In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods extract part features in an explicit manner, by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, a set of attributes and auxiliary losses are introduced to further improve the learning of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks.

Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.

This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows. Project page: https://3d-front-future.github.io/LighTNet .

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.

在本文中，我们设计了一个基于信息的多机器人来源，以寻求算法，其中一组移动传感器仅使用基于局部范围的测量值就本地化并移动靠近单个源。在算法中，移动传感器执行源标识/本地化以估计源位置；同时，他们移至新位置，以最大程度地提高有关传感器测量中包含的源的Fisher信息。在这样做的过程中，它们改善了源位置估计，并更靠近源。与传统的攀登算法相比，我们的算法在收敛速度方面具有优越性，在测量模型和信息指标的选择中是灵活的，并且对测量模型误差非常强大。此外，我们提供了算法的完全分布式版本，每个传感器都决定自己的动作，并且仅通过稀疏的通信网络与邻居共享信息。我们进行密集的仿真实验，以测试带有光传感器的小型地面车辆上的大规模系统和物理实验的算法，这表明在寻求光源方面取得了成功。

尽管人工智能（AI）在理解各个领域的分子方面取得了重大进展，但现有模型通常从单个分子模态中获得单个认知能力。由于分子知识的层次结构是深刻的，即使人类也从不同的方式中学习，包括直觉图和专业文本，以帮助他们的理解。受到这一点的启发，我们提出了一个分子多模式基础模型，该模型是从分子图及其语义相关的文本数据（从发表的科学引用索引论文中爬立）的。该AI模型代表了直接桥接分子图和自然语言的关键尝试。重要的是，通过捕获两种方式的特定和互补信息，我们提出的模型可以更好地掌握分子专业知识。实验结果表明，我们的模型不仅在诸如跨模式检索和分子标题之类的跨模式任务中表现出有希望的性能，而且还可以增强分子属性预测，并具有从自然语言描述中产生有意义的分子图的能力。我们认为，我们的模型将对跨生物学，化学，材料，环境和医学等学科的AI能力领域产生广泛的影响。

凝视估计是一种确定一个人在何处看着该人的脸的方法，是理解人类意图的宝贵线索。与其他计算机视觉领域类似，深度学习（DL）方法在凝视估计域中获得了认可。但是，凝视估计域中仍然存在凝视校准问题，从而阻止了现有方法进一步改善性能。一个有效的解决方案是直接预测两只人眼的差异信息，例如差异网络（DIFF-NN）。但是，此解决方案仅使用一个推理图像时会导致准确性丧失。我们提出了一个差异残差模型（DRNET）与新的损失函数相结合，以利用两个眼睛图像的差异信息。我们将差异信息视为辅助信息。我们主要使用两个公共数据集（1）mpiigaze和（2）Eyediap评估了提出的模型（DRNET）。仅考虑眼睛功能，DRNET分别使用Mpiigigaze和EyeDiap数据集以$ Angular-Error $为4.57和6.14的最先进的目光估计方法。此外，实验结果还表明，DRNET对噪声图像非常强大。

这项工作旨在使用带有动作查询的编码器框架（类似于DETR）来推进时间动作检测（TAD），该框架在对象检测中表现出了巨大的成功。但是，如果直接应用于TAD，该框架遇到了几个问题：解码器中争论之间关系的探索不足，由于培训样本数量有限，分类培训不足以及推断时不可靠的分类得分。为此，我们首先提出了解码器中的关系注意机制，该机制根据其关系来指导查询之间的注意力。此外，我们提出了两项​​损失，以促进和稳定行动分类的培训。最后，我们建议在推理时预测每个动作查询的本地化质量，以区分高质量的查询。所提出的命名React的方法在Thumos14上实现了最新性能，其计算成本比以前的方法低得多。此外，还进行了广泛的消融研究，以验证每个提出的组件的有效性。该代码可在https://github.com/sssste/reaeact上获得。

平均老师（MT）方案在半监督对象检测（SSOD）中被广泛采用。在MT中，通过手工制作的标签分配，采用了由教师的最终预测（例如，在无最大抑制（NMS）后处理之后）提供的稀疏伪标签（例如，在无最大抑制（NMS）后处理）。但是，稀疏到密集的范式使SSOD的管道复杂化，同时忽略了强大的直接，密集的教师监督。在本文中，我们试图直接利用教师的密集指导来监督学生培训，即密集至密集的范式。具体而言，我们建议逆NMS聚类（INC）和等级匹配（RM），以实例化密集的监督，而无需广泛使用的常规稀疏伪标签。 Inc带领学生像老师一样将候选箱子分组为NMS中的群集，这是通过学习在NMS过程中揭示的分组信息来实现的。在通过Inc获得了与教师相同的分组计划后，学生通过排名匹配进一步模仿了教师与聚类候选人的排名分配。借助拟议的Inc和RM，我们将密集的教师指导集成到半监督的对象检测（称为DTG-SSOD）中，成功地放弃了稀疏的伪标签，并在未标记的数据上提供了更有信息的学习。在可可基准上，我们的DTG-SSOD在各种标签率下实现了最先进的性能。例如，在10％的标签率下，DTG-SSOD将监督的基线从26.9提高到35.9地图，使以前的最佳方法软教师的表现优于1.9分。