Image captioning models require the high-level generalization ability to describe the contents of various images in words. Most existing approaches treat the image-caption pairs equally in their training without considering the differences in their learning difficulties. Several image captioning approaches introduce curriculum learning methods that present training data with increasing levels of difficulty. However, their difficulty measurements are either based on domain-specific features or prior model training. In this paper, we propose a simple yet efficient difficulty measurement for image captioning using cross-modal similarity calculated by a pretrained vision-language model. Experiments on the COCO and Flickr30k datasets show that our proposed approach achieves superior performance and competitive convergence speed to baselines without requiring heuristics or incurring additional training costs. Moreover, the higher model performance on difficult examples and unseen data also demonstrates the generalization ability.

Digitization of scanned receipts aims to extract text from receipt images and save it into structured documents. This is usually split into two sub-tasks: text localization and optical character recognition (OCR). Most existing OCR models only focus on the cropped text instance images, which require the bounding box information provided by a text region detection model. Introducing an additional detector to identify the text instance images in advance is inefficient, however instance-level OCR models have very low accuracy when processing the whole image for the document-level OCR, such as receipt images containing multiple text lines arranged in various layouts. To this end, we propose a localization-free document-level OCR model for transcribing all the characters in a receipt image into an ordered sequence end-to-end. Specifically, we finetune the pretrained Transformer-based instance-level model TrOCR with randomly cropped image chunks, and gradually increase the image chunk size to generalize the recognition ability from instance images to full-page images. In our experiments on the SROIE receipt OCR dataset, the model finetuned with our strategy achieved 64.4 F1-score and a 22.8% character error rates (CER) on the word-level and character-level metrics, respectively, which outperforms the baseline results with 48.5 F1-score and 50.6% CER. The best model, which splits the full image into 15 equally sized chunks, gives 87.8 F1-score and 4.98% CER with minimal additional pre or post-processing of the output. Moreover, the characters in the generated document-level sequences are arranged in the reading order, which is practical for real-world applications.

脑MRI图像的登记需要解决变形领域，这对于对准复杂的脑组织，例如皮质核等，这是极其困难的现有努力，该努力在具有微小运动的中间子场中分解目标变形领域，即逐步登记阶段或较低的分辨率，即全尺寸变形场的粗析估计。在本文中，我们认为这些努力不是相互排斥的，并为普通和粗良好的方式同时提出统一的脑MRI登记统一框架。具体地，在双编码器U-Net上构建，定制移动的MRI对被编码和解码成从粗略到精细的多尺度变形子字段。每个解码块包含两个提出的新颖模块：i）在变形场积分（DFI）中，计算单个集成子字段，翘曲，其等同于来自所有先前解码块的子字段逐渐翘曲，并且II）非刚性特征融合（NFF），固定移动对的特征由DFI集成子场对齐，然后融合以预测更精细的子场。利用DFI和NFF，目标变形字段被修改为多尺度子场，其中较粗糙的字段缓解了更精细的一个和更精细的字段的估计，以便构成以前粗糙的较粗糙的那些错位。私人和公共数据集的广泛和全面的实验结果展示了脑MRI图像的优越的登记性能，仅限于逐步登记和粗略估计，平均骰子的粗略估计数量在最多8％上升。

元强化学习（META-RL）是一种有前途的方法，使代理商能够快速学习新任务。但是，由于仅由奖励提供的任务信息不足，大多数元元素算法在多任任务方案中显示出较差的概括。语言条件的元RL通过匹配语言指令和代理的行为来改善概括。因此，从对称性学习是人类学习的一种重要形式，因此将对称性和语言指令结合到元素rl可以帮助提高算法的概括和学习效率。因此，我们提出了一种双MDP元提升学习方法，该方法可以通过对称数据和语言指令有效地学习新任务。我们在多个具有挑战性的操作任务中评估了我们的方法，实验结果表明我们的方法可以大大提高元强化学习的概括和效率。

许多现实世界图包含时域信息。时间图神经网络在生成的动态节点嵌入中捕获时间信息以及结构和上下文信息。研究人员表明，这些嵌入在许多不同的任务中实现了最先进的表现。在这项工作中，我们提出了TGL，这是一个用于大规模脱机时间图神经网络训练的统一框架，用户可以使用简单的配置文件组成各种时间图神经网络。 TGL包括五个主要组件，一个临时采样器，一个邮箱，节点内存模块，存储器更新程序和消息传递引擎。我们设计了临时CSR数据结构和平行采样器，以有效地对颞邻邻居进行制作微型批次。我们提出了一种新颖的随机块调度技术，该技术可以减轻大批量训练时过时的节点存储器的问题。为了解决仅在小规模数据集上评估当前TGNN的局限性，我们介绍了两个具有0.2亿和13亿个时间边缘的大型现实世界数据集。我们在四个具有单个GPU的小规模数据集上评估了TGL的性能，以及两个具有多个GPU的大数据集，用于链接预测和节点分类任务。我们将TGL与五种方法的开源代码进行了比较，并表明TGL平均达到13倍的速度可实现相似或更高的精度。与基准相比，我们的时间平行采样器在多核CPU上平均达到173倍加速。在4-GPU机器上，TGL可以在1-10小时内训练一个超过10亿个时间边缘的时期。据我们所知，这是第一项提出了一个关于多个GPU的大规模时间图神经网络培训的一般框架的工作。

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).

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.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.