As the application of AI in cervical cancer cell detection expands, the demand for large-scale labeled pathological slide data has increased, resulting in a time-consuming and costly process. Furthermore, significant domain shifts caused by variations in sample collection, processing, and staining conditions across different medical institutions limit the generalization capability of detection models. To address these challenges, existing unsupervised domain adaptation (UDA) methods attempt to generalize source domain models to target domains with unannotated data, reducing reliance on annotated datasets. However, these approaches still face challenges, such as the unavailability of source data, label noise leading to model degradation, and insufficient convolutional feature extraction. To overcome these limitations, this paper proposes a Deformable-based Source-Free Unsupervised Domain Adaptation (DSFUDA) method for cervical cell detection, which utilizes only on the source domain model and unlabeled target domain data. The proposed approach introduces a novel source-free UDA framework for cervical cell detection, incorporating a denoising module based on a diffusion model (FD-Module) to suppress erroneous and domain-specific features, preventing model degradation. Additionally, a deformable context-aware module (DCA-Module) is developed to adaptively adjust convolutional receptive fields, enhancing feature extraction and improving the accuracy of abnormal cell detection. The effectiveness of the proposed method is validated on two public datasets, CDTBS and Sipakmed.

This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization, EEND methods are better in terms of speaker overlap handling. However, EEND still has a disadvantage in that it cannot deal with a flexible number of speakers. To remedy this problem, we introduce encoder-decoder-based attractor calculation module (EDA) to EEND. Once frame-wise embeddings are obtained, EDA sequentially generates speaker-wise attractors on the basis of a sequence-to-sequence method using an LSTM encoder-decoder. The attractor generation continues until a stopping condition is satisfied; thus, the number of attractors can be flexible. Diarization results are then estimated as dot products of the attractors and embeddings. The embeddings from speaker overlaps result in larger dot product values with multiple attractors; thus, this method can deal with speaker overlaps. Because the maximum number of output speakers is still limited by the training set, we also propose an iterative inference method to remove this restriction. Further, we propose a method that aligns the estimated diarization results with the results of an external speech activity detector, which enables fair comparison against cascaded approaches. Extensive evaluations on simulated and real datasets show that EEND-EDA outperforms the conventional cascaded approach.

Attractor-based end-to-end diarization is achieving comparable accuracy to the carefully tuned conventional clustering-based methods on challenging datasets. However, the main drawback is that it cannot deal with the case where the number of speakers is larger than the one observed during training. This is because its speaker counting relies on supervised learning. In this work, we introduce an unsupervised clustering process embedded in the attractor-based end-to-end diarization. We first split a sequence of frame-wise embeddings into short subsequences and then perform attractor-based diarization for each subsequence. Given subsequence-wise diarization results, inter-subsequence speaker correspondence is obtained by unsupervised clustering of the vectors computed from the attractors from all the subsequences. This makes it possible to produce diarization results of a large number of speakers for the whole recording even if the number of output speakers for each subsequence is limited. Experimental results showed that our method could produce accurate diarization results of an unseen number of speakers. Our method achieved 11.84 %, 28.33 %, and 19.49 % on the CALLHOME, DI-HARD II, and DIHARD III datasets, respectively, each of which is better than the conventional end-to-end diarization methods.

This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a speaker's permutation problem due to the possibility to assign speaker regions incorrectly across the recording. To circumvent this inconsistency, we proposed a speaker-tracing buffer mechanism that selects several input frames representing the speaker permutation information from previous chunks and stores them in a buffer. These buffered frames are stacked with the input frames in the current chunk and fed into a self-attention network. Our method ensures consistent diarization outputs across the buffer and the current chunk by checking the correlation between their corresponding outputs. Additionally, we trained SA-EEND with variable chunk-sizes to mitigate the mismatch between training and inference introduced by the speaker-tracing buffer mechanism. Experimental results, including online SA-EEND and variable chunk-size, achieved DERs of 12.54% for CALLHOME and 20.77% for CSJ with 1.4 s actual latency.

