pwc-archive/papers-with-abstracts huggingface dataset:
VFA: Vision Frequency Analysis of Foundation Models and Human
Authors: Mohammad-Javad Darvishi-Bayazi, Md Rifat Arefin, Jocelyn Faubert, Irina Rish
Date: 2024-09-09 00:00:00
Tasks: Models Alignment, Out-of-Distribution Generalization

Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation. Models that better align with human perception may achieve higher out-of-distribution generalization. In this study, we investigate how various characteristics of large-scale computer vision models influence their alignment with human capabilities and robustness. Our findings indicate that increasing model and data size and incorporating rich semantic information and multiple modalities enhance models' alignment with human perception and their overall robustness. Our empirical analysis demonstrates a strong correlation between out-of-distribution accuracy and human alignment.
A Flexible Framework for Universal Computational Aberration Correction via Automatic Lens Library Generation and Domain Adaptation
Authors: Qi Jiang, Yao Gao, Shaohua Gao, Zhonghua Yi, Lei Sun, Hao Shi, Kailun Yang, Kaiwei Wang, Jian Bai
Date: 2024-09-09 00:00:00
Tasks: Domain Adaptation

Emerging universal Computational Aberration Correction (CAC) paradigms provide an inspiring solution to light-weight and high-quality imaging without repeated data preparation and model training to accommodate new lens designs. However, the training databases in these approaches, i.e., the lens libraries (LensLibs), suffer from their limited coverage of real-world aberration behaviors. In this work, we set up an OmniLens framework for universal CAC, considering both the generalization ability and flexibility. OmniLens extends the idea of universal CAC to a broader concept, where a base model is trained for three cases, including zero-shot CAC with the pre-trained model, few-shot CAC with a little lens-specific data for fine-tuning, and domain adaptive CAC using domain adaptation for lens-descriptions-unknown lens. In terms of OmniLens's data foundation, we first propose an Evolution-based Automatic Optical Design (EAOD) pipeline to construct LensLib automatically, coined AODLib, whose diversity is enriched by an evolution framework, with comprehensive constraints and a hybrid optimization strategy for achieving realistic aberration behaviors. For network design, we introduce the guidance of high-quality codebook priors to facilitate zero-shot CAC and few-shot CAC, which enhances the model's generalization ability, while also boosting its convergence in a few-shot case. Furthermore, based on the statistical observation of dark channel priors in optical degradation, we design an unsupervised regularization term to adapt the base model to the target descriptions-unknown lens using its aberration images without ground truth. We validate OmniLens on 4 manually designed low-end lenses with various structures and aberration behaviors. Remarkably, the base model trained on AODLib exhibits strong generalization capabilities, achieving 97% of the lens-specific performance in a zero-shot setting.
Input Space Mode Connectivity in Deep Neural Networks
Authors: Jakub Vrabel, Ori Shem-Ur, Yaron Oz, David Krueger
Date: 2024-09-09 00:00:00

We extend the concept of loss landscape mode connectivity to the input space of deep neural networks. Mode connectivity was originally studied within parameter space, where it describes the existence of low-loss paths between different solutions (loss minimizers) obtained through gradient descent. We present theoretical and empirical evidence of its presence in the input space of deep networks, thereby highlighting the broader nature of the phenomenon. We observe that different input images with similar predictions are generally connected, and for trained models, the path tends to be simple, with only a small deviation from being a linear path. Our methodology utilizes real, interpolated, and synthetic inputs created using the input optimization technique for feature visualization. We conjecture that input space mode connectivity in high-dimensional spaces is a geometric effect that takes place even in untrained models and can be explained through percolation theory. We exploit mode connectivity to obtain new insights about adversarial examples and demonstrate its potential for adversarial detection. Additionally, we discuss applications for the interpretability of deep networks.
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition
Authors: Kuan-Cheng Chen, Wenxuan Ma, Xiaotian Xu
Date: 2024-09-09 00:00:00
Tasks: Computational Efficiency, Emotion Recognition, Speech Emotion Recognition, speech-recognition, Speech Recognition

