pwc-archive/papers-with-abstracts huggingface dataset:
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation
Authors: Xiaoyang Chen, Hao Zheng, Yifang Xie, Yuncong Ma, Tengfei Li
Date: 2024-05-25 00:00:00
Tasks: Contrastive Learning, Image Segmentation, Incremental Learning, Knowledge Distillation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Current methods for developing foundation models in medical image segmentation rely on two primary assumptions: a fixed set of classes and the immediate availability of a substantial and diverse training dataset. However, this can be impractical due to the evolving nature of imaging technology and patient demographics, as well as labor-intensive data curation, limiting their practical applicability and scalability. To address these challenges, we introduce a novel segmentation paradigm enabling the segmentation of a variable number of classes within a single classifier-free network, featuring an architecture independent of class number. This network is trained using contrastive learning and produces discriminative feature representations that facilitate straightforward interpretation. Additionally, we integrate this strategy into a knowledge distillation-based incremental learning framework, facilitating the gradual assimilation of new information from non-stationary data streams while avoiding catastrophic forgetting. Our approach provides a unified solution for tackling both class- and domain-incremental learning scenarios. We demonstrate the flexibility of our method in handling varying class numbers within a unified network and its capacity for incremental learning. Experimental results on an incompletely annotated, multi-modal, multi-source dataset for medical image segmentation underscore its superiority over state-of-the-art alternative approaches.
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples
Authors: Dae Ung Jo, Kyuewang Lee, JaeHo Chung, Jin Young Choi
Date: 2024-05-25 00:00:00
Tasks: Active Learning, Image-text Retrieval, Retrieval, Text Retrieval

Securing a sufficient amount of paired data is important to train an image-text retrieval (ITR) model, but collecting paired data is very expensive. To address this issue, in this paper, we propose an active learning algorithm for ITR that can collect paired data cost-efficiently. Previous studies assume that image-text pairs are given and their category labels are asked to the annotator. However, in the recent ITR studies, the importance of category label is decreased since a retrieval model can be trained with only image-text pairs. For this reason, we set up an active learning scenario where unpaired images (or texts) are given and the annotator provides corresponding texts (or images) to make paired data. The key idea of the proposed AL algorithm is to select unpaired images (or texts) that can be hard negative samples for existing texts (or images). To this end, we introduce a novel scoring function to choose hard negative samples. We validate the effectiveness of the proposed method on Flickr30K and MS-COCO datasets.
Neural Network-Based Tracking and 3D Reconstruction of Baseball Pitch Trajectories from Single-View 2D Video
Authors: Jhen Hsieh
Date: 2024-05-25 00:00:00
Tasks: 3D Reconstruction

In this paper, we present a neural network-based approach for tracking and reconstructing the trajectories of baseball pitches from 2D video footage to 3D coordinates. We utilize OpenCV's CSRT algorithm to accurately track the baseball and fixed reference points in 2D video frames. These tracked pixel coordinates are then used as input features for our neural network model, which comprises multiple fully connected layers to map the 2D coordinates to 3D space. The model is trained on a dataset of labeled trajectories using a mean squared error loss function and the Adam optimizer, optimizing the network to minimize prediction errors. Our experimental results demonstrate that this approach achieves high accuracy in reconstructing 3D trajectories from 2D inputs. This method shows great potential for applications in sports analysis, coaching, and enhancing the accuracy of trajectory predictions in various sports.
M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
Authors: Mingshuang Luo, Ruibing Hou, Zhuo Li, Hong Chang, Zimo Liu, YaoWei Wang, Shiguang Shan
Date: 2024-05-25 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Motion Generation, Quantization, Zero-shot Generalization

