One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control. Existing works on adaptation to unknown environment contexts either assume the contexts are the same for the whole episode or assume the context variables are Markovian. However, in many real-world applications, the environment context usually stays stable for a stochastic period and then changes in an abrupt and unpredictable manner within an episode, resulting in a segment structure, which existing works fail to address. To leverage the segment structure of piecewise stable context in real-world applications, in this paper, we propose a \textit{\textbf{Se}gmented \textbf{C}ontext \textbf{B}elief \textbf{A}ugmented \textbf{D}eep~(SeCBAD)} RL method. Our method can jointly infer the belief distribution over latent context with the posterior over segment length and perform more accurate belief context inference with observed data within the current context segment. The inferred belief context can be leveraged to augment the state, leading to a policy that can adapt to abrupt variations in context. We demonstrate empirically that SeCBAD can infer context segment length accurately and outperform existing methods on a toy grid world environment and Mujuco tasks with piecewise-stable context.

The ability to create realistic, animatable and relightable head avatars from casual video sequences would open up wide ranging applications in communication and entertainment. Current methods either build on explicit 3D morphable meshes (3DMM) or exploit neural implicit representations. The former are limited by fixed topology, while the latter are non-trivial to deform and inefficient to render. Furthermore, existing approaches entangle lighting in the color estimation, thus they are limited in re-rendering the avatar in new environments. In contrast, we propose PointAvatar, a deformable point-based representation that disentangles the source color into intrinsic albedo and normal-dependent shading. We demonstrate that PointAvatar bridges the gap between existing mesh- and implicit representations, combining high-quality geometry and appearance with topological flexibility, ease of deformation and rendering efficiency. We show that our method is able to generate animatable 3D avatars using monocular videos from multiple sources including hand-held smartphones, laptop webcams and internet videos, achieving state-of-the-art quality in challenging cases where previous methods fail, e.g., thin hair strands, while being significantly more efficient in training than competing methods.

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

哈希（Hashing）将项目数据投入二进制代码已显示出由于其储存量低和高查询速度而显示出跨模式检索的非凡人才。尽管在某些情况下取得了经验成功，但现有的跨模式散列方法通常不存在带有大量标记信息的数据时跨模式差距跨模式差距。为了避免以分裂和纠纷策略的激励，我们提出了深层的歧管散列（DMH），这是一种新颖的方法，是将半分配的无监督的交叉模式检索分为三个子问题，并建立一个简单而又简单而又又有一个简单的方法每个子问题的效率模型。具体而言，第一个模型是通过基于多种学习的半生数据补充的半生数据来构建的，用于获得模态不变的特征，而第二个模型和第三个模型旨在分别学习哈希码和哈希功能。在三个基准上进行的广泛实验表明，与最先进的完全配对和半成本无监督的跨模式散列方法相比，我们的DMH的优势。

我们从一组未配对的清晰和朦胧的图像中提供了实用的基于学习的图像飞行网络。本文提供了一种新的观点，可以将图像除去作为两类分离的因子分离任务，即清晰图像重建的任务相关因素以及与雾霾相关的分布的任务含量。为了在深度特征空间中实现这两类因素的分离，将对比度学习引入了一个自行车框架中，以通过指导与潜在因素相关的生成的图像来学习分离的表示形式。通过这种表述，提出的对比度拆除的脱掩护方法（CDD-GAN）采用负面发电机与编码器网络合作以交替进行更新，以产生挑战性负面对手的队列。然后，这些负面的对手是端到端训练的，以及骨干代表网络，以通过最大化对抗性对比损失来增强歧视性信息并促进因素分离性能。在培训期间，我们进一步表明，硬性负面例子可以抑制任务 - 无关紧要的因素和未配对的清晰景象可以增强与任务相关的因素，以便更好地促进雾霾去除并帮助图像恢复。对合成和现实世界数据集的广泛实验表明，我们的方法对现有的未配对飞行基线的表现良好。

传统的变形面模型提供了对表达的细粒度控制，但不能轻易捕获几何和外观细节。神经体积表示方法是光学 - 现实主义，但很难动画，并没有概括到看不见的表达。为了解决这个问题，我们提出了iMavatar（隐式的可变头像），这是一种从单眼视频学习隐含头头像的新方法。灵感来自传统3DMMS提供的细粒度控制机制，我们代表了通过学习的闪打和剥皮领域的表达和与姿势相关的变形。这些属性是姿势独立的，可用于使规范几何形状和纹理字段变成新颖的表达和姿势参数。我们使用射线跟踪和迭代根发现来定位每个像素的规范表面交叉点。关键贡献是我们的新型分析梯度制定，可实现来自视频的imavatars的端到端培训。我们的定量和定性地显示了我们的方法改善了几何形状，并与最先进的方法相比，涵盖了更完整的表达空间。

神经隐式表面表示作为有希望以连续和独立的方式捕获3D形状的承诺范式。然而，将它们适应铰接形状是非微不足道的。现有方法学习落后的扭曲领域，即地图变形到规范点。然而，这是有问题的，因为后向扭曲字段依赖于姿势，因此需要大量数据来学习。为了解决这个问题，我们通过学习前向变形领域而没有直接监督，将多边形网格与神经隐式表面的线性混合皮肤（LBS）的优势相结合的Snarf。该变形场在规范，姿势独立的空间中定义，允许概括地看不见。学习从姿势网格中的变形字段独立地是具有挑战性，因为变形点的对应关系被隐含地定义，并且在拓扑的变化下可能不是唯一的。我们提出了一种前瞻性的剥皮模型，使用迭代根发现，找到任何变形点的所有规范对应关系。我们通过隐式差分派生分析梯度，从而实现从3D网格与骨骼变换的端到端训练。与最先进的神经隐式表示相比，我们的方法在保持准确性的同时，我们的方法更好地展示了未经造成的姿势。我们展示了我们在多样化和看不见的姿态上挑战（披装）3D人类的具有挑战性的方法。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.