pwc-archive/papers-with-abstracts huggingface dataset:
MLtoGAI: Semantic Web based with Machine Learning for Enhanced Disease Prediction and Personalized Recommendations using Generative AI
Authors: Shyam Dongre, Ritesh Chandra, Sonali Agarwal
Date: 2024-07-26 00:00:00
Tasks: Diagnostic, Disease Prediction, Prediction

In modern healthcare, addressing the complexities of accurate disease prediction and personalized recommendations is both crucial and challenging. This research introduces MLtoGAI, which integrates Semantic Web technology with Machine Learning (ML) to enhance disease prediction and offer user-friendly explanations through ChatGPT. The system comprises three key components: a reusable disease ontology that incorporates detailed knowledge about various diseases, a diagnostic classification model that uses patient symptoms to detect specific diseases accurately, and the integration of Semantic Web Rule Language (SWRL) with ontology and ChatGPT to generate clear, personalized health advice. This approach significantly improves prediction accuracy and ensures results that are easy to understand, addressing the complexity of diseases and diverse symptoms. The MLtoGAI system demonstrates substantial advancements in accuracy and user satisfaction, contributing to developing more intelligent and accessible healthcare solutions. This innovative approach combines the strengths of ML algorithms with the ability to provide transparent, human-understandable explanations through ChatGPT, achieving significant improvements in prediction accuracy and user comprehension. By leveraging semantic technology and explainable AI, the system enhances the accuracy of disease prediction and ensures that the recommendations are relevant and easily understood by individual patients. Our research highlights the potential of integrating advanced technologies to overcome existing challenges in medical diagnostics, paving the way for future developments in intelligent healthcare systems. Additionally, the system is validated using 200 synthetic patient data records, ensuring robust performance and reliability.
Matting by Generation
Authors: Zhixiang Wang, Baiang Li, Jian Wang, Yu-Lun Liu, Jinwei Gu, Yung-Yu Chuang, Shin'ichi Satoh
Date: 2024-07-30 00:00:00
Tasks: Image Matting

This paper introduces an innovative approach for image matting that redefines the traditional regression-based task as a generative modeling challenge. Our method harnesses the capabilities of latent diffusion models, enriched with extensive pre-trained knowledge, to regularize the matting process. We present novel architectural innovations that empower our model to produce mattes with superior resolution and detail. The proposed method is versatile and can perform both guidance-free and guidance-based image matting, accommodating a variety of additional cues. Our comprehensive evaluation across three benchmark datasets demonstrates the superior performance of our approach, both quantitatively and qualitatively. The results not only reflect our method's robust effectiveness but also highlight its ability to generate visually compelling mattes that approach photorealistic quality. The project page for this paper is available at https://lightchaserx.github.io/matting-by-generation/
Add-SD: Rational Generation without Manual Reference
Authors: Lingfeng Yang, Xinyu Zhang, Xiang Li, Jinwen Chen, Kun Yao, Gang Zhang, Errui Ding, Lingqiao Liu, Jingdong Wang, Jian Yang
Date: 2024-07-30 00:00:00

Diffusion models have exhibited remarkable prowess in visual generalization. Building on this success, we introduce an instruction-based object addition pipeline, named Add-SD, which automatically inserts objects into realistic scenes with rational sizes and positions. Different from layout-conditioned methods, Add-SD is solely conditioned on simple text prompts rather than any other human-costly references like bounding boxes. Our work contributes in three aspects: proposing a dataset containing numerous instructed image pairs; fine-tuning a diffusion model for rational generation; and generating synthetic data to boost downstream tasks. The first aspect involves creating a RemovalDataset consisting of original-edited image pairs with textual instructions, where an object has been removed from the original image while maintaining strong pixel consistency in the background. These data pairs are then used for fine-tuning the Stable Diffusion (SD) model. Subsequently, the pretrained Add-SD model allows for the insertion of expected objects into an image with good rationale. Additionally, we generate synthetic instances for downstream task datasets at scale, particularly for tail classes, to alleviate the long-tailed problem. Downstream tasks benefit from the enriched dataset with enhanced diversity and rationale. Experiments on LVIS val demonstrate that Add-SD yields an improvement of 4.3 mAP on rare classes over the baseline. Code and models are available at https://github.com/ylingfeng/Add-SD.
Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection
Authors: Jinfa Huang, Jinsheng Pan, Zhongwei Wan, Hanjia Lyu, Jiebo Luo
Date: 2024-07-30 00:00:00
Tasks: Attribute

