal heterogeneity in the effects of climate and human activities on the EEQ of the LP. We quantified the ecological environment quality of the study area from 2001 to 2019 based on the improved remote sensing ecological index (RSEI-2) and studied the spatial and temporal evolution of EEQ and its drivers during this period by trend analysis and multiscale geographic weighted regression (MGWR) model. The EEQ of the LP showed an increasingly slowing trend during 2001–2019, with apparent spatial heterogeneity, the south-central part was the hot spot area of change, and the center of gravity of change shifted 124.56 km to the southwest. The driving effects and ranges of each factor changed over time during the study period, and the positive effects of precipitation (PRE) and temperature (TEM) on the EEQ of the southern LP became more apparent, but the negative effects of TEM on the northwestern part have expanded. The negative effect of the intensity of land utilization (LUI) has increased from north to south and has the most profound impact, while population growth has less impact on the central region. The results of this research indicate that the execution of the Grain to Green Program (GGP) in the LP over the last two decades has been effective, but more attention should be paid to the maintenance of the restoration effect in the central region and the reasonable development of the land in the southern area. This research can enhance the comprehension of alterations in ecological factors that impact the environment of the LP. Additionally, it serves as a foundation for investigating strategies for ecological preservation and sustainable land development