pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection
Authors: Siyuan Yao, Hao Sun, Tian-Zhu Xiang, Xiao Wang, Xiaochun Cao
Date: 2024-08-27 00:00:00
Tasks: Decoder, object-detection, Object Detection

Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e. COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods. Our code is available at https://github.com/Garyson1204/HGINet.
Alternating Minimization Schemes for Computing Rate-Distortion-Perception Functions with $f$-Divergence Perception Constraints
Authors: Giuseppe Serra, Photios A. Stavrou, Marios Kountouris
Date: 2024-08-27 00:00:00

We study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint that belongs to the family of $f$-divergences. In this setting, the RDPF forms a convex programming problem for which we characterize the optimal parametric solutions. We employ the developed solutions in an alternating minimization scheme, namely Optimal Alternating Minimization (OAM), for which we provide convergence guarantees. Nevertheless, the OAM scheme does not lead to a direct implementation of a generalized Blahut-Arimoto (BA) type of algorithm due to the presence of implicit equations in the structure of the iteration. To overcome this difficulty, we propose two alternative minimization approaches whose applicability depends on the smoothness of the used perception metric: a Newton-based Alternating Minimization (NAM) scheme, relying on Newton's root-finding method for the approximation of the optimal iteration solution, and a Relaxed Alternating Minimization (RAM) scheme, based on a relaxation of the OAM iterates. Both schemes are shown, via the derivation of necessary and sufficient conditions, to guarantee convergence to a globally optimal solution. We also provide sufficient conditions on the distortion and the perception constraints which guarantee that the proposed algorithms converge exponentially fast in the number of iteration steps. We corroborate our theoretical results with numerical simulations and draw connections with existing results.
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training
Authors: Xingliang Lei, Yiwen Ye, Ziyang Chen, Minglei Shu, Yong Xia
Date: 2024-08-27 00:00:00
Tasks: Medical Image Analysis, parameter-efficient fine-tuning, Self-Supervised Learning

Parameter-efficient fine-tuning (PEFT) techniques have emerged to address issues of overfitting and high computational costs associated with fully fine-tuning in the paradigm of self-supervised learning. Mainstream methods based on PEFT involve adding a few trainable parameters while keeping the pre-trained parameters of the backbone fixed. These methods achieve comparative, and often superior, performance to fully fine-tuning, demonstrating the powerful representation ability of the pre-trained backbone. Despite its success, these methods typically ignore the initialization of the new parameters, often relying solely on random initialization. We argue that if pre-training is significantly beneficial, it should be applied to all parameters requiring representational capacity. Motivated by this insight, we propose a simple yet effective fine-tuning framework based on Target Parameter Pre-training (TPP). The target parameters refer to the new parameters introduced during fine-tuning. TPP includes an additional stage before PEFT to pre-train these target parameters. During this stage, the pre-trained backbone parameters are frozen, and only the target parameters are trainable. A defined pre-text task is used to encourage the target parameters to learn specific representations of downstream data. When PEFT is subsequently employed, the pre-trained target parameters are loaded to enhance fine-tuning efficiency. The proposed TPP framework is versatile, allowing for the integration of various pretext tasks for pre-training and supporting different PEFT methods as backbones. We evaluated the fine-tining performance of our method using five public datasets, including three modalities and two task types. The results demonstrate that the proposed TPP can be easily integrated into existing PEFT methods, significantly improving performance.
Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation
Authors: Elona Shatri, George Fazekas
Date: 2024-08-27 00:00:00
Tasks: Information Retrieval, Instance Segmentation, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR), Retrieval, Segmentation, Semantic Segmentation

Optical Music Recognition (OMR) automates the transcription of musical notation from images into machine-readable formats like MusicXML, MEI, or MIDI, significantly reducing the costs and time of manual transcription. This study explores knowledge discovery in OMR by applying instance segmentation using Mask R-CNN to enhance the detection and delineation of musical symbols in sheet music. Unlike Optical Character Recognition (OCR), OMR must handle the intricate semantics of Common Western Music Notation (CWMN), where symbol meanings depend on shape, position, and context. Our approach leverages instance segmentation to manage the density and overlap of musical symbols, facilitating more precise information retrieval from music scores. Evaluations on the DoReMi and MUSCIMA++ datasets demonstrate substantial improvements, with our method achieving a mean Average Precision (mAP) of up to 59.70\% in dense symbol environments, achieving comparable results to object detection. Furthermore, using traditional computer vision techniques, we add a parallel step for staff detection to infer the pitch for the recognised symbols. This study emphasises the role of pixel-wise segmentation in advancing accurate music symbol recognition, contributing to knowledge discovery in OMR. Our findings indicate that instance segmentation provides more precise representations of musical symbols, particularly in densely populated scores, advancing OMR technology. We make our implementation, pre-processing scripts, trained models, and evaluation results publicly available to support further research and development.
FastTextSpotter: A High-Efficiency Transformer for Multilingual Scene Text Spotting
Authors: Alloy Das, Sanket Biswas, Umapada Pal, Josep Lladós, Saumik Bhattacharya
Date: 2024-08-27 00:00:00
Tasks: Benchmarking, Decoder, Optical Character Recognition, Optical Character Recognition (OCR), Text Spotting