Recent advances show that two-stream approaches have achieved outstanding performance in hateful meme detection. However, hateful memes constantly evolve as new memes emerge by fusing progressive cultural ideas, making existing methods obsolete or ineffective. In this work, we explore the potential of Large Multimodal Models (LMMs) for hateful meme detection. To this end, we propose Evolver, which incorporates LMMs via Chain-of-Evolution (CoE) Prompting, by integrating the evolution attribute and in-context information of memes. Specifically, Evolver simulates the evolving and expressing process of memes and reasons through LMMs in a step-by-step manner. First, an evolutionary pair mining module retrieves the top-k most similar memes in the external curated meme set with the input meme. Second, an evolutionary information extractor is designed to summarize the semantic regularities between the paired memes for prompting. Finally, a contextual relevance amplifier enhances the in-context hatefulness information to boost the search for evolutionary processes. Extensive experiments on public FHM, MAMI, and HarM datasets show that CoE prompting can be incorporated into existing LMMs to improve their performance. More encouragingly, it can serve as an interpretive tool to promote the understanding of the evolution of social memes. [Homepage] (https://github.com/inFaaa/Evolver)
PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter
Authors: Pujan Paudel, Chen Ling, Jeremy Blackburn, Gianluca Stringhini
Date: 2024-07-30 00:00:00
Tasks: Misinformation, Optical Character Recognition, Optical Character Recognition (OCR)

Images are a powerful and immediate vehicle to carry misleading or outright false messages, yet identifying image-based misinformation at scale poses unique challenges. In this paper, we present PIXELMOD, a system that leverages perceptual hashes, vector databases, and optical character recognition (OCR) to efficiently identify images that are candidates to receive soft moderation labels on Twitter. We show that PIXELMOD outperforms existing image similarity approaches when applied to soft moderation, with negligible performance overhead. We then test PIXELMOD on a dataset of tweets surrounding the 2020 US Presidential Election, and find that it is able to identify visually misleading images that are candidates for soft moderation with 0.99% false detection and 2.06% false negatives.
MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions
Authors: Xiaowei Chi, Yatian Wang, Aosong Cheng, Pengjun Fang, Zeyue Tian, Yingqing He, Zhaoyang Liu, Xingqun Qi, Jiahao Pan, Rongyu Zhang, Mengfei Li, Ruibin Yuan, Yanbing Jiang, Wei Xue, Wenhan Luo, Qifeng Chen, Shanghang Zhang, Qifeng Liu, Yike Guo
Date: 2024-07-30 00:00:00
Tasks: Audio Generation, Image to Video Generation, Language Modelling, Text-to-Video Generation

