pwc-archive/papers-with-abstracts huggingface dataset:
EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles
Authors: Zicheng Pan, Xiaohan Yu, Yongsheng Gao
Date: 2024-07-23 00:00:00
Tasks: Domain Adaptation, Fine-Grained Visual Categorization, Source-Free Domain Adaptation

Source-free domain adaptation (SFDA) aims to transfer knowledge from a labelled source domain to an unlabelled target domain. A major challenge in SFDA is deriving accurate categorical information for the target domain, especially when sample embeddings from different classes appear similar. This issue is particularly pronounced in fine-grained visual categorization tasks, where inter-class differences are subtle. To overcome this challenge, we introduce a novel ETF-Informed Attention Network (EIANet) to separate class prototypes by utilizing attention and neural collapse principles. More specifically, EIANet employs a simplex Equiangular Tight Frame (ETF) classifier in conjunction with an attention mechanism, facilitating the model to focus on discriminative features and ensuring maximum class prototype separation. This innovative approach effectively enlarges the feature difference between different classes in the latent space by locating salient regions, thereby preventing the misclassification of similar but distinct category samples and providing more accurate categorical information to guide the fine-tuning process on the target domain. Experimental results across four SFDA datasets validate EIANet's state-of-the-art performance. Code is available at: https://github.com/zichengpan/EIANet.
No Re-Train, More Gain: Upgrading Backbones with Diffusion Model for Few-Shot Segmentation
Authors: Shuai Chen, Fanman Meng, Chenhao Wu, Haoran Wei, Runtong Zhang, Qingbo Wu, Linfeng Xu, Hongliang Li
Date: 2024-07-23 00:00:00

Few-Shot Segmentation (FSS) aims to segment novel classes using only a few annotated images. Despite considerable process under pixel-wise support annotation, current FSS methods still face three issues: the inflexibility of backbone upgrade without re-training, the inability to uniformly handle various types of annotations (e.g., scribble, bounding box, mask and text), and the difficulty in accommodating different annotation quantity. To address these issues simultaneously, we propose DiffUp, a novel FSS method that conceptualizes the FSS task as a conditional generative problem using a diffusion process. For the first issue, we introduce a backbone-agnostic feature transformation module that converts different segmentation cues into unified coarse priors, facilitating seamless backbone upgrade without re-training. For the second issue, due to the varying granularity of transformed priors from diverse annotation types, we conceptualize these multi-granular transformed priors as analogous to noisy intermediates at different steps of a diffusion model. This is implemented via a self-conditioned modulation block coupled with a dual-level quality modulation branch. For the third issue, we incorporates an uncertainty-aware information fusion module that harmonizing the variability across zero-shot, one-shot and many-shot scenarios. Evaluated through rigorous benchmarks, DiffUp significantly outperforms existing FSS models in terms of flexibility and accuracy.
Integrating Meshes and 3D Gaussians for Indoor Scene Reconstruction with SAM Mask Guidance
Authors: Jiyeop Kim, Jongwoo Lim
Date: 2024-07-23 00:00:00
Tasks: 3DGS, Indoor Scene Reconstruction

We present a novel approach for 3D indoor scene reconstruction that combines 3D Gaussian Splatting (3DGS) with mesh representations. We use meshes for the room layout of the indoor scene, such as walls, ceilings, and floors, while employing 3D Gaussians for other objects. This hybrid approach leverages the strengths of both representations, offering enhanced flexibility and ease of editing. However, joint training of meshes and 3D Gaussians is challenging because it is not clear which primitive should affect which part of the rendered image. Objects close to the room layout often struggle during training, particularly when the room layout is textureless, which can lead to incorrect optimizations and unnecessary 3D Gaussians. To overcome these challenges, we employ Segment Anything Model (SAM) to guide the selection of primitives. The SAM mask loss enforces each instance to be represented by either Gaussians or meshes, ensuring clear separation and stable training. Furthermore, we introduce an additional densification stage without resetting the opacity after the standard densification. This stage mitigates the degradation of image quality caused by a limited number of 3D Gaussians after the standard densification.
Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models
Authors: Liheng Jiang, Xuechun yang, Chang Yu, Zhizhong Wu, Yuting Wang
Date: 2024-07-23 00:00:00
Tasks: Decision Making, Diagnostic

Trauma is a significant cause of mortality and disability, particularly among individuals under forty. Traditional diagnostic methods for traumatic injuries, such as X-rays, CT scans, and MRI, are often time-consuming and dependent on medical expertise, which can delay critical interventions. This study explores the application of artificial intelligence (AI) and machine learning (ML) to improve the speed and accuracy of abdominal trauma diagnosis. We developed an advanced AI-based model combining 3D segmentation, 2D Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic performance. Our model processes abdominal CT scans to provide real-time, precise assessments, thereby improving clinical decision-making and patient outcomes. Comprehensive experiments demonstrated that our approach significantly outperforms traditional diagnostic methods, as evidenced by rigorous evaluation metrics. This research sets a new benchmark for automated trauma detection, leveraging the strengths of AI and ML to revolutionize trauma care.
Cross-Domain Separable Translation Network for Multimodal Image Change Detection
Authors: Tao Zhan, Yuanyuan Zhu, Jie Lan, Qianlong Dang
Date: 2024-07-23 00:00:00
Tasks: Change Detection, Translation

