pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge-aided and data-driven fuzzy decision-making for sludge bulking
Authors: Zheng Liu; Honggui Han; Hongyan Yang; Junfei Qiao
Date: 2022-08-09 00:00:00
Proceeding: journal 2022 8
Tasks: Decision Making

Decision making is essential to utilize the operation information of wastewater treatment process (WWTP) to provide the inhibition strategy for sludge bulking. However, since majority of decision-making models focus solely on knowledge or data resources, avoiding the interrelations and dependencies between the operation information, these models are difficult to obtain comprehensive and precise solutions. Thus, to solve this problem, a knowledge-aided and data-driven fuzzy decision-making (KD-FDM) model is designed for sludge bulking. First, a recursive reconstruction contribution (RRC) method is proposed to analyze the operation data to diagnose the fault of sludge bulking. Then, the fault information can be recorded as valid knowledge to assist in decision making. Second, a knowledge internalization mechanism is developed to make use of the knowledge from the results of RRC and the expert experience of sludge bulking to construct the initial condition of KD-FDM model. Then, the KD-FDM model can obtain the precision parameters and compact structure in the initialization phase. Third, the KD-FDM model using a knowledge-aided fuzzy broad learning system is employed to determine suppression strategies for sludge bulking. Then, the KD-FDM model can obtain fast and accurate strategies to mitigate the detrimental impact on the process performance. Finally, the KD-FDM model is tested in a real WWTP to confirm its effectiveness. The experimental results demonstrate that the proposed model can achieve outstanding performance.
Transfer Learning Algorithm with Knowledge Division Level
Authors: Honggui Han; Hongxu Liu; Cuili Yang; Junfei Qiao
Date: 2022-03-01 00:00:00
Proceeding: journal 2022 3
Tasks: Transfer Learning

One of the major challenges of transfer learning algorithms is the domain drifting problem where the knowledge of source scene is inappropriate for the task of target scene. To solve this problem, a transfer learning algorithm with knowledge division level (KDTL) is proposed to subdivide knowledge of source scene and leverage them with different drifting degrees. The main properties of KDTL are three folds. First, a comparative evaluation mechanism is developed to detect and subdivide the knowledge into three kinds—the ineffective knowledge, the usable knowledge, and the efficient knowledge. Then, the ineffective and usable knowledge can be found to avoid the negative transfer problem. Second, an integrated framework is designed to prune the ineffective knowledge in the elastic layer, reconstruct the usable knowledge in the refined layer, and learn the efficient knowledge in the leveraged layer. Then, the efficient knowledge can be acquired to improve the learning performance. Third, the theoretical analysis of the proposed KDTL is analyzed in different phases. Then, the convergence property, error bound, and computational complexity of KDTL are provided for the successful applications. Finally, the proposed KDTL is tested by several benchmark problems and some real problems. The experimental results demonstrate that this proposed KDTL can achieve significant improvement over some state-of-the-art algorithms.
LS-GAN: Iterative Language-based Image Manipulation via Long and Short Term Consistency Reasoning
Authors: Gaoxiang Cong, Liang Li, Zhenhuan Liu, Yunbin Tu, Weijun Qin, Shenyuan Zhang, Chengang Yan, Wenyu Wang, Bin Jiang
Date: 2022-10-22 00:00:00
Proceeding: journal 2022 10
Tasks: Generative Adversarial Network, Image Manipulation