Massive multi-modality datasets play a significant role in facilitating the success of large video-language models. However, current video-language datasets primarily provide text descriptions for visual frames, considering audio to be weakly related information. They usually overlook exploring the potential of inherent audio-visual correlation, leading to monotonous annotation within each modality instead of comprehensive and precise descriptions. Such ignorance results in the difficulty of multiple cross-modality studies. To fulfill this gap, we present MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions, and 2M high-quality clips with multimodal captions. Trailers preview full-length video works and integrate context, visual frames, and background music. In particular, the trailer has two main advantages: (1) the topics are diverse, and the content characters are of various types, e.g., film, news, and gaming. (2) the corresponding background music is custom-designed, making it more coherent with the visual context. Upon these insights, we propose a systemic captioning framework, achieving various modality annotations with more than 27.1k hours of trailer videos. Here, to ensure the caption retains music perspective while preserving the authority of visual context, we leverage the advanced LLM to merge all annotations adaptively. In this fashion, our MMtrail dataset potentially paves the path for fine-grained large multimodal-language model training. In experiments, we provide evaluation metrics and benchmark results on our dataset, demonstrating the high quality of our annotation and its effectiveness for model training.
Learning Ordinality in Semantic Segmentation
Authors: Rafael Cristino, Ricardo P. M. Cruz, Jaime S. Cardoso
Date: 2024-07-30 00:00:00
Tasks: Autonomous Driving, Segmentation, Semantic Segmentation

Semantic segmentation consists of predicting a semantic label for each image pixel. Conventional deep learning models do not take advantage of ordinal relations that might exist in the domain at hand. For example, it is known that the pupil is inside the iris, and the lane markings are inside the road. Such domain knowledge can be employed as constraints to make the model more robust. The current literature on this topic has explored pixel-wise ordinal segmentation methods, which treat each pixel as an independent observation and promote ordinality in its representation. This paper proposes novel spatial ordinal segmentation methods, which take advantage of the structured image space by considering each pixel as an observation dependent on its neighborhood context to also promote ordinal spatial consistency. When evaluated with five biomedical datasets and multiple configurations of autonomous driving datasets, ordinal methods resulted in more ordinally-consistent models, with substantial improvements in ordinal metrics and some increase in the Dice coefficient. It was also shown that the incorporation of ordinal consistency results in models with better generalization abilities.
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
Authors: Marek Herde, Denis Huseljic, Lukas Rauch, Bernhard Sick
Date: 2024-07-30 00:00:00
Tasks: Active Learning

Human annotators typically provide annotated data for training machine learning models, such as neural networks. Yet, human annotations are subject to noise, impairing generalization performances. Methodological research on approaches counteracting noisy annotations requires corresponding datasets for a meaningful empirical evaluation. Consequently, we introduce a novel benchmark dataset, dopanim, consisting of about 15,750 animal images of 15 classes with ground truth labels. For approximately 10,500 of these images, 20 humans provided over 52,000 annotations with an accuracy of circa 67%. Its key attributes include (1) the challenging task of classifying doppelganger animals, (2) human-estimated likelihoods as annotations, and (3) annotator metadata. We benchmark well-known multi-annotator learning approaches using seven variants of this dataset and outline further evaluation use cases such as learning beyond hard class labels and active learning. Our dataset and a comprehensive codebase are publicly available to emulate the data collection process and to reproduce all empirical results.
EAR: Edge-Aware Reconstruction of 3-D vertebrae structures from bi-planar X-ray images
Authors: Lixing Tan, Shuang Song, Yaofeng He, Kangneng Zhou, Tong Lu, Ruoxiu Xiao
Date: 2024-07-30 00:00:00
Tasks: SSIM

X-ray images ease the diagnosis and treatment process due to their rapid imaging speed and high resolution. However, due to the projection process of X-ray imaging, much spatial information has been lost. To accurately provide efficient spinal morphological and structural information, reconstructing the 3-D structures of the spine from the 2-D X-ray images is essential. It is challenging for current reconstruction methods to preserve the edge information and local shapes of the asymmetrical vertebrae structures. In this study, we propose a new Edge-Aware Reconstruction network (EAR) to focus on the performance improvement of the edge information and vertebrae shapes. In our network, by using the auto-encoder architecture as the backbone, the edge attention module and frequency enhancement module are proposed to strengthen the perception of the edge reconstruction. Meanwhile, we also combine four loss terms, including reconstruction loss, edge loss, frequency loss and projection loss. The proposed method is evaluated using three publicly accessible datasets and compared with four state-of-the-art models. The proposed method is superior to other methods and achieves 25.32%, 15.32%, 86.44%, 80.13%, 23.7612 and 0.3014 with regard to MSE, MAE, Dice, SSIM, PSNR and frequency distance. Due to the end-to-end and accurate reconstruction process, EAR can provide sufficient 3-D spatial information and precise preoperative surgical planning guidance.
UniProcessor: A Text-induced Unified Low-level Image Processor
Authors: Huiyu Duan, Xiongkuo Min, Sijing Wu, Wei Shen, Guangtao Zhai
Date: 2024-07-30 00:00:00
Tasks: Image Enhancement, Image Restoration, Language Modelling