In the remote sensing community, multimodal change detection (MCD) is particularly critical due to its ability to track changes across different imaging conditions and sensor types, making it highly applicable to a wide range of real-world scenarios. This paper focuses on addressing the challenges of MCD, especially the difficulty in comparing images from different sensors with varying styles and statistical characteristics of geospatial objects. Traditional MCD methods often struggle with these variations, leading to inaccurate and unreliable results. To overcome these limitations, a novel unsupervised cross-domain separable translation network (CSTN) is proposed, which uniquely integrates a within-domain self-reconstruction and a cross-domain image translation and cycle-reconstruction workflow with change detection constraints. The model is optimized by implementing both the tasks of image translation and MCD simultaneously, thereby guaranteeing the comparability of learned features from multimodal images. Specifically, a simple yet efficient dual-branch convolutional architecture is employed to separate the content and style information of multimodal images. This process generates a style-independent content-comparable feature space, which is crucial for achieving accurate change detection even in the presence of significant sensor variations. Extensive experimental results demonstrate the effectiveness of the proposed method, showing remarkable improvements over state-of-the-art approaches in terms of accuracy and efficacy for MCD. The implementation of our method will be publicly available at \url{https://github.com/OMEGA-RS/CSTN}
Improved Few-Shot Image Classification Through Multiple-Choice Questions
Authors: Dipika Khullar, Emmett Goodman, Negin Sokhandan
Date: 2024-07-23 00:00:00
Tasks: Articles, Few-Shot Image Classification, image-classification, Image Classification, Multiple-choice, Visual Question Answering (VQA)

Through a simple multiple choice language prompt a VQA model can operate as a zero-shot image classifier, producing a classification label. Compared to typical image encoders, VQA models offer an advantage: VQA-produced image embeddings can be infused with the most relevant visual information through tailored language prompts. Nevertheless, for most tasks, zero-shot VQA performance is lacking, either because of unfamiliar category names, or dissimilar pre-training data and test data distributions. We propose a simple method to boost VQA performance for image classification using only a handful of labeled examples and a multiple-choice question. This few-shot method is training-free and maintains the dynamic and flexible advantages of the VQA model. Rather than relying on the final language output, our approach uses multiple-choice questions to extract prompt-specific latent representations, which are enriched with relevant visual information. These representations are combined to create a final overall image embedding, which is decoded via reference to latent class prototypes constructed from the few labeled examples. We demonstrate this method outperforms both pure visual encoders and zero-shot VQA baselines to achieve impressive performance on common few-shot tasks including MiniImageNet, Caltech-UCSD Birds, and CIFAR-100. Finally, we show our approach does particularly well in settings with numerous diverse visual attributes such as the fabric, article-style, texture, and view of different articles of clothing, where other few-shot approaches struggle, as we can tailor our image representations only on the semantic features of interest.
3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images
Authors: Jie Zhao, Jianing Li, Weihan Chen, Wentong Wang, Pengfei Yuan, Xu Zhang, Deshu Peng
Date: 2024-07-23 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

Human pose estimation remains a multifaceted challenge in computer vision, pivotal across diverse domains such as behavior recognition, human-computer interaction, and pedestrian tracking. This paper proposes an improved method based on the spatial-temporal graph convolution net-work (UGCN) to address the issue of missing human posture skeleton sequences in single-view videos. We present the improved UGCN, which allows the network to process 3D human pose data and improves the 3D human pose skeleton sequence, thereby resolving the occlusion issue.
Open-Set Biometrics: Beyond Good Closed-Set Models
Authors: Yiyang Su, Minchul Kim, Feng Liu, Anil Jain, Xiaoming Liu
Date: 2024-07-23 00:00:00
Tasks: Face Recognition, Gait Recognition, Person Re-Identification