Iterative language-based image manipulation aims to edit images step by step according to user's linguistic instructions. The existing methods mostly focus on aligning the attributes and appearance of new-added visual elements with current instruction. However, they fail to maintain consistency between instructions and images as iterative rounds increase. To address this issue, we propose a novel Long and Short term consistency reasoning Generative Adversarial Network (LS-GAN), which enhances the awareness of previous objects with current instruction and better maintains the consistency with the user's intent under the continuous iterations. Specifically, we first design a Context-aware Phrase Encoder (CPE) to learn the user's intention by extracting different phrase-level information about the instruction. Further, we introduce a Long and Short term Consistency Reasoning (LSCR) mechanism. The long-term reasoning improves the model on semantic understanding and positional reasoning, while short-term reasoning ensures the ability to construct visual scenes based on linguistic instructions. Extensive results show that LS-GAN improves the generation quality in terms of both object identity and position, and achieves the state-of-the-art performance on two public datasets.
Siamese Graph Learning for Semi-supervised Age Estimation
Authors: Hao Liu; Mei Ma; Zixian Gao; Zongyong Deng; Fengjun Li; Zhendong Li
Date: 2023-03-13 00:00:00
Proceeding: journal 2023 3
Tasks: Age Estimation, Diversity, Graph Learning

In this paper, we propose a Siamese graph learning (SGL) approach to alleviate aging dataset bias. While numerous semi-supervised algorithms have been successfully applied to classification tasks, most of them assume that both the labeled and unlabeled samples are drawn from identical distributions. However, this assumption may not hold due to the heterogeneity of face aging data, which gives rise to a bias and unpromising prediction. Motivated by this, our SGL learns to align the sparse distribution with the dense one for dataset debias with preserving the real aging smoothness. To achieve this, we adopt a mixup strategy to plausibly generate hallucinatory samples, which leverages amounts of unlabeled data to enhance the diversity of unbalanced classes. Moreover, we develop a graph contrastive regularization to suppress the noise introduced by auxiliary unlabeled samples. Extensive experimental results show compelling performance by only utilizing the limited scalability of training annotations.
Joint Statistical and Causal Feature Modulated Face Anti-Spoofing
Authors: Xin Dong; Tao Wang; Zhendong Li; Hao Liu
Date: 2023-07-04 00:00:00
Proceeding: journal 2023 7
Tasks: Face Anti-Spoofing, Miscellaneous

In this paper, we propose a hierarchical feature modulation (HFM) approach for stable face anti-spoofing in unseen domains and unseen attacks. The conventional multi-domain based generalizable approaches likely lead to local optima due to the complicated or heuristic learning paradigm. Inspired by the fact that high-level semantic disturbances and low-level miscellaneous bias jointly cause the distribution shift, HFM aims to modulate the fine-grained feature in a hierarchical manner. Specifically, we complement the structural feature with patch-wise learnable statistical information, i.e. local difference histogram, to relieve the overfitting on high-level semantics. We further introduce the structural causal model (SCM) with imaging color model to reveal that presenting mediums and capturing devices destroy the liveness-relevant information from the low level. Thus we model this hidden entanglement as a distribution mixture problem and propose the expectation-maximization (EM) based causal intervention to remove these miscellanies. Experimental results on public datasets demonstrate the effectiveness of HFM, especially in out-of-distribution settings.
Cross-Modality Fourier Feature for Medical Image Synthesis
Authors: Mei Ma; Ling Lin; Heng Wang; Zhendong Li; Hao Liu
Date: 2023-07-10 00:00:00
Proceeding: journal 2023 7
Tasks: Image Generation

In this paper, we propose a cross-modality fourier feature (CMFF) method via frequency selection, which learns the rational anatomical structure for targeting medical modality images. Unlike existing works seeking pixel-wise intensity discrepancy likely misleading to bias anatomical structures, our approach strongly holds the medical prior that different modality MRI images should share the same anatomical structure. To achieve this, our method instead learns to convert MRI images to the auxiliary frequency domain. Moreover, we adopt the Shapley value to quantify the contribution of each frequency, with respect to the structure for MRI image pairs of different modalities. Thus our approach learns to refine the anatomical structure generated by the target modality iteratively. Extensive experimental results on the BraTS dataset show that our model surpasses the performance compared to SOTAs.
Discovering Frequency Bursting Patterns in Temporal Graphs
Authors: Qianzhen Zhang, Deke Guo, Xiang Zhao, Long Yuan, Lailong Luo
Date: 2023-07-23 00:00:00
Proceeding: 2023 IEEE 39th International Conference on Data Engineering (ICDE) 2023 7

