Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-world applications, it is common and necessary to generate a series of coherent images for story-stelling. In this work, we mainly focus on story visualization and continuation tasks and propose AR-LDM, a latent diffusion model auto-regressively conditioned on history captions and generated images. Moreover, AR-LDM can generalize to new characters through adaptation. To our best knowledge, this is the first work successfully leveraging diffusion models for coherent visual story synthesizing. It also extends the text-conditioned method to multimodal conditioning. Quantitative results show that AR-LDM achieves SoTA FID scores on PororoSV, FlintstonesSV, and the adopted challenging dataset VIST containing natural images. Large-scale human evaluations show that AR-LDM has superior performance in terms of quality, relevance, and consistency. Code available at this https URL

Video Temporal Grounding (VTG) aims to locate the time interval in a video that is semantically relevant to a language query. Existing VTG methods interact the query with entangled video features and treat the instances in a dataset independently. However, intra-video entanglement and inter-video connection are rarely considered in these methods, leading to mismatches between the video and language. To this end, we propose a novel method, dubbed Hierarchically Semantic Associating (HiSA), which aims to precisely align the video with language and obtain discriminative representation for further location regression. Specifically, the action factors and background factors are disentangled from adjacent video segments, enforcing precise multimodal interaction and alleviating the intra-video entanglement. In addition, cross-guided contrast is elaborately framed to capture the inter-video connection, which benefits the multimodal understanding to locate the time interval. Extensive experiments on three benchmark datasets demonstrate that our approach significantly outperforms the state-of-the-art methods. The project page is available at: https://github.com/zhexu1997/HiSA .

Fast Adversarial Training (FAT) not only improves the model robustness but also reduces the training cost of standard adversarial training. However, fast adversarial training often suffers from Catastrophic Overfitting (CO), which results in poor robustness performance. Catastrophic Overfitting describes the phenomenon of a sudden and significant decrease in robust accuracy during the training of fast adversarial training. Many effective techniques have been developed to prevent Catastrophic Overfitting and improve the model robustness from different perspectives. However, these techniques adopt inconsistent training settings and require different training costs, i.e., training time and memory costs, leading to unfair comparisons. In this paper, we conduct a comprehensive study of over 10 fast adversarial training methods in terms of adversarial robustness and training costs. We revisit the effectiveness and efficiency of fast adversarial training techniques in preventing Catastrophic Overfitting from the perspective of model local nonlinearity and propose an effective Lipschitz regularization method for fast adversarial training. Furthermore, we explore the effect of data augmentation and weight averaging in fast adversarial training and propose a simple yet effective auto weight averaging method to improve robustness further. By assembling these techniques, we propose an effective FGSM-based fast adversarial training method equipped with Lipschitz regularization and Auto Weight averaging, abbreviated as FGSM-LAW. Experimental evaluations on four benchmark databases demonstrate the superiority of our method over state-of-the-art fast adversarial training methods and the advanced standard adversarial training methods.

Generative large language models (LLMs) have revolutionized natural language processing with their transformative and emergent capabilities. However, recent evidence indicates that LLMs can produce harmful content that violates social norms, raising significant concerns regarding the safety and ethical ramifications of deploying these advanced models. Thus, it is both critical and imperative to perform a rigorous and comprehensive safety evaluation of LLMs before deployment. Despite this need, owing to the extensiveness of LLM generation space, it still lacks a unified and standardized risk taxonomy to systematically reflect the LLM content safety, as well as automated safety assessment techniques to explore the potential risks efficiently. To bridge the striking gap, we propose S-Eval, a novel LLM-based automated Safety Evaluation framework with a newly defined comprehensive risk taxonomy. S-Eval incorporates two key components, i.e., an expert testing LLM M _t and a novel safety critique LLM M _c . The expert testing LLM M _t is responsible for automatically generating test cases in accordance with the proposed risk management (including 8 risk dimensions and a total of 102 subdivided risks). The safety critique LLM M _c can provide quantitative and explainable safety evaluations for better risk awareness of LLMs. In contrast to prior works, S-Eval differs in significant ways: (i) efficient – we construct a multi-dimensional and open-ended benchmark comprising 220,000 test cases across 102 risks utilizing M _t and conduct safety evaluations for 21 influential LLMs via M _c on our benchmark. The entire process is fully automated and requires no human involvement. (ii) effective – extensive validations show S-Eval facilitates a more thorough assessment and better perception of potential LLM risks, and M _c not only accurately quantifies the risks of LLMs but also provides explainable and in-depth insights into their safety, surpassing comparable models such as LLaMA-Guard-2. (iii) adaptive – S-Eval can be flexibly configured and adapted to the rapid evolution of LLMs and accompanying new safety threats, test generation methods and safety critique methods thanks to the LLM-based architecture. We further study the impact of hyper-parameters and language environments on model safety, which may lead to promising directions for future research. S-Eval has been deployed in our industrial partner for the automated safety evaluation of multiple LLMs serving millions of users, demonstrating its effectiveness in real-world scenarios.