The proliferation of scene text in both structured and unstructured environments presents significant challenges in optical character recognition (OCR), necessitating more efficient and robust text spotting solutions. This paper presents FastTextSpotter, a framework that integrates a Swin Transformer visual backbone with a Transformer Encoder-Decoder architecture, enhanced by a novel, faster self-attention unit, SAC2, to improve processing speeds while maintaining accuracy. FastTextSpotter has been validated across multiple datasets, including ICDAR2015 for regular texts and CTW1500 and TotalText for arbitrary-shaped texts, benchmarking against current state-of-the-art models. Our results indicate that FastTextSpotter not only achieves superior accuracy in detecting and recognizing multilingual scene text (English and Vietnamese) but also improves model efficiency, thereby setting new benchmarks in the field. This study underscores the potential of advanced transformer architectures in improving the adaptability and speed of text spotting applications in diverse real-world settings. The dataset, code, and pre-trained models have been released in our Github.
Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover
Authors: Ran Yu, Haixin Yu, Shoujie Li, Huang Yan, Ziwu Song, Wenbo Ding
Date: 2024-08-27 00:00:00
Tasks: Depth Estimation, Transparent objects

Transparent objects are common in daily life, while their optical properties pose challenges for RGB-D cameras to capture accurate depth information. This issue is further amplified when these objects are hand-held, as hand occlusions further complicate depth estimation. For assistant robots, however, accurately perceiving hand-held transparent objects is critical to effective human-robot interaction. This paper presents a Hand-Aware Depth Restoration (HADR) method based on creating an implicit neural representation function from a single RGB-D image. The proposed method utilizes hand posture as an important guidance to leverage semantic and geometric information of hand-object interaction. To train and evaluate the proposed method, we create a high-fidelity synthetic dataset named TransHand-14K with a real-to-sim data generation scheme. Experiments show that our method has better performance and generalization ability compared with existing methods. We further develop a real-world human-to-robot handover system based on HADR, demonstrating its potential in human-robot interaction applications.
LN-Gen: Rectal Lymph Nodes Generation via Anatomical Features
Authors: Weidong Guo, Hantao Zhang, Shouhong Wan, Bingbing Zou, Wanqin Wang, Peiquan Jin
Date: 2024-08-27 00:00:00

Accurate segmentation of rectal lymph nodes is crucial for the staging and treatment planning of rectal cancer. However, the complexity of the surrounding anatomical structures and the scarcity of annotated data pose significant challenges. This study introduces a novel lymph node synthesis technique aimed at generating diverse and realistic synthetic rectal lymph node samples to mitigate the reliance on manual annotation. Unlike direct diffusion methods, which often produce masks that are discontinuous and of suboptimal quality, our approach leverages an implicit SDF-based method for mask generation, ensuring the production of continuous, stable, and morphologically diverse masks. Experimental results demonstrate that our synthetic data significantly improves segmentation performance. Our work highlights the potential of diffusion model for accurately synthesizing structurally complex lesions, such as lymph nodes in rectal cancer, alleviating the challenge of limited annotated data in this field and aiding in advancements in rectal cancer diagnosis and treatment.
MegActor-$Σ$: Unlocking Flexible Mixed-Modal Control in Portrait Animation with Diffusion Transformer
Authors: Shurong Yang, Huadong Li, Juhao Wu, Minhao Jing, Linze Li, Renhe Ji, Jiajun Liang, Haoqiang Fan, Jin Wang
Date: 2024-08-27 00:00:00
Tasks: Portrait Animation

