pwc-archive/papers-with-abstracts huggingface dataset:
Sepformer-based Models: More Efficient Models for Long Sequence Time-Series Forecasting
Authors: Jin Fan, Zehao Wang, Danfeng Sun, Huifeng Wu
Date: 2022-12-23 00:00:00
Proceeding: IEEE Transactions on Emerging Topics in Computing 2022 12
Tasks: GPU, Time Series, Time Series Forecasting

Forecasting long sequence time series plays a crucial role in many applications such as anomaly detection and financial predictions. Achieving consistently good results requires a model that can precisely capture the long-range dependencies in input sequences. And very few current models can meet the requirements. Informer has recently demonstrated state-of-the-art accuracy in LSTF. Yet several other aspects of its performance leave much room for improvement. These include: 1) complexity - Informer has a relatively high computational complexity and a high memory overhead; 2) nuance - there is limited ability to capture the subtle features in a data stream; 3) interpretability - the inference procedure of Informer is not explainable; 4) extensibility - accuracy is poor with extra-long multivariate time series. To address these issues, we propose a suite of models under the banner Sepformer. The set comprises Sepformer and two variants SWformer and Mini-SWformer. Sepformer uses separate networks to extract data stream features in parallel. SWformer and Mini-SWformer dramatically separate high-frequency and low-frequency components to process the data stream and reduce the requirement for GPU memory by adopting a discrete wavelet transform. Extensive experiments show that the Sepformer models substantially outperform state-of-the-art methods in terms of accuracy, computational complexity and usage of GPU memory use.
Leveraging Batch Normalization for Vision Transformers
Authors: Zhuliang Yao, Yue Cao, Yutong Lin, Ze Liu, Zheng Zhang, Han Hu
Date: 2021-01-01 00:00:00
Proceeding: ICCVW 2021 1

Transformer-based vision architectures have attracted great attention because of the strong performance over the convolutional neural networks (CNNs). Inherited from the NLP tasks, the architectures take Layer Normalization (LN) as a default normalization technique. On the other side, previous vision models, i.e., CNNs, treat Batch Normalization (BN) as a de facto standard, with the merits of faster inference than other normalization layers due to an avoidance of calculating the mean and variance statistics during inference, as well as better regularization effects during training. In this paper, we aim to introduce Batch Normalization to Transformer-based vision architectures. Our initial exploration reveals frequent crashes in model training when directly replacing all LN layers with BN, contributing to the un-normalized feed forward network (FFN) blocks. We therefore propose to add a BN layer in-between the two linear layers in the FFN block where stabilized training statistics are observed, resulting in a pure BN-based architecture. Our experiments proved that our resulting approach is as effective as the LN-based counterpart and is about 20% faster.
ADA-VAD: Unpaired Adversarial Domain Adaptation for Noise-Robust Voice Activity Detection
Authors: Taesoo Kim, Jiho Chang, Jong Hwan Ko
Date: 2022-04-22 00:00:00
Proceeding: ICASSP 2022 4
Tasks: Action Detection, Activity Detection, Domain Adaptation

Voice Activity Detection (VAD) is becoming an essential front-end component in various speech processing systems. As those systems are commonly deployed in environments with diverse noise types and low signal-to-noise ratios (SNRs), an effective VAD method should perform robust detection of speech region out of noisy background signals. In this paper, we propose adversarial domain adaptive VAD (ADA-VAD), which is a deep neural network (DNN) based VAD method highly robust to audio samples with various noise types and low SNRs. The proposed method trains DNN models for a VAD task in a supervised manner. Simultaneously, to mitigate the performance degradation due to back-ground noises, the adversarial domain adaptation method is adopted to match the domain discrepancy between noisy and clean audio stream in an unsupervised manner. The results show that ADA-VAD achieves an average of 3.6%p and 7%p higher AUC than models trained with manually extracted features on the AVA-speech dataset and a speech database synthesized with an unseen noise database, respectively.
An Acne Grading Framework on Face Images via Skin Attention and SFNet
Authors: Yi Lin, Yi Guan, Zhaoyang Ma, Haiyan You, Xue Cheng, Jingchi Jiang
Date: 2022-01-14 00:00:00
Proceeding: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2022 1
Tasks: Acne Severity Grading, Diagnostic

