在视频监视和时尚检索中，识别软性识别人行人属性至关重要。最近的作品在单个数据集上显示了有希望的结果。然而，这些方法在不同属性分布，观点，不同的照明和低分辨率下的概括能力很少因当前数据集中的强偏差和变化属性而很少被理解。为了缩小这一差距并支持系统的调查，我们介绍了UPAR，即统一的人属性识别数据集。它基于四个知名人士属性识别数据集：PA100K，PETA，RAPV2和Market1501。我们通过提供3300万个附加注释来统一这些数据集，以在整个数据集中统一40个属性类别的40个重要二进制属性。因此，我们首次对可概括的行人属性识别以及基于属性的人检索进行研究。由于图像分布，行人姿势，规模和遮挡的巨大差异，现有方法在准确性和效率方面都受到了极大的挑战。此外，我们基于对正则化方法的彻底分析，为基于PAR和属性的人检索开发了强大的基线。我们的模型在PA100K，PETA，RAPV2，Market1501-Atributes和UPAR上的跨域和专业设置中实现了最先进的性能。我们相信UPAR和我们的强大基线将为人工智能界做出贡献，并促进有关大规模，可推广属性识别系统的研究。

Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, for LLMs beyond 100 billion parameters, existing methods cannot maintain accuracy or do not run efficiently on hardware. We propose SmoothQuant, a training-free, accuracy-preserving, and general-purpose post-training quantization (PTQ) solution to enable 8-bit weight, 8-bit activation (W8A8) quantization for LLMs that can be implemented efficiently. We observe that systematic outliers appear at fixed activation channels. Based on the fact that weights are easy to quantize while activations are not, SmoothQuant smooths the activation outliers by offline migrating the quantization difficulty from activations to weights with a mathematically equivalent transformation. SmoothQuant enables an INT8 quantization of both weights and activations for all the GEMMs in LLMs, including OPT-175B, BLOOM-176B, and GLM-130B. SmoothQuant has better hardware efficiency than existing techniques using mixed-precision activation quantization or weight-only quantization. We demonstrate up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy. Thanks to the hardware-friendly design, we integrate SmoothQuant into FasterTransformer, a state-of-the-art LLM serving framework, and achieve faster inference speed with half the number of GPUs compared to FP16. Our work offers a turn-key solution that reduces hardware costs and democratizes LLMs. Code is available at: https://github.com/mit-han-lab/smoothquant.

Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community.

从文档数据中进行的多模式学习最近取得了巨大的成功，因为它允许将语义有意义的特征预先作为先验的特征，成为可学习的下游方法。在本文中，我们通过使用语言和视觉线索来学习跨模式的表示，考虑了内模式和模式间关系，我们解决了文档分类问题。该方法没有将不同模态的特征合并为一个共同表示空间，而是利用高级相互作用，并从跨模态内外的有效注意流中学习相关的语义信息。提出的学习目标是在内部和模式间比对任务之间设计的，其中每个任务的相似性分布是通过收缩阳性样品对计算的，同时在共同特征表示空间中同时对比}。公共文档分类数据集的广泛实验证明了我们模型对低规模和大规模数据集的有效性和概括能力。

In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource speech translation track, and they consist of collections of radio recordings from the Studio Kalangou (Niger) and Studio Tamani (Mali) daily broadcast news. We share (i) a massive amount of unlabeled audio data (671 hours) in five languages: French from Niger, Fulfulde, Hausa, Tamasheq and Zarma, and (ii) a smaller parallel corpus of audio recordings (17 hours) in Tamasheq, with utterance-level translations in the French language. All this data is shared under the Creative Commons BY-NC-ND 3.0 license. We hope these resources will inspire the speech community to develop and benchmark models using the Tamasheq language.