pwc-archive/papers-with-abstracts huggingface dataset:
A Training-Free Defense Framework for Robust Learned Image Compression
Authors: Myungseo Song, Jinyoung Choi, Bohyung Han
Date: 2024-01-22 00:00:00
Tasks: Image Compression

We study the robustness of learned image compression models against adversarial attacks and present a training-free defense technique based on simple image transform functions. Recent learned image compression models are vulnerable to adversarial attacks that result in poor compression rate, low reconstruction quality, or weird artifacts. To address the limitations, we propose a simple but effective two-way compression algorithm with random input transforms, which is conveniently applicable to existing image compression models. Unlike the na\"ive approaches, our approach preserves the original rate-distortion performance of the models on clean images. Moreover, the proposed algorithm requires no additional training or modification of existing models, making it more practical. We demonstrate the effectiveness of the proposed techniques through extensive experiments under multiple compression models, evaluation metrics, and attack scenarios.
Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities
Authors: F. Xavier Gaya-Morey, Silvia Ramis, Jose M. Buades-Rubio, Cristina Manresa-Yee
Date: 2024-01-22 00:00:00
Tasks: Explainable artificial intelligence, Facial Expression Recognition

Facial expression recognition has gained significance as a means of imparting social robots with the capacity to discern the emotional states of users. The use of social robotics includes a variety of settings, including homes, nursing homes or daycare centers, serving to a wide range of users. Remarkable performance has been achieved by deep learning approaches, however, its direct use for recognizing facial expressions in individuals with intellectual disabilities has not been yet studied in the literature, to the best of our knowledge. To address this objective, we train a set of 12 convolutional neural networks in different approaches, including an ensemble of datasets without individuals with intellectual disabilities and a dataset featuring such individuals. Our examination of the outcomes, both the performance and the important image regions for the models, reveals significant distinctions in facial expressions between individuals with and without intellectual disabilities, as well as among individuals with intellectual disabilities. Remarkably, our findings show the need of facial expression recognition within this population through tailored user-specific training methodologies, which enable the models to effectively address the unique expressions of each user.
Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis
Authors: Jiawei Wang, Kai Hu, Zhuoyao Zhong, Lei Sun, Qiang Huo
Date: 2024-01-22 00:00:00
Tasks: Document Layout Analysis, Document Summarization, Information Retrieval, object-detection, Object Detection, Retrieval

Document structure analysis (aka document layout analysis) is crucial for understanding the physical layout and logical structure of documents, with applications in information retrieval, document summarization, knowledge extraction, etc. In this paper, we concentrate on Hierarchical Document Structure Analysis (HDSA) to explore hierarchical relationships within structured documents created using authoring software employing hierarchical schemas, such as LaTeX, Microsoft Word, and HTML. To comprehensively analyze hierarchical document structures, we propose a tree construction based approach that addresses multiple subtasks concurrently, including page object detection (Detect), reading order prediction of identified objects (Order), and the construction of intended hierarchical structure (Construct). We present an effective end-to-end solution based on this framework to demonstrate its performance. To assess our approach, we develop a comprehensive benchmark called Comp-HRDoc, which evaluates the above subtasks simultaneously. Our end-to-end system achieves state-of-the-art performance on two large-scale document layout analysis datasets (PubLayNet and DocLayNet), a high-quality hierarchical document structure reconstruction dataset (HRDoc), and our Comp-HRDoc benchmark. The Comp-HRDoc benchmark will be released to facilitate further research in this field.
LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution
Authors: Feiwei Qin, Kang Yan, Changmiao Wang, Ruiquan Ge, Yong Peng, Kai Zhang
Date: 2024-01-22 00:00:00
Tasks: Image Super-Resolution, Infrared image super-resolution, Super-Resolution

Given the broad application of infrared technology across diverse fields, there is an increasing emphasis on investigating super-resolution techniques for infrared images within the realm of deep learning. Despite the impressive results of current Transformer-based methods in image super-resolution tasks, their reliance on the self-attentive mechanism intrinsic to the Transformer architecture results in images being treated as one-dimensional sequences, thereby neglecting their inherent two-dimensional structure. Moreover, infrared images exhibit a uniform pixel distribution and a limited gradient range, posing challenges for the model to capture effective feature information. Consequently, we suggest a potent Transformer model, termed Large Kernel Transformer (LKFormer), to address this issue. Specifically, we have designed a Large Kernel Residual Attention (LKRA) module with linear complexity. This mainly employs depth-wise convolution with large kernels to execute non-local feature modeling, thereby substituting the standard self-attentive layer. Additionally, we have devised a novel feed-forward network structure called Gated-Pixel Feed-Forward Network (GPFN) to augment the LKFormer's capacity to manage the information flow within the network. Comprehensive experimental results reveal that our method surpasses the most advanced techniques available, using fewer parameters and yielding considerably superior performance.The source code will be available at https://github.com/sad192/large-kernel-Transformer.
SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning
Authors: Hao Chen, Jiaze Wang, Ziyu Guo, Jinpeng Li, Donghao Zhou, Bian Wu, Chenyong Guan, Guangyong Chen, Pheng-Ann Heng
Date: 2024-01-22 00:00:00
Tasks: Contrastive Learning, Language Modeling, Language Modelling, Optical Flow Estimation, Sentence, Sign Language Recognition