Diffusion models have demonstrated superior performance in the field of portrait animation. However, current approaches relied on either visual or audio modality to control character movements, failing to exploit the potential of mixed-modal control. This challenge arises from the difficulty in balancing the weak control strength of audio modality and the strong control strength of visual modality. To address this issue, we introduce MegActor-$\Sigma$: a mixed-modal conditional diffusion transformer (DiT), which can flexibly inject audio and visual modality control signals into portrait animation. Specifically, we make substantial advancements over its predecessor, MegActor, by leveraging the promising model structure of DiT and integrating audio and visual conditions through advanced modules within the DiT framework. To further achieve flexible combinations of mixed-modal control signals, we propose a ``Modality Decoupling Control" training strategy to balance the control strength between visual and audio modalities, along with the ``Amplitude Adjustment" inference strategy to freely regulate the motion amplitude of each modality. Finally, to facilitate extensive studies in this field, we design several dataset evaluation metrics to filter out public datasets and solely use this filtered dataset to train MegActor-$\Sigma$. Extensive experiments demonstrate the superiority of our approach in generating vivid portrait animations, outperforming previous methods trained on private dataset.
Deep Learning-based Average Shear Wave Velocity Prediction using Accelerometer Records
Authors: Barış Yılmaz, Melek Türkmen, Sanem Meral, Erdem Akagündüz, Salih Tileylioglu
Date: 2024-08-27 00:00:00

Assessing seismic hazards and thereby designing earthquake-resilient structures or evaluating structural damage that has been incurred after an earthquake are important objectives in earthquake engineering. Both tasks require critical evaluation of strong ground motion records, and the knowledge of site conditions at the earthquake stations plays a major role in achieving the aforementioned objectives. Site conditions are generally represented by the time-averaged shear wave velocity in the upper 30 meters of the geological materials (Vs30). Several strong motion stations lack Vs30 measurements resulting in potentially inaccurate assessment of seismic hazards and evaluation of ground motion records. In this study, we present a deep learning-based approach for predicting Vs30 at strong motion station locations using three-channel earthquake records. For this purpose, Convolutional Neural Networks (CNNs) with dilated and causal convolutional layers are used to extract deep features from accelerometer records collected from over 700 stations located in Turkey. In order to overcome the limited availability of labeled data, we propose a two-phase training approach. In the first phase, a CNN is trained to estimate the epicenters, for which ground truth is available for all records. After the CNN is trained, the pre-trained encoder is fine-tuned based on the Vs30 ground truth. The performance of the proposed method is compared with machine learning models that utilize hand-crafted features. The results demonstrate that the deep convolutional encoder based Vs30 prediction model outperforms the machine learning models that rely on hand-crafted features.
Applying ViT in Generalized Few-shot Semantic Segmentation
Authors: Liyuan Geng, Jinhong Xia, Yuanhe Guo
Date: 2024-08-27 00:00:00
Tasks: Decoder, Few-Shot Semantic Segmentation, Generalized Few-Shot Semantic Segmentation, Segmentation, Semantic Segmentation

This paper explores the capability of ViT-based models under the generalized few-shot semantic segmentation (GFSS) framework. We conduct experiments with various combinations of backbone models, including ResNets and pretrained Vision Transformer (ViT)-based models, along with decoders featuring a linear classifier, UPerNet, and Mask Transformer. The structure made of DINOv2 and linear classifier takes the lead on popular few-shot segmentation bench mark PASCAL-$5^i$, substantially outperforming the best of ResNet structure by 116% in one-shot scenario. We demonstrate the great potential of large pretrained ViT-based model on GFSS task, and expect further improvement on testing benchmarks. However, a potential caveat is that when applying pure ViT-based model and large scale ViT decoder, the model is easy to overfit.
ERX: A Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line Scanning
Authors: Samuel Garske, Bradley Evans, Christopher Artlett, KC Wong
Date: 2024-08-27 00:00:00
Tasks: Anomaly Detection, CPU, Edge-computing