A frequency bursting pattern (FBP) in temporal
graphs represents some interaction behavior that accumulates its
frequency at the fastest rate. Mining FBPs is essential to early
warning of emergencies. However, existing studies on frequencybased pattern mining in graphs do not consider the temporal
information and bursting features of a subgraph pattern. As
a result, they may not provide effective and efficient mining
algorithms for FBP discovery. In this paper, we study the problem
of discovering top-k FBPs in temporal graphs. We present a
novel model, referred to as maximal (m, θ)-bursting pattern, to
describe FBPs in a temporal graph, which is a subgraph with a
size larger than m that accumulates its frequency at the fastest
rate during a time interval of length no less than θ. A naive
solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns
are mined. However, this method will result in huge search space
since we need to check every possible time interval for a candidate
pattern in the temporal graph. To tackle this problem, we devise
an online top-k framework in which k candidate results are
maintained from the initial timestamp to the end in the temporal
graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching
and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that
our algorithm has higher efficiency, effectiveness and scalability.
Index Terms—temporal graph, frequency bursting pattern
C2T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition
Authors: Doanh C. Bui, Thinh V. Le, Ba Hung Ngo
Date: 2023-12-26 00:00:00
Proceeding: WACV202 2023 12
Tasks: Attribute, Pedestrian Attribute Recognition

Pedestrian attribute recognition (PAR) poses a significant challenge but holds practical significance in various security applications, including surveillance. In the scope of the UPAR challenge, this paper introduces the Channel-Aware Cross-Fused Transformer-Style Networks (C2T-Net). This network effectively integrates two powerful transformer-style networks, namely the Swin Transformer (SwinT) and a customized variant of the vanilla vision transformer (EVA ViT). The aim is to capture both local and global aspects of an individual for precise attribute recognition. To facilitate the understanding of intricate relationships among channels, a channel-aware self-attention mechanism is devised and integrated into each SwinT block. Furthermore, the fusion of features from the two transformer-style networks is accomplished through cross-fusion, enabling each network to mutually amplify and boost the textural nuances present in the other. The efficacy of the proposed model has been demonstrated through its performance on three PAR benchmarks: PA100K, PETA, and the UPAR2024 private test. With respect to the PA100K benchmark, our approach has achieved state-of-the-art results when compared to models that do not employ any pre-training techniques. Our performance on the PETA dataset remains competitive, standing on par with other cutting-edge models. Notably, our model achieved runner-up performance on the UPAR2024-track-1 test set. Source code is available at https://github.com/caodoanh2001/upar_challenge.
DocReal: Robust Document Dewarping of Real-Life Images via Attention-Enhanced Control Point Prediction
Authors: Fangchen Yu, Yina Xie, Lei Wu, Yafei Wen, Guozhi Wang, Shuai Ren, Xiaoxin Chen, Jianfeng Mao, Wenye Li
Date: 2023-12-01 00:00:00
Proceeding: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV-2024) 2023 12
Tasks: Optical Character Recognition (OCR)

Document image dewarping is a crucial task in computer vision with numerous practical applications. The control point method, as a popular image dewarping approach, has attracted attention due to its simplicity and efficiency. However, inaccurate control point prediction due to varying background noises and deformation types can result in unsatisfactory performance. To address these issues, we propose a robust document dewarping approach for real-life images, namely DocReal, which utilizes Enet to effectively remove background noise and an attention-enhanced control point (AECP) module to better capture local deformations. Moreover, we augment the training data by synthesizing 2D images with 3D deformations and additional deformation types. Our proposed method achieves state-of-the-art performance on the DocUNet benchmark and a newly proposed benchmark of 200 Chinese distorted images, exhibiting superior dewarping accuracy, OCR performance, and robustness to various types of image distortion.
AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator
Authors: Elysia Q. Smyers, Sydney M. Katz, Anthony L. Corso, Mykel J. Kochenderfer
Date: 2023-06-19 00:00:00
Tasks: Collision Avoidance, object-detection, Object Detection

