变压器被认为是自2018年以来最重要的深度学习模型之一，部分原因是它建立了最先进的记录（SOTA）记录，并有可能取代现有的深神经网络（DNNS）。尽管取得了显着的胜利，但变压器模型的延长周转时间是公认的障碍。序列长度的多样性施加了其他计算开销，其中需要将输入零填充到批处理中的最大句子长度，以容纳并行计算平台。本文针对现场可编程的门阵列（FPGA），并提出了一个连贯的序列长度自适应算法 - 硬件与变压器加速度的共同设计。特别是，我们开发了一个适合硬件的稀疏注意操作员和长度意识的硬件资源调度算法。提出的稀疏注意操作员将基于注意力的模型的复杂性降低到线性复杂性，并减轻片外记忆流量。提出的长度感知资源硬件调度算法动态分配了硬件资源以填充管道插槽并消除了NLP任务的气泡。实验表明，与CPU和GPU实施相比，我们的设计准确度损失很小，并且具有80.2 $ \ times $和2.6 $ \ times $速度，并且比先进的GPU加速器高4 $ \ times $ $ $ \ times $通过Cublas Gemm优化。

使用毫米波（MMWAVE）信号的人类手势识别提供有吸引力的应用，包括智能家居和车载界面。虽然现有的作品在受控设置下实现有前途的性能，但实际应用仍然有限，因为需要密集数据收集，适应新域时的额外培训努力（即环境，人员和地点）和实时识别的表现不佳。在本文中，我们提出了Di-Gesture，一个独立于域和实时MMWAVE手势识别系统。具体地，我们首先导出与具有空间时间处理的人体手势对应的信号变化。为了增强系统的稳健性并减少数据收集工作，我们根据信号模式与手势变化之间的相关性设计数据增强框架。此外，我们提出了一种动态窗口机制来自动且准确地执行手势分割，从而能够实时识别。最后，我们建立了一种轻量级神经网络，以从用于手势分类的数据中提取空间信息。广泛的实验结果表明，Di-Gesture分别为新用户，环境和地点的平均精度为97.92％，99.18％和98.76％。在实时场景中，Di-Gesutre的准确性达到97％以上，平均推断时间为2.87ms，这表明了我们系统的优越稳健性和有效性。

Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on substantial 1D sequences have shown burgeoning capabilities with scale in a broad range of applications. Nevertheless, no preceding studies consider combining these different protein modalities to promote the representation power of geometric neural networks. To address this gap, we make the foremost step to integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks. Experiments are evaluated on a variety of protein representation learning benchmarks, including protein-protein interface prediction, model quality assessment, protein-protein rigid-body docking, and binding affinity prediction, leading to an overall improvement of 20% over baselines and the new state-of-the-art performance. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin and can be generalized to complex tasks.

Existing 3D scene stylization methods employ an arbitrary style reference to transfer textures and colors as styles without establishing meaningful semantic correspondences. We present Reference-Based Non-Photorealistic Radiance Fields, i.e., Ref-NPR. It is a controllable scene stylization method utilizing radiance fields to stylize a 3D scene, with a single stylized 2D view taken as reference. To achieve decent results, we propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views, and exploit the semantic correspondence in content images to fill occluded regions with perceptually similar styles. Combining these operations, Ref-NPR generates non-photorealistic and continuous novel view sequences with a single reference while obtaining reasonable stylization in occluded regions. Experiments show that Ref-NPR significantly outperforms other scene and video stylization methods in terms of both visual quality and semantic correspondence. Code and data will be made publicly available.

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.