Detecting unexpected objects (anomalies) in real time has great potential for monitoring, managing, and protecting the environment. Hyperspectral line-scan cameras are a low-cost solution that enhance confidence in anomaly detection over RGB and multispectral imagery. However, existing line-scan algorithms are too slow when using small computers (e.g. those onboard a drone or small satellite), do not adapt to changing scenery, or lack robustness against geometric distortions. This paper introduces the Exponentially moving RX algorithm (ERX) to address these issues, and compares it with four existing RX-based anomaly detection methods for hyperspectral line scanning. Three large and more complex datasets are also introduced to better assess the practical challenges when using line-scan cameras (two hyperspectral and one multispectral). ERX was evaluated using a Jetson Xavier NX edge computing module (6-core CPU, 8GB RAM, 20W power draw), achieving the best combination of speed and detection performance. ERX was 9 times faster than the next-best algorithm on the dataset with the highest number of bands (108 band), with an average speed of 561 lines per second on the Jetson. It achieved a 29.3% AUC improvement over the next-best algorithm on the most challenging dataset, while showing greater adaptability through consistently high AUC scores regardless of the camera's starting location. ERX performed robustly across all datasets, achieving an AUC of 0.941 on a drone-collected hyperspectral line scan dataset without geometric corrections (a 16.9% improvement over existing algorithms). This work enables future research on the detection of anomalous objects in real time, adaptive and automatic threshold selection, and real-time field tests. The datasets and the Python code are openly available at: https://github.com/WiseGamgee/HyperAD, promoting accessibility and future work.
BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization
Authors: Mario A. V. Saucedo, Nikolaos Stathoulopoulos, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos
Date: 2024-08-27 00:00:00
Tasks: 3D Object Detection, Benchmarking, object-detection, Object Detection, Scene Understanding

Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This article proposes BOX3D, a novel multi-modal and lightweight scheme for localizing objects of interest by fusing the information from RGB camera and 3D LiDAR. BOX3D is structured around a three-layered architecture, building up from the local perception of the incoming sequential sensor data to the global perception refinement that covers for outliers and the general consistency of each object's observation. More specifically, the first layer handles the low-level fusion of camera and LiDAR data for initial 3D bounding box extraction. The second layer converts each LiDAR's scan 3D bounding boxes to the world coordinate frame and applies a spatial pairing and merging mechanism to maintain the uniqueness of objects observed from different viewpoints. Finally, BOX3D integrates the third layer that supervises the consistency of the results on the global map iteratively, using a point-to-voxel comparison for identifying all points in the global map that belong to the object. Benchmarking results of the proposed novel architecture are showcased in multiple experimental trials on public state-of-the-art large-scale dataset of urban environments.
CMTA: Cross-Modal Temporal Alignment for Event-guided Video Deblurring
Authors: Taewoo Kim, Hoonhee Cho, Kuk-Jin Yoon
Date: 2024-08-27 00:00:00
Tasks: Deblurring, Video Deblurring

Video deblurring aims to enhance the quality of restored results in motion-blurred videos by effectively gathering information from adjacent video frames to compensate for the insufficient data in a single blurred frame. However, when faced with consecutively severe motion blur situations, frame-based video deblurring methods often fail to find accurate temporal correspondence among neighboring video frames, leading to diminished performance. To address this limitation, we aim to solve the video deblurring task by leveraging an event camera with micro-second temporal resolution. To fully exploit the dense temporal resolution of the event camera, we propose two modules: 1) Intra-frame feature enhancement operates within the exposure time of a single blurred frame, iteratively enhancing cross-modality features in a recurrent manner to better utilize the rich temporal information of events, 2) Inter-frame temporal feature alignment gathers valuable long-range temporal information to target frames, aggregating sharp features leveraging the advantages of the events. In addition, we present a novel dataset composed of real-world blurred RGB videos, corresponding sharp videos, and event data. This dataset serves as a valuable resource for evaluating event-guided deblurring methods. We demonstrate that our proposed methods outperform state-of-the-art frame-based and event-based motion deblurring methods through extensive experiments conducted on both synthetic and real-world deblurring datasets. The code and dataset are available at https://github.com/intelpro/CMTA.
Automatic Detection of COVID-19 from Chest X-ray Images Using Deep Learning Model
Authors: Alloy Das, Rohit Agarwal, Rituparna Singh, Arindam Chowdhury, Debashis Nandi
Date: 2024-08-27 00:00:00
Tasks: Diagnostic

The infectious disease caused by novel corona virus (2019-nCoV) has been widely spreading since last year and has shaken the entire world. It has caused an unprecedented effect on daily life, global economy and public health. Hence this disease detection has life-saving importance for both patients as well as doctors. Due to limited test kits, it is also a daunting task to test every patient with severe respiratory problems using conventional techniques (RT-PCR). Thus implementing an automatic diagnosis system is urgently required to overcome the scarcity problem of Covid-19 test kits at hospital, health care systems. The diagnostic approach is mainly classified into two categories-laboratory based and Chest radiography approach. In this paper, a novel approach for computerized corona virus (2019-nCoV) detection from lung x-ray images is presented. Here, we propose models using deep learning to show the effectiveness of diagnostic systems. In the experimental result, we evaluate proposed models on publicly available data-set which exhibit satisfactory performance and promising results compared with other previous existing methods.