Designing robust machine learning systems remains an open problem, and there is a need for benchmark problems that cover both environmental changes and evaluation on a downstream task. In this work, we introduce AVOIDDS, a realistic object detection benchmark for the vision-based aircraft detect-and-avoid problem. We provide a labeled dataset consisting of 72,000 photorealistic images of intruder aircraft with various lighting conditions, weather conditions, relative geometries, and geographic locations. We also provide an interface that evaluates trained models on slices of this dataset to identify changes in performance with respect to changing environmental conditions. Finally, we implement a fully-integrated, closed-loop simulator of the vision-based detect-and-avoid problem to evaluate trained models with respect to the downstream collision avoidance task. This benchmark will enable further research in the design of robust machine learning systems for use in safety-critical applications. The AVOIDDS dataset and code are publicly available at https://purl.stanford.edu/hj293cv5980 and https://github.com/sisl/VisionBasedAircraftDAA respectively.
A new type of window functions constructed with exponential function
Authors: Haichao Xu, Xingpao Suo
Date: 2023-12-23 00:00:00

The Discrete Fourier Transform (DFT) is widely utilized for signal analysis but is plagued by spectral leakage, leading to inaccuracies in signal approximation. Window functions play a crucial role in mitigating spectral leakage by providing weighting mechanisms for discrete signals. In this paper, we introduce a novel window type based on exponential function, allowing for adjustable parameters and diverse variations. We present the formulation, properties, and motivation behind the design of the new window functions. Additionally, we analyze their behavior and evaluate their performance by comparing them with mainstream window functions using six parameters. Our findings demonstrate that these new window functions exhibit outstanding flexibility and versatility in signal analysis.
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Authors: Haonan Wang, Peng Cao, Xiaoli Liu, Jinzhu Yang, Osmar Zaiane
Date: 2023-12-23 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Most state-of-the-art methods for medical image segmentation adopt the encoder-decoder architecture. However, this U-shaped framework still has limitations in capturing the non-local multi-scale information with a simple skip connection. To solve the problem, we firstly explore the potential weakness of skip connections in U-Net on multiple segmentation tasks, and find that i) not all skip connections are useful, each skip connection has different contribution; ii) the optimal combinations of skip connections are different, relying on the specific datasets. Based on our findings, we propose a new segmentation framework, named UDTransNet, to solve three semantic gaps in U-Net. Specifically, we propose a Dual Attention Transformer (DAT) module for capturing the channel- and spatial-wise relationships to better fuse the encoder features, and a Decoder-guided Recalibration Attention (DRA) module for effectively connecting the DAT tokens and the decoder features to eliminate the inconsistency. Hence, both modules establish a learnable connection to solve the semantic gaps between the encoder and the decoder, which leads to a high-performance segmentation model for medical images. Comprehensive experimental results indicate that our UDTransNet produces higher evaluation scores and finer segmentation results with relatively fewer parameters over the state-of-the-art segmentation methods on different public datasets. Code: https://github.com/McGregorWwww/UDTransNet.
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
Authors: Xinglin Xiao, Yijie Wang, Nan Xu, Yuqi Wang, Hanxuan Yang, Minzheng Wang, Yin Luo, Lei Wang, Wenji Mao, Daniel Zeng
Date: 2023-12-24 00:00:00
Tasks: UIE

The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures. Recent work has proposed methods based on large language models to uniformly model different information extraction tasks. However, these existing methods are deficient in their information extraction capabilities for Chinese languages other than English. In this paper, we propose an end-to-end chat-enhanced instruction tuning framework for universal information extraction (YAYI-UIE), which supports both Chinese and English. Specifically, we utilize dialogue data and information extraction data to enhance the information extraction performance jointly. Experimental results show that our proposed framework achieves state-of-the-art performance on Chinese datasets while also achieving comparable performance on English datasets under both supervised settings and zero-shot settings.
Word length-aware text spotting: Enhancing detection and recognition in dense text image
Authors: Hao Wang, Huabing Zhou, Yanduo Zhang, Tao Lu, Jiayi Ma
Date: 2023-12-25 00:00:00
Tasks: Text Detection, Text Spotting