Image processing, including image restoration, image enhancement, etc., involves generating a high-quality clean image from a degraded input. Deep learning-based methods have shown superior performance for various image processing tasks in terms of single-task conditions. However, they require to train separate models for different degradations and levels, which limits the generalization abilities of these models and restricts their applications in real-world. In this paper, we propose a text-induced unified image processor for low-level vision tasks, termed UniProcessor, which can effectively process various degradation types and levels, and support multimodal control. Specifically, our UniProcessor encodes degradation-specific information with the subject prompt and process degradations with the manipulation prompt. These context control features are injected into the UniProcessor backbone via cross-attention to control the processing procedure. For automatic subject-prompt generation, we further build a vision-language model for general-purpose low-level degradation perception via instruction tuning techniques. Our UniProcessor covers 30 degradation types, and extensive experiments demonstrate that our UniProcessor can well process these degradations without additional training or tuning and outperforms other competing methods. Moreover, with the help of degradation-aware context control, our UniProcessor first shows the ability to individually handle a single distortion in an image with multiple degradations.
SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
Authors: Hao Tan, Zichang Tan, Jun Li, Jun Wan, Zhen Lei, Stan Z. Li
Date: 2024-07-30 00:00:00
Tasks: In-Context Learning, Multi-Label Image Recognition

Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-Language Models (VLMs) have made notable advancements in this area. However, previous methods fail to effectively leverage the rich knowledge in language models and often incorporate label semantics into visual features unidirectionally. To overcome these problems, we propose a Split-and-Synthesize Prompting with Gated Alignments (SSPA) framework to amplify the potential of VLMs. Specifically, we develop an in-context learning approach to associate the inherent knowledge from LLMs. Then we propose a novel Split-and-Synthesize Prompting (SSP) strategy to first model the generic knowledge and downstream label semantics individually and then aggregate them carefully through the quaternion network. Moreover, we present Gated Dual-Modal Alignments (GDMA) to bidirectionally interact visual and linguistic modalities while eliminating redundant cross-modal information, enabling more efficient region-level alignments. Rather than making the final prediction by a sharp manner in previous works, we propose a soft aggregator to jointly consider results from all image regions. With the help of flexible prompting and gated alignments, SSPA is generalizable to specific domains. Extensive experiments on nine datasets from three domains (i.e., natural, pedestrian attributes and remote sensing) demonstrate the state-of-the-art performance of SSPA. Further analyses verify the effectiveness of SSP and the interpretability of GDMA. The code will be made public.
Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering
Authors: Yanpeng Zhao, Yiwei Hao, Siyu Gao, Yunbo Wang, Xiaokang Yang
Date: 2024-07-30 00:00:00
Tasks: Inverse Rendering, NeRF, Neural Rendering, Novel View Synthesis, Object, Scene Generation, Scene Understanding