This paper presents M$^3$GPT, an advanced $\textbf{M}$ultimodal, $\textbf{M}$ultitask framework for $\textbf{M}$otion comprehension and generation. M$^3$GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal conditional signals, such as text, music and motion/dance, enabling seamless integration into a large language model (LLM) with a single vocabulary. The second involves modeling motion generation directly in the raw motion space. This strategy circumvents the information loss associated with a discrete tokenizer, resulting in more detailed and comprehensive motion generation. Third, M$^3$GPT learns to model the connections and synergies among various motion-relevant tasks. Text, the most familiar and well-understood modality for LLMs, is utilized as a bridge to establish connections between different motion tasks, facilitating mutual reinforcement. To our knowledge, M$^3$GPT is the first model capable of comprehending and generating motions based on multiple signals. Extensive experiments highlight M$^3$GPT's superior performance across various motion-relevant tasks and its powerful zero-shot generalization capabilities for extremely challenging tasks. Project page: \url{https://github.com/luomingshuang/M3GPT}.
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination
Authors: Shelly Golan, Roy Ganz, Michael Elad
Date: 2024-05-25 00:00:00
Tasks: Image Generation

The recently introduced Consistency models pose an efficient alternative to diffusion algorithms, enabling rapid and good quality image synthesis. These methods overcome the slowness of diffusion models by directly mapping noise to data, while maintaining a (relatively) simpler training. Consistency models enable a fast one- or few-step generation, but they typically fall somewhat short in sample quality when compared to their diffusion origins. In this work we propose a novel and highly effective technique for post-processing Consistency-based generated images, enhancing their perceptual quality. Our approach utilizes a joint classifier-discriminator model, in which both portions are trained adversarially. While the classifier aims to grade an image based on its assignment to a designated class, the discriminator portion of the very same network leverages the softmax values to assess the proximity of the input image to the targeted data manifold, thereby serving as an Energy-based Model. By employing example-specific projected gradient iterations under the guidance of this joint machine, we refine synthesized images and achieve an improved FID scores on the ImageNet 64x64 dataset for both Consistency-Training and Consistency-Distillation techniques.
Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD
Authors: Junlin Song, Yuzhuo Chen, Yuan YAO, Zetong Chen, Renhao Guo, Lida Yang, Xinyi Sui, Qihang Wang, Xijiao Li, Aihua Cao, Wei Li
Date: 2024-05-25 00:00:00
Tasks: Diagnostic

Autism Spectrum Disorder is a condition characterized by a typical brain development leading to impairments in social skills, communication abilities, repetitive behaviors, and sensory processing. There have been many studies combining brain MRI images with machine learning algorithms to achieve objective diagnosis of autism, but the correlation between white matter and autism has not been fully utilized. To address this gap, we develop a computer-aided diagnostic model focusing on white matter regions in brain MRI by employing radiomics and machine learning methods. This study introduced a MultiUNet model for segmenting white matter, leveraging the UNet architecture and utilizing manually segmented MRI images as the training data. Subsequently, we extracted white matter features using the Pyradiomics toolkit and applied different machine learning models such as Support Vector Machine, Random Forest, Logistic Regression, and K-Nearest Neighbors to predict autism. The prediction sets all exceeded 80% accuracy. Additionally, we employed Convolutional Neural Network to analyze segmented white matter images, achieving a prediction accuracy of 86.84%. Notably, Support Vector Machine demonstrated the highest prediction accuracy at 89.47%. These findings not only underscore the efficacy of the models but also establish a link between white matter abnormalities and autism. Our study contributes to a comprehensive evaluation of various diagnostic models for autism and introduces a computer-aided diagnostic algorithm for early and objective autism diagnosis based on MRI white matter regions.
A better approach to diagnose retinal diseases: Combining our Segmentation-based Vascular Enhancement with deep learning features
Authors: Yuzhuo Chen, Zetong Chen, Yuanyuan Liu
Date: 2024-05-25 00:00:00