Scene text spotting is essential in various computer vision applications, enabling extracting and interpreting textual information from images. However, existing methods often neglect the spatial semantics of word images, leading to suboptimal detection recall rates for long and short words within long-tailed word length distributions that exist prominently in dense scenes. In this paper, we present WordLenSpotter, a novel word length-aware spotter for scene text image detection and recognition, improving the spotting capabilities for long and short words, particularly in the tail data of dense text images. We first design an image encoder equipped with a dilated convolutional fusion module to integrate multiscale text image features effectively. Then, leveraging the Transformer framework, we synergistically optimize text detection and recognition accuracy after iteratively refining text region image features using the word length prior. Specially, we design a Spatial Length Predictor module (SLP) using character count prior tailored to different word lengths to constrain the regions of interest effectively. Furthermore, we introduce a specialized word Length-aware Segmentation (LenSeg) proposal head, enhancing the network's capacity to capture the distinctive features of long and short terms within categories characterized by long-tailed distributions. Comprehensive experiments on public datasets and our dense text spotting dataset DSTD1500 demonstrate the superiority of our proposed methods, particularly in dense text image detection and recognition tasks involving long-tailed word length distributions encompassing a range of long and short words.
Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View Stereo
Authors: Hongjie Li, Yao Guo, Xianwei Zheng, Hanjiang Xiong
Date: 2023-12-26 00:00:00
Tasks: Depth Estimation, Depth Prediction

This paper introduces a learnable Deformable Hypothesis Sampler (DeformSampler) to address the challenging issue of noisy depth estimation for accurate PatchMatch Multi-View Stereo (MVS). We observe that the heuristic depth hypothesis sampling modes employed by PatchMatch MVS solvers are insensitive to (i) the piece-wise smooth distribution of depths across the object surface, and (ii) the implicit multi-modal distribution of depth prediction probabilities along the ray direction on the surface points. Accordingly, we develop DeformSampler to learn distribution-sensitive sample spaces to (i) propagate depths consistent with the scene's geometry across the object surface, and (ii) fit a Laplace Mixture model that approaches the point-wise probabilities distribution of the actual depths along the ray direction. We integrate DeformSampler into a learnable PatchMatch MVS system to enhance depth estimation in challenging areas, such as piece-wise discontinuous surface boundaries and weakly-textured regions. Experimental results on DTU and Tanks \& Temples datasets demonstrate its superior performance and generalization capabilities compared to state-of-the-art competitors. Code is available at https://github.com/Geo-Tell/DS-PMNet.
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
Authors: Ziyang Ma, Zhisheng Zheng, Jiaxin Ye, Jinchao Li, Zhifu Gao, Shiliang Zhang, Xie Chen
Date: 2023-12-23 00:00:00
Tasks: Emotion Recognition, Self-Supervised Learning, Sentiment Analysis, Speech Emotion Recognition

We propose emotion2vec, a universal speech emotion representation model. emotion2vec is pre-trained on open-source unlabeled emotion data through self-supervised online distillation, combining utterance-level loss and frame-level loss during pre-training. emotion2vec outperforms state-of-the-art pre-trained universal models and emotion specialist models by only training linear layers for the speech emotion recognition task on the mainstream IEMOCAP dataset. In addition, emotion2vec shows consistent improvements among 10 different languages of speech emotion recognition datasets. emotion2vec also shows excellent results on other emotion tasks, such as song emotion recognition, emotion prediction in conversation, and sentiment analysis. Comparison experiments, ablation experiments, and visualization comprehensively demonstrate the universal capability of the proposed emotion2vec. To the best of our knowledge, emotion2vec is the first universal representation model in various emotion-related tasks, filling a gap in the field.