In this paper, we present the Circular Accessible Depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multi-frame point cloud fusion module, Stability-Attention Module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semi-supervised learning for the CADNet, and thus desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.

The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them. While existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, they are insufficient for diagnosing whether metrics are: 1) consistent, i.e., decrease as errors are introduced into a summary, 2) effective on human-written texts, and 3) sensitive to different error types (as summaries can contain multiple errors). To address these needs, we present a benchmark of unfaithful minimal pairs (BUMP), a dataset of 889 human-written, minimally different summary pairs, where a single error (from an ontology of 7 types) is introduced to a summary from the CNN/DailyMail dataset to produce an unfaithful summary. We find BUMP complements existing benchmarks in a number of ways: 1) the summaries in BUMP are harder to discriminate and less probable under SOTA summarization models, 2) BUMP enables measuring the consistency of metrics, and reveals that the most discriminative metrics tend not to be the most consistent, 3) BUMP enables the measurement of metrics' performance on individual error types and highlights areas of weakness for future work.

U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and thus ignore privacy issues. To address the privacy concerns, federated learning (FL) and split learning (SL) have attracted increasing attention. However, it is hard for both FL and SL to balance the local computational cost, model privacy and parallel training simultaneously. To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL. Previous works cannot preserve the data privacy, including the input, model parameters, label and output simultaneously. To effectively deal with all of them, we design a novel splitting method for U-shaped medical image networks, which splits the network into three parts hosted by different parties. Besides, the distributed learning methods usually suffer from a drift between local and global models caused by data heterogeneity. Based on this consideration, we propose a dynamic weight correction strategy (\textbf{DWCS}) to stabilize the training process and avoid model drift. Specifically, a weight correction loss is designed to quantify the drift between the models from two adjacent communication rounds. By minimizing this loss, a correction model is obtained. Then we treat the weighted sum of correction model and final round models as the result. The effectiveness of the proposed RoS-FL is supported by extensive experimental results on different tasks. Related codes will be released at https://github.com/Zi-YuanYang/RoS-FL.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

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.

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.

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

通用域的适应性（UNIDA）旨在将公共类的知识从源域转移到目标域，而无需对标签集的任何先验知识，这需要将未知样本与目标域中的已知样本区分开。就像传统的无监督域适应问题一样，由于偏见和歧视性较低的嵌入，两个域之间的错位也存在。最新方法提出了通过将目标样品与最近的邻居或原型聚类来完成域未对准的方法。但是，这样做是很危险的，因为我们对未知样本的分布没有任何先验知识，这些样本可以放大错位，尤其是当未知集很大的时候。同时，其他现有基于分类器的方法可以轻松地产生对未知样本的过度自信预测，因为在源域中有监督的目标导致整个模型偏向于目标域中的共同类别。因此，我们提出了一种新型的非参数未知样品检测方法，基于将原始特征空间中的样品映射到可靠的线性子空间中，这使数据点更稀疏，以减少未知样品和源样本之间的不对准。此外，与最近应用额外参数以改善未知样品分类的方法不同，本文通过未知的自适应保证金损失可以很好地平衡已知样品和未知样品的置信值，从而可以控制分类器学习的梯度在有监督的来源上的梯度更新样品取决于当前步骤中检测到的未知样品的置信度。最后，在四个公共数据集上的实验表明，我们的方法显着胜过现有的最新方法。

我们提出了Patron，这是一种新方法，它使用基于及时的不确定性估计，用于在冷启动场景下进行预训练的语言模型进行微调的数据选择，即，没有初始标记的数据可用。在顾客中，我们设计（1）一种基于迅速的不确定性传播方法来估计数据点的重要性和（2）分区 - 然后 - 剥离（PTR）策略，以促进对注释的样品多样性。六个文本分类数据集的实验表明，赞助人的表现优于最强的冷启动数据选择基准，高达6.9％。此外，仅具有128个标签，顾客分别基于香草微调和及时的学习，获得了91.0％和92.1％的全面监督性能。我们的赞助人实施可在\ url {https://github.com/yueyu1030/patron}上获得。

在高光谱图像分类（HSI）任务中，忽略了包括有关土地覆盖类别的大量先验知识在内的文本信息。有必要探索语言模式在协助HSI分类方面的有效性。此外，大规模训练的图像文本基础模型在各种下游应用中都表现出了出色的性能，包括零拍传输。但是，大多数领域的概括方法从未解决过采矿语言模态知识以提高模型的概括性能。为了弥补上述不足的不足，提出了一个语言感知的域概括网络（LDGNET），以从跨域共享的先验知识中学习跨域不变的表示。所提出的方法仅在源域（SD）上训练，然后将模型传输到目标域（TD）。包括图像编码器和文本编码器在内的双流架构用于提取视觉和语言特征，其中粗粒和细粒度的文本表示旨在提取两个层次的语言特征。此外，语言特征被用作跨域共享的语义空间，并且通过在语义空间中的对比度学习完成视觉语言对齐。与最先进的技术相比，三个数据集上的广泛实验证明了该方法的优越性。