与人类相比，即使是最先进的深度学习模型也缺乏基本能力。已经提出了多重比较范例来探索人类与深度学习之间的区别。尽管大多数比较都取决于受数学转变启发的腐败，但很少有人在人类认知现象上具有基础。在这项研究中，我们提出了一种基于毗邻的光栅幻觉的新型腐败方法，这是在人类和广泛的动物物种中广泛发现的视觉现象。腐败方法破坏了梯度定义的边界，并使用彼此毗邻的线光栅产生了虚幻轮廓的感知。我们应用了MNIST，高分辨率MNIST和Silhouette对象图像的方法。对腐败的各种深度学习模型进行了测试，包括从头开始训练的模型和通过ImageNet或各种数据增强技术预测的109个模型。我们的结果表明，即使对于最先进的深度学习模型，将光栅腐败毗邻也是挑战性的，因为大多数模型都是随机猜测的。我们还发现，深度指示技术可以极大地改善固定光栅幻觉的鲁棒性。早期层的可视化表明，更好的性能模型表现出更强的终端特性，这与神经科学发现一致。为了验证腐败方法，涉及24名人类受试者以对损坏数据集进行分类。

这项工作为聚类提供了无监督的深入判别分析。该方法基于深层神经网络，旨在最大程度地减少群集内差异，并以无监督的方式最大化集群间差异。该方法能够将数据投射到具有紧凑和不同分布模式的非线性低维潜在空间中，以便可以有效地识别数据簇。我们进一步提供了该方法的扩展，以便可以有效利用可用的图形信息来提高聚类性能。带有或没有图形信息的图像和非图像数据的广泛数值结果证明了所提出的方法的有效性。

A polarization camera has great potential for 3D reconstruction since the angle of polarization (AoP) and the degree of polarization (DoP) of reflected light are related to an object's surface normal. In this paper, we propose a novel 3D reconstruction method called Polarimetric Multi-View Inverse Rendering (Polarimetric MVIR) that effectively exploits geometric, photometric, and polarimetric cues extracted from input multi-view color-polarization images. We first estimate camera poses and an initial 3D model by geometric reconstruction with a standard structure-from-motion and multi-view stereo pipeline. We then refine the initial model by optimizing photometric rendering errors and polarimetric errors using multi-view RGB, AoP, and DoP images, where we propose a novel polarimetric cost function that enables an effective constraint on the estimated surface normal of each vertex, while considering four possible ambiguous azimuth angles revealed from the AoP measurement. The weight for the polarimetric cost is effectively determined based on the DoP measurement, which is regarded as the reliability of polarimetric information. Experimental results using both synthetic and real data demonstrate that our Polarimetric MVIR can reconstruct a detailed 3D shape without assuming a specific surface material and lighting condition.

Neural transducer is now the most popular end-to-end model for speech recognition, due to its naturally streaming ability. However, it is challenging to adapt it with text-only data. Factorized neural transducer (FNT) model was proposed to mitigate this problem. The improved adaptation ability of FNT on text-only adaptation data came at the cost of lowered accuracy compared to the standard neural transducer model. We propose several methods to improve the performance of the FNT model. They are: adding CTC criterion during training, adding KL divergence loss during adaptation, using a pre-trained language model to seed the vocabulary predictor, and an efficient adaptation approach by interpolating the vocabulary predictor with the n-gram language model. A combination of these approaches results in a relative word-error-rate reduction of 9.48\% from the standard FNT model. Furthermore, n-gram interpolation with the vocabulary predictor improves the adaptation speed hugely with satisfactory adaptation performance.

Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the exploration of the SSL-based SS by massively scaling up both the pre-training data (more than 300K hours) and fine-tuning data (10K hours). We also investigate various techniques to efficiently integrate the pre-trained model with the SS network under a limited computation budget, including a low frame rate SSL model training setup and a fine-tuning scheme using only the part of the pre-trained model. Compared with a supervised baseline and the WavLM-based SS model using feature embeddings obtained with the previously released 94K hours trained WavLM, our proposed model obtains 15.9% and 11.2% of relative word error rate (WER) reductions, respectively, for a simulated far-field speech mixture test set. For conversation transcription on real meeting recordings using continuous speech separation, the proposed model achieves 6.8% and 10.6% of relative WER reductions over the purely supervised baseline on AMI and ICSI evaluation sets, respectively, while reducing the computational cost by 38%.