This paper presents a Consensus-based Distributed Quantum Kernel Learning (CDQKL) framework aimed at improving speech recognition through distributed quantum computing.CDQKL addresses the challenges of scalability and data privacy in centralized quantum kernel learning. It does this by distributing computational tasks across quantum terminals, which are connected through classical channels. This approach enables the exchange of model parameters without sharing local training data, thereby maintaining data privacy and enhancing computational efficiency. Experimental evaluations on benchmark speech emotion recognition datasets demonstrate that CDQKL achieves competitive classification accuracy and scalability compared to centralized and local quantum kernel learning models. The distributed nature of CDQKL offers advantages in privacy preservation and computational efficiency, making it suitable for data-sensitive fields such as telecommunications, automotive, and finance. The findings suggest that CDQKL can effectively leverage distributed quantum computing for large-scale machine-learning tasks.
Robust Loss Functions for Object Grasping under Limited Ground Truth
Authors: Yangfan Deng, Mengyao Zhang, Yong Zhao
Date: 2024-09-09 00:00:00

Object grasping is a crucial technology enabling robots to perceive and interact with the environment sufficiently. However, in practical applications, researchers are faced with missing or noisy ground truth while training the convolutional neural network, which decreases the accuracy of the model. Therefore, different loss functions are proposed to deal with these problems to improve the accuracy of the neural network. For missing ground truth, a new predicted category probability method is defined for unlabeled samples, which works effectively in conjunction with the pseudo-labeling method. Furthermore, for noisy ground truth, a symmetric loss function is introduced to resist the corruption of label noises. The proposed loss functions are powerful, robust, and easy to use. Experimental results based on the typical grasping neural network show that our method can improve performance by 2 to 13 percent.
Boosting CNN-based Handwriting Recognition Systems with Learnable Relaxation Labeling
Authors: Sara Ferro, Alessandro Torcinovich, Arianna Traviglia, Marcello Pelillo
Date: 2024-09-09 00:00:00
Tasks: Handwriting Recognition

The primary challenge for handwriting recognition systems lies in managing long-range contextual dependencies, an issue that traditional models often struggle with. To mitigate it, attention mechanisms have recently been employed to enhance context-aware labelling, thereby achieving state-of-the-art performance. In the field of pattern recognition and image analysis, however, the use of contextual information in labelling problems has a long history and goes back at least to the early 1970's. Among the various approaches developed in those years, Relaxation Labelling (RL) processes have played a prominent role and have been the method of choice in the field for more than a decade. Contrary to recent transformer-based architectures, RL processes offer a principled approach to the use of contextual constraints, having a solid theoretic foundation grounded on variational inequality and game theory, as well as effective algorithms with convergence guarantees. In this paper, we propose a novel approach to handwriting recognition that integrates the strengths of two distinct methodologies. In particular, we propose integrating (trainable) RL processes with various well-established neural architectures and we introduce a sparsification technique that accelerates the convergence of the algorithm and enhances the overall system's performance. Experiments over several benchmark datasets show that RL processes can improve the generalisation ability, even surpassing in some cases transformer-based architectures.
Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features
Authors: Jacob Gildenblat, Ofir Hadar
Date: 2024-09-09 00:00:00
Tasks: Deep Learning, Segmentation, Semantic Segmentation, Unsupervised Semantic Segmentation

We introduce Segmentation by Factorization (F-SEG), an unsupervised segmentation method for pathology that generates segmentation masks from pre-trained deep learning models. F-SEG allows the use of pre-trained deep neural networks, including recently developed pathology foundation models, for semantic segmentation. It achieves this without requiring additional training or finetuning, by factorizing the spatial features extracted by the models into segmentation masks and their associated concept features. We create generic tissue phenotypes for H&E images by training clustering models for multiple numbers of clusters on features extracted from several deep learning models on The Cancer Genome Atlas Program (TCGA), and then show how the clusters can be used for factorizing corresponding segmentation masks using off-the-shelf deep learning models. Our results show that F-SEG provides robust unsupervised segmentation capabilities for H&E pathology images, and that the segmentation quality is greatly improved by utilizing pathology foundation models. We discuss and propose methods for evaluating the performance of unsupervised segmentation in pathology.
LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow
Authors: Hongyu Wen, Erich Liang, Jia Deng
Date: 2024-09-09 00:00:00
Tasks: Diversity, Optical Flow Estimation