End-to-end speaker diarization for an unknown number of speakers is addressed in this paper.Recently proposed end-toend speaker diarization outperformed conventional clusteringbased speaker diarization, but it has one drawback: it is less flexible in terms of the number of speakers.This paper proposes a method for encoder-decoder based attractor calculation (EDA), which first generates a flexible number of attractors from a speech embedding sequence.Then, the generated multiple attractors are multiplied by the speech embedding sequence to produce the same number of speaker activities.The speech embedding sequence is extracted using the conventional self-attentive end-to-end neural speaker diarization (SA-EEND) network.In a two-speaker condition, our method achieved a 2.69 % diarization error rate (DER) on simulated mixtures and a 8.07 % DER on the two-speaker subset of CALLHOME, while vanilla SA-EEND attained 4.56 % and 9.54 %, respectively.In unknown numbers of speakers conditions, our method attained a 15.29 % DER on CALLHOME, while the x-vectorbased clustering method achieved a 19.43 % DER.

Speaker diarization has been mainly developed based on the clustering of speaker embeddings. However, the clustering-based approach has two major problems; i.e., (i) it is not optimized to minimize diarization errors directly, and (ii) it cannot handle speaker overlaps correctly. To solve these problems, the End-to-End Neural Diarization (EEND), in which a bidirectional long short-term memory (BLSTM) network directly outputs speaker diarization results given a multi-talker recording, was recently proposed. In this study, we enhance EEND by introducing self-attention blocks instead of BLSTM blocks. In contrast to BLSTM, which is conditioned only on its previous and next hidden states, self-attention is directly conditioned on all the other frames, making it much suitable for dealing with the speaker diarization problem. We evaluated our proposed method on simulated mixtures, real telephone calls, and real dialogue recordings. The experimental results revealed that the self-attention was the key to achieving good performance and that our proposed method performed significantly better than the conventional BLSTM-based method. Our method was even better than that of the state-of-the-art x-vector clustering-based method. Finally, by visualizing the latent representation, we show that the self-attention can capture global speaker characteristics in addition to local speech activity dynamics. Our source code is available online at https://github.com/hitachi-speech/EEND.

This paper investigates the use of target-speaker automatic speech recognition (TS-ASR) for simultaneous speech recognition and speaker diarization of single-channel dialogue recordings. TS-ASR is a technique to automatically extract and recognize only the speech of a target speaker given a short sample utterance of that speaker. One obvious drawback of TS-ASR is that it cannot be used when the speakers in the recordings are unknown because it requires a sample of the target speakers in advance of decoding. To remove this limitation, we propose an iterative method, in which (i) the estimation of speaker embeddings and (ii) TS-ASR based on the estimated speaker embeddings are alternately executed. We evaluated the proposed method by using very challenging dialogue recordings in which the speaker overlap ratio was over 20%. We confirmed that the proposed method significantly reduced both the word error rate (WER) and diarization error rate (DER). Our proposed method combined with i-vector speaker embeddings ultimately achieved a WER that differed by only 2.1 % from that of TS-ASR given oracle speaker embeddings. Furthermore, our method can solve speaker diarization simultaneously as a by-product and achieved better DER than that of the conventional clustering-based speaker diarization method based on i-vector.

Motion saliency detection has an important impact on further video processing tasks, such as video segmentation, object recognition and adaptive compression. Different to image saliency, in videos, moving regions (objects) catch human beings' attention much easier than static ones. Based on this observation, we propose a novel method of motion saliency detection, which makes use of the low-rank and sparse decomposition on video slices along X-T and Y-T planes to achieve the goal, i.e. separating foreground moving objects from backgrounds. In addition, we adopt the spatial information to preserve the completeness of the detected motion objects. In virtue of adaptive threshold selection and efficient noise elimination, the proposed approach is suitable for different video scenes, and robust to low resolution and noisy cases. The experiments demonstrate the performance of our method compared with the state-of-the-art.