Sign language recognition (SLR) plays a vital role in facilitating communication for the hearing-impaired community. SLR is a weakly supervised task where entire videos are annotated with glosses, making it challenging to identify the corresponding gloss within a video segment. Recent studies indicate that the main bottleneck in SLR is the insufficient training caused by the limited availability of large-scale datasets. To address this challenge, we present SignVTCL, a multi-modal continuous sign language recognition framework enhanced by visual-textual contrastive learning, which leverages the full potential of multi-modal data and the generalization ability of language model. SignVTCL integrates multi-modal data (video, keypoints, and optical flow) simultaneously to train a unified visual backbone, thereby yielding more robust visual representations. Furthermore, SignVTCL contains a visual-textual alignment approach incorporating gloss-level and sentence-level alignment to ensure precise correspondence between visual features and glosses at the level of individual glosses and sentence. Experimental results conducted on three datasets, Phoenix-2014, Phoenix-2014T, and CSL-Daily, demonstrate that SignVTCL achieves state-of-the-art results compared with previous methods.
Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI
Authors: F. Xavier Gaya-Morey, Silvia Ramis-Guarinos, Cristina Manresa-Yee, Jose M. Buades-Rubio
Date: 2024-01-22 00:00:00
Tasks: Facial Expression Recognition

Facial expression recognition is vital for human behavior analysis, and deep learning has enabled models that can outperform humans. However, it is unclear how closely they mimic human processing. This study aims to explore the similarity between deep neural networks and human perception by comparing twelve different networks, including both general object classifiers and FER-specific models. We employ an innovative global explainable AI method to generate heatmaps, revealing crucial facial regions for the twelve networks trained on six facial expressions. We assess these results both quantitatively and qualitatively, comparing them to ground truth masks based on Friesen and Ekman's description and among them. We use Intersection over Union (IoU) and normalized correlation coefficients for comparisons. We generate 72 heatmaps to highlight critical regions for each expression and architecture. Qualitatively, models with pre-trained weights show more similarity in heatmaps compared to those without pre-training. Specifically, eye and nose areas influence certain facial expressions, while the mouth is consistently important across all models and expressions. Quantitatively, we find low average IoU values (avg. 0.2702) across all expressions and architectures. The best-performing architecture averages 0.3269, while the worst-performing one averages 0.2066. Dendrograms, built with the normalized correlation coefficient, reveal two main clusters for most expressions: models with pre-training and models without pre-training. Findings suggest limited alignment between human and AI facial expression recognition, with network architectures influencing the similarity, as similar architectures prioritize similar facial regions.
A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches
Authors: Richin Sukesh, Mathias Seuret, Anguelos Nicolaou, Martin Mayr, Vincent Christlein
Date: 2024-01-22 00:00:00
Tasks: Binarization, Deep Learning, valid

Binarization of document images is an important pre-processing step in the field of document analysis. Traditional image binarization techniques usually rely on histograms or local statistics to identify a valid threshold to differentiate between different aspects of the image. Deep learning techniques are able to generate binarized versions of the images by learning context-dependent features that are less error-prone to degradation typically occurring in document images. In recent years, many deep learning-based methods have been developed for document binarization. But which one to choose? There have been no studies that compare these methods rigorously. Therefore, this work focuses on the evaluation of different deep learning-based methods under the same evaluation protocol. We evaluate them on different Document Image Binarization Contest (DIBCO) datasets and obtain very heterogeneous results. We show that the DE-GAN model was able to perform better compared to other models when evaluated on the DIBCO2013 dataset while DP-LinkNet performed best on the DIBCO2017 dataset. The 2-StageGAN performed best on the DIBCO2018 dataset while SauvolaNet outperformed the others on the DIBCO2019 challenge. Finally, we make the code, all models and evaluation publicly available (https://github.com/RichSu95/Document_Binarization_Collection) to ensure reproducibility and simplify future binarization evaluations.
Rethinking Centered Kernel Alignment in Knowledge Distillation
Authors: Zikai Zhou, Yunhang Shen, Shitong Shao, Linrui Gong, Shaohui Lin
Date: 2024-01-22 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation, object-detection, Object Detection

