ManiCLIP: Multi-Attribute Face Manipulation from Text

In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimization for each individual image or are restricted to single attribute editing. Extending these methods to multi-attribute face image editing scenarios will introduce undesired excessive attribute change, e.g., text-relevant attributes are overly manipulated and text-irrelevant attributes are also changed. In order to address these challenges and achieve natural editing over multiple face attributes, we propose a new decoupling training scheme where we use group sampling to get text segments from same attribute categories, instead of whole complex sentences. Further, to preserve other existing face attributes, we encourage the model to edit the latent code of each attribute separately via an entropy constraint. During the inference phase, our model is able to edit new face images without any test-time optimization, even from complex textual prompts. We show extensive experiments and analysis to demonstrate the efficacy of our method, which generates natural manipulated faces with minimal text-irrelevant attribute editing. Code and pre-trained model will be released

Universal Instance Perception as Object Discovery and Retrieval

All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks. In this work, we present a universal instance perception model of the next generation, termed UNINEXT. UNINEXT reformulates diverse instance perception tasks into a unified object discovery and retrieval paradigm and can flexibly perceive different types of objects by simply changing the input prompts. This unified formulation brings the following benefits: (1) enormous data from different tasks and label vocabularies can be exploited for jointly training general instance-level representations, which is especially beneficial for tasks lacking in training data. (2) the unified model is parameter-efficient and can save redundant computation when handling multiple tasks simultaneously. UNINEXT shows superior performance on 20 challenging benchmarks from 10 instance-level tasks including classical image-level tasks (object detection and instance segmentation), vision-and-language tasks (referring expression comprehension and segmentation), and six video-level object tracking tasks. Code is available at https://github.com/MasterBin-IIAU/UNINEXT.Comment: CVPR202

Slimmable Generative Adversarial Networks

Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models makes them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power. In this paper, we introduce slimmable GANs (SlimGANs), which can flexibly switch the width of the generator to accommodate various quality-efficiency trade-offs at runtime. Specifically, we leverage multiple discriminators that share partial parameters to train the slimmable generator. To facilitate the \textit{consistency} between generators of different widths, we present a stepwise inplace distillation technique that encourages narrow generators to learn from wide ones. As for class-conditional generation, we propose a sliceable conditional batch normalization that incorporates the label information into different widths. Our methods are validated, both quantitatively and qualitatively, by extensive experiments and a detailed ablation study.Comment: Accepted to AAAI 202

Nutrient content of 122 kinds of retail handcrafted milk tea products in Shanghai

BackgroundThe retail milk tea industry is in a period of rapid development, but there is little research on its nutrient content, which restricts the nutritional guidance of milk tea. ObjectiveTo determine the levels of nutrients in best-selling handcrafted milk tea in Shanghai and analyze the nutritional characteristics. MethodsIn 2018 and 2021, a total of 13 handcrafted milk tea brands with ≥3 branch stores in Shanghai were selected by searching for milk tea on Meituan and Ele.me food delivery platforms, and a total of 122 types of handcrafted milk tea products were collected from the top three sales [milk tea (including all sweetness levels available), milk cover tea, and fruit tea]. National standard methods were used to detect energy, protein, fat, carbohydrate, sugar, trans fatty acid, calcium, caffeine, and tea polyphenol. ResultsThe median energy of the milk tea samples was 310 kJ (per 100 g sample). The main sources of energy were carbohydrate and fat. The levels of energy, protein, and fat in milk cover tea and milk tea were significantly higher than those in fruit tea (P＜0.05), and there was no significant difference in carbohydrate among them. The total sugar, fructose, and glucose levels in milk tea were significantly lower than those in milk cover tea and fruit tea, and the lactose level in fruit tea was significantly lower than those in milk tea and milk cover tea (P＜0.05). Themedian trans fat acid level in milk cover tea was higher than that in milk tea (P＜0.05). The median levels of caffeine and tea polyphenol were higher in milk tea than in milk cover tea (P＜0.05). The levels of energy, carbohydrate, sucrose, total sugar, and calcium in milk tea were positively correlated with the number of ingredients added (0-3) (r=0.386, 0.371, 0.238, 0.698, 0.466, respectively, P < 0.05). The levels of energy, carbohydrate, and total sugar tended to increase with increasing sweetness (P<0.05), and total sugar was mainly sucrose, followed by fructose and glucose. The total sugar levels of the samples labeled sugar free, light sugar, half sugar, less sugar, and regular sugar were 3.40 (2.20, 4.9), 4.97 (4.25, 5.97), 5.80 (4.31, 6.88), 6.59 (5.17, 8.53), and 7.96 (6.82, 9.20) g, respectively; the proportions of the samples containing more than 0.5 g of total sugar were 93.3% for sugar free milk tea, 47.4% for light sugar milk tea, and 94.0% for regular sugar milk tea; the proportion of the regular sugar samples with sugar content greater than 10 g was 18.0% (all samples with nominal sugar content were measured per 100 g). ConclusionThe retail handcrafted milk tea in Shanghai is characterized by high energy, high added sugar, high fat, and low protein. It is necessary to standardize the added sugar content and sweetness labeling, strengthen the nutrition education of milk tea, and guide residents to limit its intake

Complete chloroplast genomes of 11 Sabia samples: Genomic features, comparative analysis, and phylogenetic relationship

The genus Sabia is a woody climber belonging to the family Sabiaceae, order Proteales. Several species of this genus have been utilized as medicines for treating diseases, such as rheumatic arthritis, traumatism, hepatitis, etc. However, the lack of molecular data has prevented the accurate identification and refinement of taxonomic relationships in this genus. In this study, chloroplast genomes of 11 samples of the genus Sabia were assembled and analyzed. These chloroplast genomes showed a typical quadripartite structure and ranged in length from 160,956 to 162,209 bp. The structure of the genomes was found to be relatively conserved, with 130 genes annotated, including 85 coding genes, 37 tRNA genes, and eight rRNA genes. A total of 78–98 simple sequence repeats and 52–61 interspersed repeats were detected. Sequence alignment revealed 11 highly variable loci in chloroplast genomes. Among these loci, ndhF-ndhD achieved a remarkably higher resolution than the other regions. In addition, phylogenetic analysis indicated that Sect. Pachydiscus and Sect. Sabia of Sabia did not form two separate monophyletic groups. The divergence time calculated based on the Reltime method indicated that the evolutionary branches of Sabia and Meliosma started to form approximately 85.95 million years ago (Mya), and the species within Sabia began to diverge approximately 7.65 Mya. In conclusion, our study provides a basis for comprehensively exploring the phylogenetic relationships of Sabia. It also provides a methodological basis and data support for establishing a standardized and scientific identification system for this genus