Abnormalities in retinal fundus images may indicate certain pathologies such as diabetic retinopathy, hypertension, stroke, glaucoma, retinal macular edema, venous occlusion, and atherosclerosis, making the study and analysis of retinal images of great significance. In conventional medicine, the diagnosis of retina-related diseases relies on a physician's subjective assessment of the retinal fundus images, which is a time-consuming process and the accuracy is highly dependent on the physician's subjective experience. To this end, this paper proposes a fast, objective, and accurate method for the diagnosis of diseases related to retinal fundus images. This method is a multiclassification study of normal samples and 13 categories of disease samples on the STARE database, with a test set accuracy of 99.96%. Compared with other studies, our method achieved the highest accuracy. This study innovatively propose Segmentation-based Vascular Enhancement(SVE). After comparing the classification performances of the deep learning models of SVE images, original images and Smooth Grad-CAM ++ images, we extracted the deep learning features and traditional features of the SVE images and input them into nine meta learners for classification. The results shows that our proposed UNet-SVE-VGG-MLP model has the optimal performance for classifying diseases related to retinal fundus images on the STARE database, with a overall accuracy of 99.96% and a weighted AUC of 99.98% for the 14 categories on test dataset. This method can be used to realize rapid, objective, and accurate classification and diagnosis of retinal fundus image related diseases.
Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities
Authors: Shiyu Xia, Junyu Xiong, Haoyu Dong, Jianbo Zhao, Yuzhang Tian, Mengyu Zhou, Yeye He, Shi Han, Dongmei Zhang
Date: 2024-05-25 00:00:00
Tasks: Boundary Detection, Optical Character Recognition, Optical Character Recognition (OCR), Table Detection

This paper explores capabilities of Vision Language Models on spreadsheet comprehension. We propose three self-supervised challenges with corresponding evaluation metrics to comprehensively evaluate VLMs on Optical Character Recognition (OCR), spatial perception, and visual format recognition. Additionally, we utilize the spreadsheet table detection task to assess the overall performance of VLMs by integrating these challenges. To probe VLMs more finely, we propose three spreadsheet-to-image settings: column width adjustment, style change, and address augmentation. We propose variants of prompts to address the above tasks in different settings. Notably, to leverage the strengths of VLMs in understanding text rather than two-dimensional positioning, we propose to decode cell values on the four boundaries of the table in spreadsheet boundary detection. Our findings reveal that VLMs demonstrate promising OCR capabilities but produce unsatisfactory results due to cell omission and misalignment, and they notably exhibit insufficient spatial and format recognition skills, motivating future work to enhance VLMs' spreadsheet data comprehension capabilities using our methods to generate extensive spreadsheet-image pairs in various settings.
Detecting Adversarial Data via Perturbation Forgery
Authors: Qian Wang, Chen Li, Yuchen Luo, Hefei Ling, Ping Li, Jiazhong Chen, Shijuan Huang, Ning Yu
Date: 2024-05-25 00:00:00

As a defense strategy against adversarial attacks, adversarial detection aims to identify and filter out adversarial data from the data flow based on discrepancies in distribution and noise patterns between natural and adversarial data. Although previous detection methods achieve high performance in detecting gradient-based adversarial attacks, new attacks based on generative models with imbalanced and anisotropic noise patterns evade detection. Even worse, existing techniques either necessitate access to attack data before deploying a defense or incur a significant time cost for inference, rendering them impractical for defending against newly emerging attacks that are unseen by defenders. In this paper, we explore the proximity relationship between adversarial noise distributions and demonstrate the existence of an open covering for them. By learning to distinguish this open covering from the distribution of natural data, we can develop a detector with strong generalization capabilities against all types of adversarial attacks. Based on this insight, we heuristically propose Perturbation Forgery, which includes noise distribution perturbation, sparse mask generation, and pseudo-adversarial data production, to train an adversarial detector capable of detecting unseen gradient-based, generative-model-based, and physical adversarial attacks, while remaining agnostic to any specific models. Comprehensive experiments conducted on multiple general and facial datasets, with a wide spectrum of attacks, validate the strong generalization of our method.
DAFFNet: A Dual Attention Feature Fusion Network for Classification of White Blood Cells
Authors: Yuzhuo Chen, Zetong Chen, Yunuo An, Chenyang Lu, Xu Qiao
Date: 2024-05-25 00:00:00
Tasks: Classification

