Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning

Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world scenarios, the number of input data and tasks is constantly changing in a statistical way, not a static way. Although recently introduced incremental learning scenarios having blurry task boundaries somewhat address the above issues, they still do not fully reflect the statistical properties of real-world situations because of the fixed ratio of disjoint and blurry samples. In this paper, we propose a new Stochastic incremental Blurry task boundary scenario, called Si-Blurry, which reflects the stochastic properties of the real-world. We find that there are two major challenges in the Si-Blurry scenario: (1) inter- and intra-task forgettings and (2) class imbalance problem. To alleviate them, we introduce Mask and Visual Prompt tuning (MVP). In MVP, to address the inter- and intra-task forgetting issues, we propose a novel instance-wise logit masking and contrastive visual prompt tuning loss. Both of them help our model discern the classes to be learned in the current batch. It results in consolidating the previous knowledge. In addition, to alleviate the class imbalance problem, we introduce a new gradient similarity-based focal loss and adaptive feature scaling to ease overfitting to the major classes and underfitting to the minor classes. Extensive experiments show that our proposed MVP significantly outperforms the existing state-of-the-art methods in our challenging Si-Blurry scenario

LFS-GAN: Lifelong Few-Shot Image Generation

We address a challenging lifelong few-shot image generation task for the first time. In this situation, a generative model learns a sequence of tasks using only a few samples per task. Consequently, the learned model encounters both catastrophic forgetting and overfitting problems at a time. Existing studies on lifelong GANs have proposed modulation-based methods to prevent catastrophic forgetting. However, they require considerable additional parameters and cannot generate high-fidelity and diverse images from limited data. On the other hand, the existing few-shot GANs suffer from severe catastrophic forgetting when learning multiple tasks. To alleviate these issues, we propose a framework called Lifelong Few-Shot GAN (LFS-GAN) that can generate high-quality and diverse images in lifelong few-shot image generation task. Our proposed framework learns each task using an efficient task-specific modulator - Learnable Factorized Tensor (LeFT). LeFT is rank-constrained and has a rich representation ability due to its unique reconstruction technique. Furthermore, we propose a novel mode seeking loss to improve the diversity of our model in low-data circumstances. Extensive experiments demonstrate that the proposed LFS-GAN can generate high-fidelity and diverse images without any forgetting and mode collapse in various domains, achieving state-of-the-art in lifelong few-shot image generation task. Surprisingly, we find that our LFS-GAN even outperforms the existing few-shot GANs in the few-shot image generation task. The code is available at Github.Comment: 20 pages, 19 figures, 14 tables, ICCV 2023 Poste

RADIO: Reference-Agnostic Dubbing Video Synthesis

One of the most challenging problems in audio-driven talking head generation is achieving high-fidelity detail while ensuring precise synchronization. Given only a single reference image, extracting meaningful identity attributes becomes even more challenging, often causing the network to mirror the facial and lip structures too closely. To address these issues, we introduce RADIO, a framework engineered to yield high-quality dubbed videos regardless of the pose or expression in reference images. The key is to modulate the decoder layers using latent space composed of audio and reference features. Additionally, we incorporate ViT blocks into the decoder to emphasize high-fidelity details, especially in the lip region. Our experimental results demonstrate that RADIO displays high synchronization without the loss of fidelity. Especially in harsh scenarios where the reference frame deviates significantly from the ground truth, our method outperforms state-of-the-art methods, highlighting its robustness. Pre-trained model and codes will be made public after the review.Comment: Under revie

Multi-dimensional histone methylations for coordinated regulation of gene expression under hypoxia

Hypoxia increases both active and repressive histone methylation levels via decreased activity of histone demethylases. However, how such increases coordinately regulate induction or repression of hypoxia-responsive genes is largely unknown. Here, we profiled active and repressive histone tri-methylations (H3K4me3, H3K9me3, and H3K27me3) and analyzed gene expression profiles in human adipocyte-derived stem cells under hypoxia. We identified differentially expressed genes (DEGs) and differentially methylated genes (DMGs) by hypoxia and clustered the DEGs and DMGs into four major groups. We found that each group of DEGs was predominantly associated with alterations in only one type among the three histone tri-methylations. Moreover, the four groups of DEGs were associated with different TFs and localization patterns of their predominant types of H3K4me3, H3K9me3 and H3K27me3. Our results suggest that the association of altered gene expression with prominent single-type histone tri-methylations characterized by different localization patterns and with different sets of TFs contributes to regulation of particular sets of genes, which can serve as a model for coordinated epigenetic regulation of gene expression under hypoxia.111Ysciescopu

Development of Anthropometry-Based Equations for the Estimation of the Total Body Water in Koreans

For developing race-specific anthropometry-based total body water (TBW) equations, we measured TBW using bioelectrical impedance analysis (TBWBIA) in 2,943 healthy Korean adults. Among them, 2,223 were used as a reference group. Two equations (TBWK1 and TBWK2) were developed based on age, sex, height, and body weight. The adjusted R2 was 0.908 for TBWK1 and 0.910 for TBWK2. The remaining 720 subjects were used for the validation of our results. Watson (TBWW) and Hume-Weyers (TBWH) formulas were also used. In men, TBWBIA showed the highest correlation with TBWH, followed by TBWK1, TBWK2 and TBWW. TBWK1 and TBWK2 showed the lower root mean square errors (RMSE) and mean prediction errors (ME) than TBWW and TBWH. On the Bland-Altman plot, the correlations between the differences and means were smaller for TBWK2 than for TBWK1. On the contrary, TBWBIA showed the highest correlation with TBWW, followed by TBWK2, TBWK1, and TBWH in females. RMSE was smallest in TBWW, followed by TBWK2, TBWK1 and TBWH. ME was closest to zero for TBWK2, followed by TBWK1, TBWW and TBWH. The correlation coefficients between the means and differences were highest in TBWW, and lowest in TBWK2. In conclusion, TBWK2 provides better accuracy with a smaller bias than the TBWW or TBWH in males. TBWK2 shows a similar accuracy, but with a smaller bias than TBWW in females

One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era

OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated numerous researchers to investigate ChatGPT from various aspects. According to Google scholar, there are more than 500 articles with ChatGPT in their titles or mentioning it in their abstracts. Considering this, a review is urgently needed, and our work fills this gap. Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges. Moreover, we present an outlook on how ChatGPT might evolve to realize general-purpose AIGC (a.k.a. AI-generated content), which will be a significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated ([email protected]

In Vivo Biocompatibility Study of Electrospun Chitosan Microfiber for Tissue Engineering

In this work, we examined the biocompatibility of electrospun chitosan microfibers as a scaffold. The chitosan microfibers showed a three-dimensional pore structure by SEM. The chitosan microfibers supported attachment and viability of rat muscle-derived stem cells (rMDSCs). Subcutaneous implantation of the chitosan microfibers demonstrated that implantation of rMDSCs containing chitosan microfibers induced lower host tissue responses with decreased macrophage accumulation than did the chitosan microfibers alone, probably due to the immunosuppression of the transplanted rMDSCs. Our results collectively show that chitosan microfibers could serve as a biocompatible in vivo scaffold for rMDSCs in rats