Learning object-centric representations from unsupervised videos is challenging. Unlike most previous approaches that focus on decomposing 2D images, we present a 3D generative model named DynaVol-S for dynamic scenes that enables object-centric learning within a differentiable volume rendering framework. The key idea is to perform object-centric voxelization to capture the 3D nature of the scene, which infers per-object occupancy probabilities at individual spatial locations. These voxel features evolve through a canonical-space deformation function and are optimized in an inverse rendering pipeline with a compositional NeRF. Additionally, our approach integrates 2D semantic features to create 3D semantic grids, representing the scene through multiple disentangled voxel grids. DynaVol-S significantly outperforms existing models in both novel view synthesis and unsupervised decomposition tasks for dynamic scenes. By jointly considering geometric structures and semantic features, it effectively addresses challenging real-world scenarios involving complex object interactions. Furthermore, once trained, the explicitly meaningful voxel features enable additional capabilities that 2D scene decomposition methods cannot achieve, such as novel scene generation through editing geometric shapes or manipulating the motion trajectories of objects.
What is YOLOv5: A deep look into the internal features of the popular object detector
Authors: Rahima Khanam, Muhammad Hussain
Date: 2024-07-30 00:00:00
Tasks: Object, object-detection, Object Detection, Position

This study presents a comprehensive analysis of the YOLOv5 object detection model, examining its architecture, training methodologies, and performance. Key components, including the Cross Stage Partial backbone and Path Aggregation-Network, are explored in detail. The paper reviews the model's performance across various metrics and hardware platforms. Additionally, the study discusses the transition from Darknet to PyTorch and its impact on model development. Overall, this research provides insights into YOLOv5's capabilities and its position within the broader landscape of object detection and why it is a popular choice for constrained edge deployment scenarios.
S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap
Authors: Jiaqi Cui, Pinxian Zeng, Yuanyuan Xu, Xi Wu, Jiliu Zhou, Yan Wang
Date: 2024-07-30 00:00:00
Tasks: Image Reconstruction

To acquire high-quality positron emission tomography (PET) images while reducing the radiation tracer dose, numerous efforts have been devoted to reconstructing standard-dose PET (SPET) images from low-dose PET (LPET). However, the success of current fully-supervised approaches relies on abundant paired LPET and SPET images, which are often unavailable in clinic. Moreover, these methods often mix the dose-invariant content with dose level-related dose-specific details during reconstruction, resulting in distorted images. To alleviate these problems, in this paper, we propose a two-stage Semi-Supervised SPET reconstruction framework, namely S3PET, to accommodate the training of abundant unpaired and limited paired SPET and LPET images. Our S3PET involves an un-supervised pre-training stage (Stage I) to extract representations from unpaired images, and a supervised dose-aware reconstruction stage (Stage II) to achieve LPET-to-SPET reconstruction by transferring the dose-specific knowledge between paired images. Specifically, in stage I, two independent dose-specific masked autoencoders (DsMAEs) are adopted to comprehensively understand the unpaired SPET and LPET images. Then, in Stage II, the pre-trained DsMAEs are further finetuned using paired images. To prevent distortions in both content and details, we introduce two elaborate modules, i.e., a dose knowledge decouple module to disentangle the respective dose-specific and dose-invariant knowledge of LPET and SPET, and a dose-specific knowledge learning module to transfer the dose-specific information from SPET to LPET, thereby achieving high-quality SPET reconstruction from LPET images. Experiments on two datasets demonstrate that our S3PET achieves state-of-the-art performance quantitatively and qualitatively.
Automatic Die Studies for Ancient Numismatics
Authors: Clément Cornet, Héloïse Aumaître, Romaric Besançon, Julien Olivier, Thomas Faucher, Hervé Le Borgne
Date: 2024-07-30 00:00:00

Die studies are fundamental to quantifying ancient monetary production, providing insights into the relationship between coinage, politics, and history. The process requires tedious manual work, which limits the size of the corpora that can be studied. Few works have attempted to automate this task, and none have been properly released and evaluated from a computer vision perspective. We propose a fully automatic approach that introduces several innovations compared to previous methods. We rely on fast and robust local descriptors matching that is set automatically. Second, the core of our proposal is a clustering-based approach that uses an intrinsic metric (that does not need the ground truth labels) to determine its critical hyper-parameters. We validate the approach on two corpora of Greek coins, propose an automatic implementation and evaluation of previous baselines, and show that our approach significantly outperforms them.