Knowledge distillation has emerged as a highly effective method for bridging the representation discrepancy between large-scale models and lightweight models. Prevalent approaches involve leveraging appropriate metrics to minimize the divergence or distance between the knowledge extracted from the teacher model and the knowledge learned by the student model. Centered Kernel Alignment (CKA) is widely used to measure representation similarity and has been applied in several knowledge distillation methods. However, these methods are complex and fail to uncover the essence of CKA, thus not answering the question of how to use CKA to achieve simple and effective distillation properly. This paper first provides a theoretical perspective to illustrate the effectiveness of CKA, which decouples CKA to the upper bound of Maximum Mean Discrepancy~(MMD) and a constant term. Drawing from this, we propose a novel Relation-Centered Kernel Alignment~(RCKA) framework, which practically establishes a connection between CKA and MMD. Furthermore, we dynamically customize the application of CKA based on the characteristics of each task, with less computational source yet comparable performance than the previous methods. The extensive experiments on the CIFAR-100, ImageNet-1k, and MS-COCO demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs for image classification and object detection, validating the effectiveness of our approaches. Our code is available in https://github.com/Klayand/PCKA
REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in Video
Authors: F. Xavier Gaya-Morey, Jose M. Buades-Rubio, I. Scott MacKenzie, Cristina Manresa-Yee
Date: 2024-01-22 00:00:00
Tasks: Explainable artificial intelligence

We developed REVEX, a removal-based video explanations framework. This work extends fine-grained explanation frameworks for computer vision data and adapts six existing techniques to video by adding temporal information and local explanations. The adapted methods were evaluated across networks, datasets, image classes, and evaluation metrics. By decomposing explanation into steps, strengths and weaknesses were revealed in the studied methods, for example, on pixel clustering and perturbations in the input. Video LIME outperformed other methods with deletion values up to 31\% lower and insertion up to 30\% higher, depending on method and network. Video RISE achieved superior performance in the average drop metric, with values 10\% lower. In contrast, localization-based metrics revealed low performance across all methods, with significant variation depending on network. Pointing game accuracy reached 53\%, and IoU-based metrics remained below 20\%. Drawing on the findings across XAI methods, we further examine the limitations of the employed XAI evaluation metrics and highlight their suitability in different applications.
Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
Authors: Ci-Siang Lin, Chien-Yi Wang, Yu-Chiang Frank Wang, Min-Hung Chen
Date: 2024-01-22 00:00:00
Tasks: Object, Prompt Learning, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models using image data with only image-level supervision. Since precise pixel-level annotations are not accessible, existing methods typically focus on producing pseudo masks for training segmentation models by refining CAM-like heatmaps. However, the produced heatmaps may capture only the discriminative image regions of object categories or the associated co-occurring backgrounds. To address the issues, we propose a Semantic Prompt Learning for WSSS (SemPLeS) framework, which learns to effectively prompt the CLIP latent space to enhance the semantic alignment between the segmented regions and the target object categories. More specifically, we propose Contrastive Prompt Learning and Prompt-guided Semantic Refinement to learn the prompts that adequately describe and suppress the co-occurring backgrounds associated with each object category. In this way, SemPLeS can perform better semantic alignment between object regions and class labels, resulting in desired pseudo masks for training segmentation models. The proposed SemPLeS framework achieves competitive performance on standard WSSS benchmarks, PASCAL VOC 2012 and MS COCO 2014, and shows compatibility with other WSSS methods. Code: https://github.com/NVlabs/SemPLeS.
Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review
Authors: F. Xavier Gaya-Morey, Cristina Manresa-Yee, Jose M. Buades-Rubio
Date: 2024-01-22 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

As the percentage of elderly people in developed countries increases worldwide, the healthcare of this collective is a worrying matter, especially if it includes the preservation of their autonomy. In this direction, many studies are being published on Ambient Assisted Living (AAL) systems, which help to reduce the preoccupations raised by the independent living of the elderly. In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly, as the two main tasks to solve to guarantee the safety of elderly people living alone. To address the current tendency to perform these two tasks, the review focuses on the use of Deep Learning (DL) based approaches on computer vision data. In addition, different collections of data like DL models, datasets or hardware (e.g. depth or thermal cameras) are gathered from the reviewed studies and provided for reference in future studies. Strengths and weaknesses of existing approaches are also discussed and, based on them, our recommendations for future works are provided.
Full-Body Motion Reconstruction with Sparse Sensing from Graph Perspective
Authors: Feiyu Yao, Zongkai Wu, Li Yi
Date: 2024-01-22 00:00:00
Tasks: Graph Neural Network