The precise categorization of white blood cell (WBC) is crucial for diagnosing blood-related disorders. However, manual analysis in clinical settings is time-consuming, labor-intensive, and prone to errors. Numerous studies have employed machine learning and deep learning techniques to achieve objective WBC classification, yet these studies have not fully utilized the information of WBC images. Therefore, our motivation is to comprehensively utilize the morphological information and high-level semantic information of WBC images to achieve accurate classification of WBC. In this study, we propose a novel dual-branch network Dual Attention Feature Fusion Network (DAFFNet), which for the first time integrates the high-level semantic features with morphological features of WBC to achieve accurate classification. Specifically, we introduce a dual attention mechanism, which enables the model to utilize the channel features and spatially localized features of the image more comprehensively. Morphological Feature Extractor (MFE), comprising Morphological Attributes Predictor (MAP) and Morphological Attributes Encoder (MAE), is proposed to extract the morphological features of WBC. We also implement Deep-supervised Learning (DSL) and Semi-supervised Learning (SSL) training strategies for MAE to enhance its performance. Our proposed network framework achieves 98.77%, 91.30%, 98.36%, 99.71%, 98.45%, and 98.85% overall accuracy on the six public datasets PBC, LISC, Raabin-WBC, BCCD, LDWBC, and Labelled, respectively, demonstrating superior effectiveness compared to existing studies. The results indicate that the WBC classification combining high-level semantic features and low-level morphological features is of great significance, which lays the foundation for objective and accurate classification of WBC in microscopic blood cell images.
Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier
Authors: Shuaixin Liu, Kunqian Li, Yilin Ding, Qi Qi
Date: 2024-05-25 00:00:00
Tasks: Image Enhancement, Image Generation, UIE

Underwater Image Enhancement (UIE) aims to improve the visual quality from a low-quality input. Unlike other image enhancement tasks, underwater images suffer from the unavailability of real reference images. Although existing works exploit synthetic images and manually select well-enhanced images as reference images to train enhancement networks, their upper performance bound is limited by the reference domain. To address this challenge, we propose CLIP-UIE, a novel framework that leverages the potential of Contrastive Language-Image Pretraining (CLIP) for the UIE task. Specifically, we propose employing color transfer to yield synthetic images by degrading in-air natural images into corresponding underwater images, guided by the real underwater domain. This approach enables the diffusion model to capture the prior knowledge of mapping transitions from the underwater degradation domain to the real in-air natural domain. Still, fine-tuning the diffusion model for specific downstream tasks is inevitable and may result in the loss of this prior knowledge. To migrate this drawback, we combine the prior knowledge of the in-air natural domain with CLIP to train a CLIP-Classifier. Subsequently, we integrate this CLIP-Classifier with UIE benchmark datasets to jointly fine-tune the diffusion model, guiding the enhancement results towards the in-air natural domain. Additionally, for image enhancement tasks, we observe that both the image-to-image diffusion model and CLIP-Classifier primarily focus on the high-frequency region during fine-tuning. Therefore, we propose a new fine-tuning strategy that specifically targets the high-frequency region, which can be up to 10 times faster than traditional strategies. Extensive experiments demonstrate that our method exhibits a more natural appearance.
Learning Visual-Semantic Subspace Representations for Propositional Reasoning
Authors: Gabriel Moreira, Alexander Hauptmann, Manuel Marques, João Paulo Costeira
Date: 2024-05-25 00:00:00

Learning representations that capture rich semantic relationships and accommodate propositional calculus poses a significant challenge. Existing approaches are either contrastive, lacking theoretical guarantees, or fall short in effectively representing the partial orders inherent to rich visual-semantic hierarchies. In this paper, we propose a novel approach for learning visual representations that not only conform to a specified semantic structure but also facilitate probabilistic propositional reasoning. Our approach is based on a new nuclear norm-based loss. We show that its minimum encodes the spectral geometry of the semantics in a subspace lattice, where logical propositions can be represented by projection operators.
FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Flight Trajectory Prediction
Authors: Lan Wu, Xuebin Wang, Ruijuan Chu, Guangyi Liu, Yingchun Chen, Jing Zhang, Linyu Wang
Date: 2024-05-25 00:00:00
Tasks: Prediction, Trajectory Prediction

