Optimal Noise pursuit for Augmenting Text-to-Video Generation

Despite the remarkable progress in text-to-video generation, existing diffusion-based models often exhibit instability in terms of noise during inference. Specifically, when different noises are fed for the given text, these models produce videos that differ significantly in terms of both frame quality and temporal consistency. With this observation, we posit that there exists an optimal noise matched to each textual input; however, the widely adopted strategies of random noise sampling often fail to capture it. In this paper, we argue that the optimal noise can be approached through inverting the groundtruth video using the established noise-video mapping derived from the diffusion model. Nevertheless, the groundtruth video for the text prompt is not available during inference. To address this challenge, we propose to approximate the optimal noise via a search and inversion pipeline. Given a text prompt, we initially search for a video from a predefined candidate pool that closely relates to the text prompt. Subsequently, we invert the searched video into the noise space, which serves as an improved noise prompt for the textual input. In addition to addressing noise, we also observe that the text prompt with richer details often leads to higher-quality videos. Motivated by this, we further design a semantic-preserving rewriter to enrich the text prompt, where a reference-guided rewriting is devised for reasonable details compensation, and a denoising with a hybrid semantics strategy is proposed to preserve the semantic consistency. Extensive experiments on the WebVid-10M benchmark show that our proposed method can improve the text-to-video models with a clear margin, while introducing no optimization burden.

Towards Trustworthy Dataset Distillation

Efficiency and trustworthiness are two eternal pursuits when applying deep learning in real-world applications. With regard to efficiency, dataset distillation (DD) endeavors to reduce training costs by distilling the large dataset into a tiny synthetic dataset. However, existing methods merely concentrate on in-distribution (InD) classification in a closed-world setting, disregarding out-of-distribution (OOD) samples. On the other hand, OOD detection aims to enhance models' trustworthiness, which is always inefficiently achieved in full-data settings. For the first time, we simultaneously consider both issues and propose a novel paradigm called Trustworthy Dataset Distillation (TrustDD). By distilling both InD samples and outliers, the condensed datasets are capable to train models competent in both InD classification and OOD detection. To alleviate the requirement of real outlier data and make OOD detection more practical, we further propose to corrupt InD samples to generate pseudo-outliers and introduce Pseudo-Outlier Exposure (POE). Comprehensive experiments on various settings demonstrate the effectiveness of TrustDD, and the proposed POE surpasses state-of-the-art method Outlier Exposure (OE). Compared with the preceding DD, TrustDD is more trustworthy and applicable to real open-world scenarios. Our code will be publicly available.

Rethinking Pretraining as a Bridge from ANNs to SNNs

Spiking neural networks (SNNs) are known as a typical kind of brain-inspired models with their unique features of rich neuronal dynamics, diverse coding schemes and low power consumption properties. How to obtain a high-accuracy model has always been the main challenge in the field of SNN. Currently, there are two mainstream methods, i.e., obtaining a converted SNN through converting a well-trained Artificial Neural Network (ANN) to its SNN counterpart or training an SNN directly. However, the inference time of a converted SNN is too long, while SNN training is generally very costly and inefficient. In this work, a new SNN training paradigm is proposed by combining the concepts of the two different training methods with the help of the pretrain technique and BP-based deep SNN training mechanism. We believe that the proposed paradigm is a more efficient pipeline for training SNNs. The pipeline includes pipeS for static data transfer tasks and pipeD for dynamic data transfer tasks. SOTA results are obtained in a large-scale event-driven dataset ES-ImageNet. For training acceleration, we achieve the same (or higher) best accuracy as similar LIF-SNNs using 1/10 training time on ImageNet-1K and 2/5 training time on ES-ImageNet and also provide a time-accuracy benchmark for a new dataset ES-UCF101. These experimental results reveal the similarity of the functions of parameters between ANNs and SNNs and also demonstrate the various potential applications of this SNN training pipeline.