Estimating 3D full-body pose from sparse sensor data is a pivotal technique employed for the reconstruction of realistic human motions in Augmented Reality and Virtual Reality. However, translating sparse sensor signals into comprehensive human motion remains a challenge since the sparsely distributed sensors in common VR systems fail to capture the motion of full human body. In this paper, we use well-designed Body Pose Graph (BPG) to represent the human body and translate the challenge into a prediction problem of graph missing nodes. Then, we propose a novel full-body motion reconstruction framework based on BPG. To establish BPG, nodes are initially endowed with features extracted from sparse sensor signals. Features from identifiable joint nodes across diverse sensors are amalgamated and processed from both temporal and spatial perspectives. Temporal dynamics are captured using the Temporal Pyramid Structure, while spatial relations in joint movements inform the spatial attributes. The resultant features serve as the foundational elements of the BPG nodes. To further refine the BPG, node features are updated through a graph neural network that incorporates edge reflecting varying joint relations. Our method's effectiveness is evidenced by the attained state-of-the-art performance, particularly in lower body motion, outperforming other baseline methods. Additionally, an ablation study validates the efficacy of each module in our proposed framework.
Collaborative Position Reasoning Network for Referring Image Segmentation
Authors: JianJian Cao, Beiya Dai, Yulin Li, Xiameng Qin, Jingdong Wang
Date: 2024-01-22 00:00:00
Tasks: Image Segmentation, Position, Segmentation, Semantic Segmentation

Given an image and a natural language expression as input, the goal of referring image segmentation is to segment the foreground masks of the entities referred by the expression. Existing methods mainly focus on interactive learning between vision and language to enhance the multi-modal representations for global context reasoning. However, predicting directly in pixel-level space can lead to collapsed positioning and poor segmentation results. Its main challenge lies in how to explicitly model entity localization, especially for non-salient entities. In this paper, we tackle this problem by executing a Collaborative Position Reasoning Network (CPRN) via the proposed novel Row-and-Column interactive (RoCo) and Guided Holistic interactive (Holi) modules. Specifically, RoCo aggregates the visual features into the row- and column-wise features corresponding two directional axes respectively. It offers a fine-grained matching behavior that perceives the associations between the linguistic features and two decoupled visual features to perform position reasoning over a hierarchical space. Holi integrates features of the two modalities by a cross-modal attention mechanism, which suppresses the irrelevant redundancy under the guide of positioning information from RoCo. Thus, with the incorporation of RoCo and Holi modules, CPRN captures the visual details of position reasoning so that the model can achieve more accurate segmentation. To our knowledge, this is the first work that explicitly focuses on position reasoning modeling. We also validate the proposed method on three evaluation datasets. It consistently outperforms existing state-of-the-art methods.
Concealed Object Segmentation with Hierarchical Coherence Modeling
Authors: Fengyang Xiao, Pan Zhang, Chunming He, Runze Hu, Yutao Liu
Date: 2024-01-22 00:00:00
Tasks: Decoder, Image Segmentation, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation, Transparent Object Detection

Concealed object segmentation (COS) is a challenging task that involves localizing and segmenting those concealed objects that are visually blended with their surrounding environments. Despite achieving remarkable success, existing COS segmenters still struggle to achieve complete segmentation results in extremely concealed scenarios. In this paper, we propose a Hierarchical Coherence Modeling (HCM) segmenter for COS, aiming to address this incomplete segmentation limitation. In specific, HCM promotes feature coherence by leveraging the intra-stage coherence and cross-stage coherence modules, exploring feature correlations at both the single-stage and contextual levels. Additionally, we introduce the reversible re-calibration decoder to detect previously undetected parts in low-confidence regions, resulting in further enhancing segmentation performance. Extensive experiments conducted on three COS tasks, including camouflaged object detection, polyp image segmentation, and transparent object detection, demonstrate the promising results achieved by the proposed HCM segmenter.
Boosting Multi-view Stereo with Late Cost Aggregation
Authors: Jiang Wu, Rui Li, Yu Zhu, Wenxun Zhao, Jinqiu Sun, Yanning Zhang
Date: 2024-01-22 00:00:00
Tasks: Blocking, Geometric Matching

Pairwise matching cost aggregation is a crucial step for modern learning-based Multi-view Stereo (MVS). Prior works adopt an early aggregation scheme, which adds up pairwise costs into an intermediate cost. However, we analyze that this process can degrade informative pairwise matchings, thereby blocking the depth network from fully utilizing the original geometric matching cues. To address this challenge, we present a late aggregation approach that allows for aggregating pairwise costs throughout the network feed-forward process, achieving accurate estimations with only minor changes of the plain CasMVSNet. Instead of building an intermediate cost by weighted sum, late aggregation preserves all pairwise costs along a distinct view channel. This enables the succeeding depth network to fully utilize the crucial geometric cues without loss of cost fidelity. Grounded in the new aggregation scheme, we propose further techniques addressing view order dependence inside the preserved cost, handling flexible testing views, and improving the depth filtering process. Despite its technical simplicity, our method improves significantly upon the baseline cascade-based approach, achieving comparable results with state-of-the-art methods with favorable computation overhead.