对话中的多模式情绪识别（MERC）是自然语言处理（NLP）的一个积极研究主题，旨在预测人类在多种方式的交流中，e，g。，自然语言和面部手势的情绪状态。无数的隐式偏见和先入为主填补了人类的语言和对话，导致了当前数据驱动的MERC方法是否会产生偏见错误的问题。例如，这种方法可能比男性提供更高的情感分数。此外，现有的DEBIA模型主要集中在性别或种族上，在这种性别或种族中，缓解多重次数仍然是MERC中未开发的任务。在这项工作中，我们采取了第一步来解决这些问题，提出一系列方法来减轻文本话语（即性别，年龄，种族，宗教和LGBTQ+）和视觉表现（即性别和性别和性别和性别和性别和性别）的五种典型偏见。年龄），随后是富含双模式变压器（MMKET）的多次减少和情感知识。全面的实验结果表明了提出的模型的有效性，并证明了Debias操作对MERC的分类性能有很大的影响。我们希望我们的研究将有益于MERC和相关情绪研究中缓解偏见的发展。

仅使用图像级标签的弱监督语义细分旨在降低分割任务的注释成本。现有方法通常利用类激活图（CAM）来定位伪标签生成的对象区域。但是，凸轮只能发现对象的最歧视部分，从而导致下像素级伪标签。为了解决这个问题，我们提出了一个限制的显着性和内类关系的显着性（I $^2 $ CRC）框架，以协助CAM中激活的对象区域的扩展。具体而言，我们提出了一个显着性指导的类不足的距离模块，以通过将特征对准其类原型来更接近类别内特征。此外，我们提出了一个特定的距离模块，以将类间特征推开，并鼓励对象区域的激活高于背景。除了加强分类网络激活CAM中更多积分对象区域的能力外，我们还引入了一个对象引导的标签细化模块，以完全利用分割预测和初始标签，以获取出色的伪标签。 Pascal VOC 2012和可可数据集的广泛实验很好地证明了I $^2 $ CRC的有效性，而不是其他最先进的对应物。源代码，模型和数据已在\ url {https://github.com/nust-machine-intelligence-laboratory/i2crc}提供。

Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose Variational Graph Generator for Multi-View Graph Clustering (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then a simple yet effective graph encoder in conjunction with the multi-view clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of VGMGC by analyzing the uncertainty of the inferred consensus graph with information bottleneck principle. Extensive experiments demonstrate the superior performance of our VGMGC over SOTAs.

光流是一种易于构思和珍贵的提示，用于推进无监督的视频对象细分（UVOS）。以前的大多数方法直接提取并融合了在UVOS设置中分割目标对象的运动和外观特征。但是，光流本质上是连续帧之间所有像素的瞬时速度，因此使运动特征与相应帧之间的主要对象不太对齐。为了解决上述挑战，我们为外观和运动特征对齐方式提出了一个简洁，实用和有效的体系结构，称为层次特征对齐网络（HFAN）。具体而言，HFAN中的关键优点是顺序特征对齐（FAM）模块和特征适应（FAT）模块，这些模块被利用用于处理外观和运动特征。 FAM能够分别将外观和运动特征与主要对象语义表示分别对齐。此外，脂肪是针对外观和运动特征的自适应融合而显式设计的，以实现跨模式特征之间的理想权衡。广泛的实验证明了拟议的HFAN的有效性，该实验在Davis-16上达到了新的最新性能，达到88.7 $ \ MATHCAL {J} \＆\ MATHCAL {F} $，即相对改进，即相对改进比最佳发布结果比3.5％。

最近，深层回归森林（如深）差异模型（DDMS），最近已经广泛研究了面部年龄估计，头部姿势估计，凝视估计等问题。这些问题部分是挑战，因为没有噪声和偏差的大量有效培训数据通常不可用。虽然通过学习更具歧视特征或重新重量样本来实现的一些进展，但我们认为更可取的是逐渐学习以歧视人类。然后，我们诉诸自行节奏的学习（SPL）。但是，出现了自然问题：可以自花奏的政权引导DDMS实现更强大，偏差的解决方案吗？ SPL的严重问题是通过这项工作首先讨论的，是倾向于加剧解决方案的偏差，特别是对于明显的不平衡数据。为此，本文提出了一种新的自定位范例，用于深鉴别模型，这根据与每个示例相关的产出似然和熵区分噪声和不足的例子，并从新的视角下解决SECT中的基本排名问题：公平性。此范例是根本的，可以轻松地与各种DDMS结合。在三个计算机视觉任务中进行了广泛的实验，例如面部年龄估计，头部姿态估计和凝视估计，证明了我们的范式的功效。据我们所知，我们的作品是SPL的文献中的第一篇论文，以为自我节奏政权建设的排名公平。

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.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.