Severity level grading is a vitally important step to make correct diagnoses and personalized treatment schemes for acne, which is mainly carried out in two ways: criterion-based lesion counting and experience-based global estimation. In this paper, the global estimation of acne severity grading is studied by Convolutional Neural Networks (CNNs) and a unified acne grading framework that can diagnose referring to different grading criteria is proposed. Firstly, an adaptive image preprocessing method that can efficiently reduce the background noise and emphasize the skin information is proposed. Next, an innovative CNN structure SFNet, which fuses local skin features with global features to effectively enhance the perception of color gaps between skin and lesion, is presented. The proposed framework is verified on two datasets with different acne grading criteria. Experimental results show that the accuracy of the proposed framework reaches 84.52% exceeding the state-of-the-art method by 1.7% and reaches the diagnostic level of a professional dermatologist.
KIEGLFN: A unified acne grading framework on face images
Authors: Yi Lin, Jingchi Jiang, Zhaoyang Ma, Dongxin Chen, Yi Guan, Haiyan You, Xue Cheng, Bingmei Liu, Gongning Luo
Date: 2022-06-01 00:00:00
Proceeding: Computer Methods and Programs in Biomedicine 2022 6
Tasks: Acne Severity Grading

Grading the severity level is an extremely important procedure for correct diagnoses and personalized treatment schemes for acne. However, the acne grading criteria are not unified in the medical field. This work aims to develop an acne diagnosis system that can be generalized to various criteria. Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed. It imitates the global estimation of the dermatologist diagnosis in two steps. First, an adaptive image preprocessing method effectively filters meaningless information and enhances key information. Next, an innovative network structure fuses global deep features with local features to simulate the dermatologists’ comparison of local skin and global observation. In addition, a transfer fine-tuning strategy is proposed to transfer prior knowledge on one criterion to another criterion, which effectively improves the framework performance in case of insufficient data. Results: The Preprocessing method effectively filters meaningless areas and improves the performance of downstream models.The framework reaches accuracies of 84.52% and 59.35% on two datasets separately. Conclusions: The application of the framework on acne grading exceeds the state-of-the-art method by 1.71%, reaches the diagnostic level of a professional dermatologist and the transfer fine-tuning strategy improves the accuracy of 6.5% on the small data.
Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge
Authors: Yi Lin, Jingchi Jiang, Dongxin Chen, Zhaoyang Ma, Yi Guan, Xiguang Liu, Haiyan You, Jing Yang, Xue Cheng
Date: 2023-01-02 00:00:00
Proceeding: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2023 1
Tasks: Acne Severity Grading, image-classification, Image Classification

Acne Vulgaris seriously affects people’s daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.
DED: Diagnostic Evidence Distillation for acne severity grading on face images
Authors: Yi Lin, Jingchi Jiang, Dongxin Chen, Zhaoyang Ma, Yi Guan, Xiguang Liu, Haiyan You, Jing Yang
Date: 2023-10-05 00:00:00
Proceeding: Expert Systems with Applications 2023 10
Tasks: Acne Severity Grading, Diagnostic, image-classification, Image Classification, Knowledge Distillation, Specificity

Acne seriously affects people’s daily life. Acne severity level grading plays a decisive role in the cure. However, the acne criterion is not unified in the medical field. Most of the current studies explore the application of advanced visual models on acne severity grading but lack the adaptation to the characteristics of acne diagnosis. Meanwhile, some studies propose specifically adapted methods for respectively used acne criteria but can not be used on other acne criteria. In this study, we propose an acne diagnosis method, Diagnostic Evidence Distillation (DED), that suitably adapts the characteristics of acne diagnosis and can be applied to diagnose under different acne criteria. Firstly, we fully investigate and analyze the commonality of various acne criteria and condense the face acne diagnosis into an unconventional image classification problem based on the diagnostic evidence of type and number of fine-grained lesions distributed on a whole face. Next, we propose the DED framework to adapt the characteristics of acne diagnosis. This framework uses the teacher-student structure of knowledge distillation methods to bring the diagnostic evidence, which is not available for new patients but only in training data, into the diagnosis model. To break the limitation of different criteria, the framework uses convolutional neural networks (CNNs) as backbones to imitate the global estimation of dermatology. We also propose a subtask joint learning for the teacher network to enhance its guidance to the student. The DED is applied to diagnose acne on two datasets ACNE04 and PLSBRACNE01 based on different mainstream acne criteria. The experimental results demonstrate that on both datasets, the DED effectively improves the diagnosis performance, exceeds the state-of-the-art and reaches the diagnostic level of dermatologists. The precision, sensitivity, specificity, Youden Index and accuracy reach 85.31%, 84.83%, 94.66%, 79.48% and 86.06% on the ACNE04 dataset and 69.16%, 65.62%, 88.93%, 54.54% and 67.56% on the PLSBRACNE01 dataset, respectively.
BL-JUNIPER: A CNN-Assisted Framework for Perceptual Video Coding Leveraging Block-Level JND
Authors: Sanaz Nami, Farhad Pakdaman, Mahmoud Reza Hashemi, Shervin Shirmohammadi
Date: 2022-06-29 00:00:00
Proceeding: IEEE Transactions on Multimedia 2022 6
Tasks: Quantization

