Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations (or ``chain-of-thought'' (CoT)) for in-context learning. On the other hand, these reasoning tasks are usually presumed to be more approachable for symbolic programming. To make progress towards understanding in-context learning, we curate synthetic datasets containing equivalent (natural, symbolic) data pairs, where symbolic examples contain first-order logic rules and predicates from knowledge bases (KBs). Then we revisit neuro-symbolic approaches and use Language Models as Logic Programmer (LMLP) that learns from demonstrations containing logic rules and corresponding examples to iteratively reason over KBs, recovering Prolog's backward chaining algorithm. Comprehensive experiments are included to systematically compare LMLP with CoT in deductive reasoning settings, showing that LMLP enjoys more than 25% higher accuracy than CoT on length generalization benchmarks even with fewer parameters.

学习域不变的表示已成为域适应/概括的最受欢迎的方法之一。在本文中，我们表明不变的表示可能不足以保证良好的概括，在考虑标签函数转移的情况下。受到这一点的启发，我们首先在经验风险上获得了新的概括上限，该概括风险明确考虑了标签函数移动。然后，我们提出了特定领域的风险最小化（DRM），该风险最小化（DRM）可以分别对不同域的分布移动进行建模，并为目标域选择最合适的域。对四个流行的域概括数据集（CMNIST，PACS，VLCS和域）进行了广泛的实验，证明了所提出的DRM对域泛化的有效性，具有以下优点：1）它的表现明显超过了竞争性盆地的表现； 2）与香草经验风险最小化（ERM）相比，所有训练领域都可以在所有训练领域中具有可比性或优越的精度； 3）在培训期间，它仍然非常简单和高效，4）与不变的学习方法是互补的。

机器学习模型的基本挑战是由于杂散的相关性部分地推广到分销（OOD）数据。为了解决这一挑战，我们首先将“ood泛化问题”正式形式化为受限制的优化，称为解剖学限制域泛化（DDG）。我们以有限维参数化和经验逼近的方式将该非普通约束优化放宽到贸易形式。然后，提供了对上述变换偏离原始问题的程度的理论分析。基于转型，我们提出了一种用于联合表示解剖和域泛化的原始双向算法。与基于领域对抗性培训和域标签的传统方法形成鲜明对比，DDG共同学习解剖学的语义和变化编码器，使灵活的操纵和增强训练数据。 DDG旨在学习语义概念的内在表示，这些概念不变于滋扰因素，并遍布不同的域。对流行基准的综合实验表明，DDG可以实现竞争性的ood性能，并在数据中揭示可解释的突出结构。

更广泛的人重新识别（Reid）在最近的计算机视觉社区中引起了不断的关注。在这项工作中，我们在身份标签，特定特定因素（衣服/鞋子颜色等）和域特定因素（背景，观点等）之间构建结构因果模型。根据因果分析，我们提出了一种新颖的域不变表示，以获得概括的人重新识别（DIR-REID）框架。具体而言，我们首先建议解散特定于特定的和域特定的特征空间，我们提出了一种有效的算法实现，用于后台调整，基本上是朝向SCM的因果干预。已经进行了广泛的实验，表明Dir-Reid在大规模域泛化Reid基准上表现出最先进的方法。

在对象检测中，边界框回归（BBR）是决定对象定位性能的关键步骤。但是，我们发现BBR的大多数先前的损失功能都有两个主要缺点：（i）$ \ ell_n $ -norm和IOU基于IOU的损失功能都无法效率地描述BBR的目标，这会导致收敛速度缓慢和不准确的回归结果。 。 （ii）大多数损失函数都忽略了BBR中的不平衡问题，即与目标盒有较小重叠的大量锚盒对BBR的优化有最大的影响。为了减轻造成的不利影响，我们进行了彻底的研究，以利用本文中BBR损失的潜力。首先，提出了有关联合（EIOU）损失的有效交集，该交集明确测量了BBR中三个几何因素的差异，即重叠面积，中心点和侧面长度。之后，我们说明有效的示例挖掘（EEM）问题，并提出了焦点损失的回归版本，以使回归过程集中在高质量的锚点上。最后，将上述两个部分组合在一起以获得新的损失函数，即焦点损失。对合成数据集和真实数据集进行了广泛的实验。与其他BBR损失相比，在收敛速度和定位精度上都可以显着优势。

预测野火蔓延对于土地管理和灾害准备至关重要。为此，我们呈现“第二天野火蔓延，”一种策划，大规模的多变量数据集，历史野火的历史野火占据了美国近十年的遥感数据。与基于地球观测卫星的现有火灾数据集相比，我们的数据集合了2D解释性变量（例如，地形，植被，天气，干旱指数，人口密度）与2D区域对齐，提供了丰富的数据为机器学习设置。为了演示该数据集的有用性，我们实现了一个卷积的AutoEncoder，它利用了该数据的空间信息来预测野火扩散。我们将神经网络与其他机器学习模型的性能进行比较：Logistic回归和随机林。该数据集可以用作基于遥感数据开发野火传播模型的基准，以便有一天的提前期。

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.