Automatic Speech Recognition (ASR) systems typically yield output in lexical form. However, humans prefer a written form output. To bridge this gap, ASR systems usually employ Inverse Text Normalization (ITN). In previous works, Weighted Finite State Transducers (WFST) have been employed to do ITN. WFSTs are nicely suited to this task but their size and run-time costs can make deployment on embedded applications challenging. In this paper, we describe the development of an on-device ITN system that is streaming, lightweight & accurate. At the core of our system is a streaming transformer tagger, that tags lexical tokens from ASR. The tag informs which ITN category might be applied, if at all. Following that, we apply an ITN-category-specific WFST, only on the tagged text, to reliably perform the ITN conversion. We show that the proposed ITN solution performs equivalent to strong baselines, while being significantly smaller in size and retaining customization capabilities.

With the development of depth sensors in recent years, RGBD object tracking has received significant attention. Compared with the traditional RGB object tracking, the addition of the depth modality can effectively solve the target and background interference. However, some existing RGBD trackers use the two modalities separately and thus some particularly useful shared information between them is ignored. On the other hand, some methods attempt to fuse the two modalities by treating them equally, resulting in the missing of modality-specific features. To tackle these limitations, we propose a novel Dual-fused Modality-aware Tracker (termed DMTracker) which aims to learn informative and discriminative representations of the target objects for robust RGBD tracking. The first fusion module focuses on extracting the shared information between modalities based on cross-modal attention. The second aims at integrating the RGB-specific and depth-specific information to enhance the fused features. By fusing both the modality-shared and modality-specific information in a modality-aware scheme, our DMTracker can learn discriminative representations in complex tracking scenes. Experiments show that our proposed tracker achieves very promising results on challenging RGBD benchmarks. Code is available at \url{https://github.com/ShangGaoG/DMTracker}.

End-to-end formulation of automatic speech recognition (ASR) and speech translation (ST) makes it easy to use a single model for both multilingual ASR and many-to-many ST. In this paper, we propose streaming language-agnostic multilingual speech recognition and translation using neural transducers (LAMASSU). To enable multilingual text generation in LAMASSU, we conduct a systematic comparison between specified and unified prediction and joint networks. We leverage a language-agnostic multilingual encoder that substantially outperforms shared encoders. To enhance LAMASSU, we propose to feed target LID to encoders. We also apply connectionist temporal classification regularization to transducer training. Experimental results show that LAMASSU not only drastically reduces the model size but also outperforms monolingual ASR and bilingual ST models.

In this paper, we introduce our work of building a Streaming Multilingual Speech Model (SM2), which can transcribe or translate multiple spoken languages into texts of the target language. The backbone of SM2 is Transformer Transducer, which has high streaming capability. Instead of human labeled speech translation (ST) data, SM2 models are trained using weakly supervised data generated by converting the transcriptions in speech recognition corpora with a machine translation service. With 351 thousand hours of anonymized speech training data from 25 languages, SM2 models achieve comparable or even better ST quality than some recent popular large-scale non-streaming speech models. More importantly, we show that SM2 has the truly zero-shot capability when expanding to new target languages, yielding high quality ST results for {source-speech, target-text} pairs that are not seen during training.

本文介绍了一个新型的流媒体自动语音识别（ASR）框架，用于由带有任意几何形状的遥远麦克风阵列捕获的多对话者重叠语音。我们的名为T-Sot-VA的框架在独立开发了两种最近的技术上。基于令牌级别的序列化输出训练（T-SOT），数量几何形状 - 反应连续的语音分离或VARARRARY和流媒体多对话者ASR。为了结合两种技术的最佳，我们新设计了一个基于T-SOT的ASR模型，该模型基于Vararray的两个分离的语音信号生成序列化的多对话者转录。我们还为这种ASR模型提出了一种预训练方案，我们基于单膜单键式ASR训练数据来模拟Vararray的输出信号。使用AMI会议语料库的对话转录实验表明，基于提议的框架的系统大大优于常规的框架。我们的系统分别在保留流媒体推理能力的同时，在多远离微米频道设置中分别实现了AMI开发和评估集的最新单词错误率为13.7％和15.5％。