Just Noticeable Distortion (JND) finds the minimum distortion level perceivable by humans. This can be a natural solution for setting the compression for each video region in perceptual video coding. However, existing JND-based solutions estimate JND levels for each video frame and ignore the fact that different video regions have different perceptual importance. To address this issue, we propose a Block-Level Just Noticeable Distortion-based Perceptual (BL-JUNIPER) framework for video coding. The proposed four-stage framework combines different perceptual information to further improve the prediction accuracy. The JND mapping in the first stage derives block-level JNDs from frame-level information without the need to collect a new bock-level JND dataset. In the second stage, an efficient CNN-based model is proposed to predict JND levels for each block according to spatial and temporal characteristics. Unlike existing methods, BL-JUNIPER works on raw video frames and avoids re-encoding each frame several times, making it computationally practical. Third, the visual importance of each block is measured using a visual attention model. Finally, a proposed quantization control algorithm uses both JND levels and visual importance to adjust the Quantization Parameter (QP) for each block. The specific algorithm for each stage of the proposed framework can be changed, as long as the input and output formats of each block are followed, without the need to change other stages, based on any current or future methods, providing a flexible and robust solution. Extensive experimental results demonstrate that BL-JUNIPER achieves a mean bitrate reduction of 27.75% with a Delta Mean Opinion Score (DMOS) close to zero and BD-Rate gains of 25.44% based on MOS, compared to the baseline encoding, and also gains a better performance compared to competing methods.
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data
Authors: Sung-Min Lee, Eunhwan Park, Daeryong Seo, Donghyeon Jeon, Inho Kang, Seung-Hoon Na
Date: 2023-05-02 00:00:00
Proceeding: Conference 2023 5
Tasks: Decoder, Question Answering

Transformer-based models for question answering (QA) over tables and texts confront a “long” hybrid sequence over tabular and textual elements, causing long-range reasoning problems. To handle long-range reasoning, we extensively employ a fusion-in-decoder (FiD) and exponential moving average (EMA), proposing a {underline{M}oving {underline{A}verage Equipped {underline{F}usion-{underline{i}n-{underline{D}ecoder ({textbf{MAFiD}). With FiD as the backbone architecture, MAFiD combines various levels of reasoning: {textit{independent encoding} of homogeneous data and {textit{single-row} and {textit{multi-row heterogeneous reasoning}, using a {textit{gated cross attention layer} to effectively aggregate the three types of representations resulting from various reasonings. Experimental results on HybridQA indicate that MAFiD achieves state-of-the-art performance by increasing exact matching (EM) and F1 by $1.1$ and $1.7$, respectively, on the blind test set.
Evaluating generation of chaotic time series by convolutional generative adversarial networks
Authors: Yuki Tanaka, Yutaka Yamaguti
Date: 2023-05-26 00:00:00
Tasks: Generative Adversarial Network, Time Series, Time Series Analysis

To understand the ability and limitations of convolutional neural networks to generate time series that mimic complex temporal signals, we trained a generative adversarial network consisting of deep convolutional networks to generate chaotic time series and used nonlinear time series analysis to evaluate the generated time series. A numerical measure of determinism and the Lyapunov exponent, a measure of trajectory instability, showed that the generated time series well reproduce the chaotic properties of the original time series. However, error distribution analyses showed that large errors appeared at a low but non-negligible rate. Such errors would not be expected if the distribution were assumed to be exponential.
Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification
Authors: Bin Yang, Mang Ye, Jun Chen, Zesen Wu
Date: 2022-10-14 00:00:00
Proceeding: ACM MM 2022 10
Tasks: Contrastive Learning, Person Re-Identification