Biometric recognition has primarily addressed closed-set identification, assuming all probe subjects are in the gallery. However, most practical applications involve open-set biometrics, where probe subjects may or may not be present in the gallery. This poses distinct challenges in effectively distinguishing individuals in the gallery while minimizing false detections. While it is commonly believed that powerful biometric models can excel in both closed- and open-set scenarios, existing loss functions are inconsistent with open-set evaluation. They treat genuine (mated) and imposter (non-mated) similarity scores symmetrically and neglect the relative magnitudes of imposter scores. To address these issues, we simulate open-set evaluation using minibatches during training and introduce novel loss functions: (1) the identification-detection loss optimized for open-set performance under selective thresholds and (2) relative threshold minimization to reduce the maximum negative score for each probe. Across diverse biometric tasks, including face recognition, gait recognition, and person re-identification, our experiments demonstrate the effectiveness of the proposed loss functions, significantly enhancing open-set performance while positively impacting closed-set performance. Our code and models are available at https://github.com/prevso1088/open-set-biometrics.
MxT: Mamba x Transformer for Image Inpainting
Authors: Shuang Chen, Amir Atapour-Abarghouei, Haozheng Zhang, Hubert P. H. Shum
Date: 2024-07-23 00:00:00
Tasks: Image Inpainting, Mamba

Image inpainting, or image completion, is a crucial task in computer vision that aims to restore missing or damaged regions of images with semantically coherent content. This technique requires a precise balance of local texture replication and global contextual understanding to ensure the restored image integrates seamlessly with its surroundings. Traditional methods using Convolutional Neural Networks (CNNs) are effective at capturing local patterns but often struggle with broader contextual relationships due to the limited receptive fields. Recent advancements have incorporated transformers, leveraging their ability to understand global interactions. However, these methods face computational inefficiencies and struggle to maintain fine-grained details. To overcome these challenges, we introduce MxT composed of the proposed Hybrid Module (HM), which combines Mamba with the transformer in a synergistic manner. Mamba is adept at efficiently processing long sequences with linear computational costs, making it an ideal complement to the transformer for handling long-scale data interactions. Our HM facilitates dual-level interaction learning at both pixel and patch levels, greatly enhancing the model to reconstruct images with high quality and contextual accuracy. We evaluate MxT on the widely-used CelebA-HQ and Places2-standard datasets, where it consistently outperformed existing state-of-the-art methods. The code will be released: {\url{https://github.com/ChrisChen1023/MxT}}.
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
Authors: Sojin Lee, Dogyun Park, Inho Kong, Hyunwoo J. Kim
Date: 2024-07-23 00:00:00
Tasks: Colorization, Deblurring, Denoising, Image Colorization, Image Deblurring, Image Inpainting, Image Restoration, Image Super-Resolution, Super-Resolution, Variational Inference

Recent studies on inverse problems have proposed posterior samplers that leverage the pre-trained diffusion models as powerful priors. These attempts have paved the way for using diffusion models in a wide range of inverse problems. However, the existing methods entail computationally demanding iterative sampling procedures and optimize a separate solution for each measurement, which leads to limited scalability and lack of generalization capability across unseen samples. To address these limitations, we propose a novel approach, Diffusion prior-based Amortized Variational Inference (DAVI) that solves inverse problems with a diffusion prior from an amortized variational inference perspective. Specifically, instead of separate measurement-wise optimization, our amortized inference learns a function that directly maps measurements to the implicit posterior distributions of corresponding clean data, enabling a single-step posterior sampling even for unseen measurements. Extensive experiments on image restoration tasks, e.g., Gaussian deblur, 4$\times$ super-resolution, and box inpainting with two benchmark datasets, demonstrate our approach's superior performance over strong baselines. Code is available at https://github.com/mlvlab/DAVI.
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
Authors: Jiahe Liu, Youran Qu, Qi Yan, Xiaohui Zeng, Lele Wang, Renjie Liao
Date: 2024-07-23 00:00:00
Tasks: Image Generation, Point Tracking, Video Generation, Video Quality Assessment, Visual Question Answering (VQA)