Accurate multi-step flight trajectory prediction plays an important role in Air Traffic Control, which can ensure the safety of air transportation. Two main issues limit the flight trajectory prediction performance of existing works. The first issue is the negative impact on prediction accuracy caused by the significant differences in data range. The second issue is that real-world flight trajectories involve underlying temporal dependencies, and existing methods fail to reveal the hidden complex temporal variations and only extract features from one single time scale. To address the above issues, we propose FlightPatchNet, a multi-scale patch network with differential coding for flight trajectory prediction. Specifically, FlightPatchNet first utilizes the differential coding to encode the original values of longitude and latitude into first-order differences and generates embeddings for all variables at each time step. Then, a global temporal attention is introduced to explore the dependencies between different time steps. To fully explore the diverse temporal patterns in flight trajectories, a multi-scale patch network is delicately designed to serve as the backbone. The multi-scale patch network exploits stacked patch mixer blocks to capture inter- and intra-patch dependencies under different time scales, and further integrates multi-scale temporal features across different scales and variables. Finally, FlightPatchNet ensembles multiple predictors to make direct multi-step prediction. Extensive experiments on ADS-B datasets demonstrate that our model outperforms the competitive baselines.
A 7K Parameter Model for Underwater Image Enhancement based on Transmission Map Prior
Authors: Fuheng Zhou, Dikai Wei, Ye Fan, Yulong Huang, Yonggang Zhang
Date: 2024-05-25 00:00:00
Tasks: Data Compression, Decoder, Deep Learning, Image Enhancement

Although deep learning based models for underwater image enhancement have achieved good performance, they face limitations in both lightweight and effectiveness, which prevents their deployment and application on resource-constrained platforms. Moreover, most existing deep learning based models use data compression to get high-level semantic information in latent space instead of using the original information. Therefore, they require decoder blocks to generate the details of the output. This requires additional computational cost. In this paper, a lightweight network named lightweight selective attention network (LSNet) based on the top-k selective attention and transmission maps mechanism is proposed. The proposed model achieves a PSNR of 97\% with only 7K parameters compared to a similar attention-based model. Extensive experiments show that the proposed LSNet achieves excellent performance in state-of-the-art models with significantly fewer parameters and computational resources. The code is available at https://github.com/FuhengZhou/LSNet}{https://github.com/FuhengZhou/LSNet.
Enhancing Adversarial Transferability Through Neighborhood Conditional Sampling
Authors: Chunlin Qiu, Yiheng Duan, Lingchen Zhao, Qian Wang
Date: 2024-05-25 00:00:00

Transfer-based attacks craft adversarial examples utilizing a white-box surrogate model to compromise various black-box target models, posing significant threats to many real-world applications. However, existing transfer attacks suffer from either weak transferability or expensive computation. To bridge the gap, we propose a novel sample-based attack, named neighborhood conditional sampling (NCS), which enjoys high transferability with lightweight computation. Inspired by the observation that flat maxima result in better transferability, NCS is formulated as a max-min bi-level optimization problem to seek adversarial regions with high expected adversarial loss and small standard deviations. Specifically, due to the inner minimization problem being computationally intensive to resolve, and affecting the overall transferability, we propose a momentum-based previous gradient inversion approximation (PGIA) method to effectively solve the inner problem without any computation cost. In addition, we prove that two newly proposed attacks, which achieve flat maxima for better transferability, are actually specific cases of NCS under particular conditions. Extensive experiments demonstrate that NCS efficiently generates highly transferable adversarial examples, surpassing the current best method in transferability while requiring only 50% of the computational cost. Additionally, NCS can be seamlessly integrated with other methods to further enhance transferability.