Visible infrared person re-identification (VI-ReID) aims at searching out the corresponding infrared (visible) images from a gallery set captured by other spectrum cameras. Recent works mainly focus on supervised VI-ReID methods that require plenty of cross-modality (visible-infrared) identity labels which are more expensive than the annotations in single-modality person ReID. For the unsupervised learning visible infrared re-identification (USL-VI-ReID), the large cross-modality discrepancies lead to difficulties in generating reliable cross-modality labels and learning modality-invariant features without any annotations. To address this problem, we propose a novel Augmented Dual-Contrastive Aggregation (ADCA) learning framework. Specifically, a dual-path contrastive learning framework with two modality-specific memories is proposed to learn the intra-modality person representation. To associate positive cross-modality identities, we design a cross-modality memory aggregation module with count priority to select highly associated positive samples, and aggregate their corresponding memory features at the cluster level, ensuring that the optimization is explicitly concentrated on the modality-irrelevant perspective. Extensive experiments demonstrate that our proposed ADCA significantly outperforms existing unsupervised methods under various settings, and even surpasses some supervised counterparts, facilitating VI-ReID to real-world deployment.
Training Socially Aligned Language Models on Simulated Social Interactions
Authors: Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M. Dai, Diyi Yang, Soroush Vosoughi
Date: 2023-05-26 00:00:00

Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpar generalization in unfamiliar scenarios and vulnerability to adversarial attacks. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions. In comparison to existing methodologies, our approach is considerably more scalable and efficient, demonstrating superior performance in alignment benchmarks and human evaluations. This paradigm shift in the training of LMs brings us a step closer to developing AI systems that can robustly and accurately reflect societal norms and values.
Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification
Authors: Ke Ji, Yixin Lian, Jingsheng Gao, Baoyuan Wang
Date: 2023-05-26 00:00:00
Tasks: Classification, Contrastive Learning, Few-shot HTC, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Text Classification

Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered. Recently, there is a growing trend of applying prompts on pre-trained language models (PLMs), which has exhibited effectiveness in the few-shot flat text classification tasks. However, limited work has studied the paradigm of prompt-based learning in the HTC problem when the training data is extremely scarce. In this work, we define a path-based few-shot setting and establish a strict path-based evaluation metric to further explore few-shot HTC tasks. To address the issue, we propose the hierarchical verbalizer ("HierVerb"), a multi-verbalizer framework treating HTC as a single- or multi-label classification problem at multiple layers and learning vectors as verbalizers constrained by hierarchical structure and hierarchical contrastive learning. In this manner, HierVerb fuses label hierarchy knowledge into verbalizers and remarkably outperforms those who inject hierarchy through graph encoders, maximizing the benefits of PLMs. Extensive experiments on three popular HTC datasets under the few-shot settings demonstrate that prompt with HierVerb significantly boosts the HTC performance, meanwhile indicating an elegant way to bridge the gap between the large pre-trained model and downstream hierarchical classification tasks. Our code and few-shot dataset are publicly available at https://github.com/1KE-JI/HierVerb.
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection
Authors: Jeeho Hyun, Sangyun Kim, Giyoung Jeon, Seung Hwan Kim, Kyunghoon Bae, Byung Jun Kang
Date: 2023-05-26 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Pseudo Label, Representation Learning

Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the common visual representations pre-trained from natural image datasets and distill the relevant features. However, existing approaches still have the discrepancy between the pre-trained feature and the target data, or require the input augmentation which should be carefully designed, particularly for the industrial dataset. In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model. ReConPatch employs contrastive representation learning to collect and distribute features in a way that produces a target-oriented and easily separable representation. To address the absence of labeled pairs for the contrastive learning, we utilize two similarity measures between data representations, pairwise and contextual similarities, as pseudo-labels. Our method achieves the state-of-the-art anomaly detection performance (99.72%) for the widely used and challenging MVTec AD dataset. Additionally, we achieved a state-of-the-art anomaly detection performance (95.8%) for the BTAD dataset.
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
Authors: Vijay Viswanathan, Luyu Gao, Tongshuang Wu, PengFei Liu, Graham Neubig
Date: 2023-05-26 00:00:00
Tasks: Information Retrieval, Retrieval

Modern machine learning relies on datasets to develop and validate research ideas. Given the growth of publicly available data, finding the right dataset to use is increasingly difficult. Any research question imposes explicit and implicit constraints on how well a given dataset will enable researchers to answer this question, such as dataset size, modality, and domain. We operationalize the task of recommending datasets given a short natural language description of a research idea, to help people find relevant datasets for their needs. Dataset recommendation poses unique challenges as an information retrieval problem; datasets are hard to directly index for search and there are no corpora readily available for this task. To facilitate this task, we build the DataFinder Dataset which consists of a larger automatically-constructed training set (17.5K queries) and a smaller expert-annotated evaluation set (392 queries). Using this data, we compare various information retrieval algorithms on our test set and present a superior bi-encoder retriever for text-based dataset recommendation. This system, trained on the DataFinder Dataset, finds more relevant search results than existing third-party dataset search engines. To encourage progress on dataset recommendation, we release our dataset and models to the public.