Significant advancements have been made in video generative models recently. Unlike image generation, video generation presents greater challenges, requiring not only generating high-quality frames but also ensuring temporal consistency across these frames. Despite the impressive progress, research on metrics for evaluating the quality of generated videos, especially concerning temporal and motion consistency, remains underexplored. To bridge this research gap, we propose Fr\'echet Video Motion Distance (FVMD) metric, which focuses on evaluating motion consistency in video generation. Specifically, we design explicit motion features based on key point tracking, and then measure the similarity between these features via the Fr\'echet distance. We conduct sensitivity analysis by injecting noise into real videos to verify the effectiveness of FVMD. Further, we carry out a large-scale human study, demonstrating that our metric effectively detects temporal noise and aligns better with human perceptions of generated video quality than existing metrics. Additionally, our motion features can consistently improve the performance of Video Quality Assessment (VQA) models, indicating that our approach is also applicable to unary video quality evaluation. Code is available at https://github.com/ljh0v0/FMD-frechet-motion-distance.
Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision
Authors: Aditya Krishnan, Jayneel Vora, Prasant Mohapatra
Date: 2024-07-23 00:00:00
Tasks: 2D Semantic Segmentation, 3D Semantic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Semantic segmentation has emerged as a pivotal area of study in computer vision, offering profound implications for scene understanding and elevating human-machine interactions across various domains. While 2D semantic segmentation has witnessed significant strides in the form of lightweight, high-precision models, transitioning to 3D semantic segmentation poses distinct challenges. Our research focuses on achieving efficiency and lightweight design for 3D semantic segmentation models, similar to those achieved for 2D models. Such a design impacts applications of 3D semantic segmentation where memory and latency are of concern. This paper introduces a novel approach to 3D semantic segmentation, distinguished by incorporating a hybrid blend of 2D and 3D computer vision techniques, enabling a streamlined, efficient process. We conduct 2D semantic segmentation on RGB images linked to 3D point clouds and extend the results to 3D using an extrusion technique for specific class labels, reducing the point cloud subspace. We perform rigorous evaluations with the DeepViewAgg model on the complete point cloud as our baseline by measuring the Intersection over Union (IoU) accuracy, inference time latency, and memory consumption. This model serves as the current state-of-the-art 3D semantic segmentation model on the KITTI-360 dataset. We can achieve heightened accuracy outcomes, surpassing the baseline for 6 out of the 15 classes while maintaining a marginal 1% deviation below the baseline for the remaining class labels. Our segmentation approach demonstrates a 1.347x speedup and about a 43% reduced memory usage compared to the baseline.
PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips
Authors: Håkon Maric Solberg, Mehdi Houshmand Sarkhoosh, Sushant Gautam, Saeed Shafiee Sabet, Pål Halvorsen, Cise Midoglu
Date: 2024-07-22 00:00:00
Tasks: object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR), Sports Analytics

In the rapidly evolving field of sports analytics, the automation of targeted video processing is a pivotal advancement. We propose PlayerTV, an innovative framework which harnesses state-of-the-art AI technologies for automatic player tracking and identification in soccer videos. By integrating object detection and tracking, Optical Character Recognition (OCR), and color analysis, PlayerTV facilitates the generation of player-specific highlight clips from extensive game footage, significantly reducing the manual labor traditionally associated with such tasks. Preliminary results from the evaluation of our core pipeline, tested on a dataset from the Norwegian Eliteserien league, indicate that PlayerTV can accurately and efficiently identify teams and players, and our interactive Graphical User Interface (GUI) serves as a user-friendly application wrapping this functionality for streamlined use.
Pavement Fatigue Crack Detection and Severity Classification Based on Convolutional Neural Network
Authors: Zhen Wang, Dylan G. Ildefonzo, Linbing Wang
Date: 2024-07-22 00:00:00
Tasks: Classification, image-classification, Image Classification

Due to the varying intensity of pavement cracks, the complexity of topological structure, and the noise of texture background, image classification for asphalt pavement cracking has proven to be a challenging problem. Fatigue cracking, also known as alligator cracking, is one of the common distresses of asphalt pavement. It is thus important to detect and monitor the condition of alligator cracking on roadway pavements. Most research in this area has typically focused on pixel-level detection of cracking using limited datasets. A novel deep convolutional neural network that can achieve two objectives is proposed. The first objective of the proposed neural network is to classify presence of fatigue cracking based on pavement surface images. The second objective is to classify the fatigue cracking severity level based on the Distress Identification Manual (DIM) standard. In this paper, a databank of 4484 high-resolution pavement surface images is established in which images are taken locally in the Town of Blacksburg, Virginia, USA. In the data pre-preparation, over 4000 images are labeled into 4 categories manually according to DIM standards. A four-layer convolutional neural network model is then built to achieve the goal of classification of images by pavement crack severity category. The trained model reached the highest accuracy among all existing methods. After only 30 epochs of training, the model achieved a crack existence classification accuracy of 96.23% and a severity level classification accuracy of 96.74%. After 20 epochs of training, the model achieved a pavement marking presence classification accuracy of 97.64%.
Wallcamera: Reinventing the Wheel?
Authors: Aurélien Bourquard, Jeff Yan
Date: 2024-07-22 00:00:00
Tasks: Activity Recognition, Face Swapping

Developed at MIT CSAIL, the Wallcamera has captivated the public's imagination. Here, we show that the key insight underlying the Wallcamera is the same one that underpins the concept and the prototype of differential imaging forensics (DIF), both of which were validated and reported several years prior to the Wallcamera's debut. Rather than being the first to extract and amplify invisible signals -- aka latent evidence in the forensics context -- from wall reflections in a video, or the first to propose activity recognition following that approach, the Wallcamera's actual innovation is achieving activity recognition at a finer granularity than DIF demonstrated. In addition to activity recognition, DIF as conceived has a number of other applications in forensics, including 1) the recovery of a photographer's personal identifiable information such as body width, height, and even the color of their clothing, from a single photo, and 2) the detection of image tampering and deepfake videos.