Achieving 3D understanding of non-Lambertian objects is an important task with many useful applications, but most existing algorithms struggle to deal with such objects. One major obstacle towards progress in this field is the lack of holistic non-Lambertian benchmarks -- most benchmarks have low scene and object diversity, and none provide multi-layer 3D annotations for objects occluded by transparent surfaces. In this paper, we introduce LayeredFlow, a real world benchmark containing multi-layer ground truth annotation for optical flow of non-Lambertian objects. Compared to previous benchmarks, our benchmark exhibits greater scene and object diversity, with 150k high quality optical flow and stereo pairs taken over 185 indoor and outdoor scenes and 360 unique objects. Using LayeredFlow as evaluation data, we propose a new task called multi-layer optical flow. To provide training data for this task, we introduce a large-scale densely-annotated synthetic dataset containing 60k images within 30 scenes tailored for non-Lambertian objects. Training on our synthetic dataset enables model to predict multi-layer optical flow, while fine-tuning existing optical flow methods on the dataset notably boosts their performance on non-Lambertian objects without compromising the performance on diffuse objects. Data is available at https://layeredflow.cs.princeton.edu.
Cherenkov Imaged Bio-morphological Features Verify Patient Positioning with Deformable Tissue Translocation in Breast Radiotherapy
Authors: Yao Chen, Savannah M. Decker, Petr Bruza, David J. Gladstone, Lesley A. Jarvis, Brian W. Pogue, Kimberley S. Samkoe, Rongxiao Zhang
Date: 2024-09-09 00:00:00
Tasks: Vessel Detection

Accurate patient positioning is critical for precise radiotherapy dose delivery, as positioning errors can significantly affect treatment outcomes. This study introduces a novel method for tracking loco-regional tissue deformation through Cherenkov image analysis during fractionated breast cancer radiotherapy. The primary goal was to develop and test an algorithm for Cherenkov-based regional position accuracy quantification, specifically for loco-regional deformations, which lack ideal quantification methods in radiotherapy. Blood vessel detection and segmentation were developed in Cherenkov images using a tissue phantom with incremental movements, and later applied to images from fractionated whole breast radiotherapy in human patients (n=10). A combined rigid and non-rigid registration technique was used to detect inter- and intra-fractional positioning variations. This approach quantified positioning variations in two parts: a global shift from rigid registration and a two-dimensional variation map of loco-regional deformation from non-rigid registration. The methodology was validated using an anthropomorphic chest phantom experiment, where known treatment couch translations and respiratory motion were simulated to assess inter- and intra-fractional uncertainties, yielding an average accuracy of 0.83 mm for couch translations up to 20 mm. Analysis of clinical Cherenkov data from ten breast cancer patients showed an inter-fraction setup variation of 3.7 plus minus 2.4 mm relative to the first fraction and loco-regional deformations (95th percentile) of up to 3.3 plus minus 1.9 mm. This study presents a Cherenkov-based approach to quantify global and local positioning variations, demonstrating feasibility in addressing loco-regional deformations that conventional imaging techniques fail to capture.
AnomalyCD: A benchmark for Earth anomaly change detection with high-resolution and time-series observations
Authors: Jingtao Li, Qian Zhu, Xinyu Wang, Hengwei Zhao, Yanfei Zhong
Date: 2024-09-09 00:00:00
Tasks: Change Detection, Time Series

Various Earth anomalies have destroyed the stable, balanced state, resulting in fatalities and serious destruction of property. With the advantages of large-scale and precise observation, high-resolution remote sensing images have been widely used for anomaly monitoring and localization. Powered by the deep representation, the existing methods have achieved remarkable advances, primarily in classification and change detection techniques. However, labeled samples are difficult to acquire due to the low probability of anomaly occurrence, and the trained models are limited to fixed anomaly categories, which hinders the application for anomalies with few samples or unknown anomalies. In this paper, to tackle this problem, we propose the anomaly change detection (AnomalyCD) technique, which accepts time-series observations and learns to identify anomalous changes by learning from the historical normal change pattern. Compared to the existing techniques, AnomalyCD processes an unfixed number of time steps and can localize the various anomalies in a unified manner, without human supervision. To benchmark AnomalyCD, we constructed a high-resolution dataset with time-series images dedicated to various Earth anomalies (the AnomalyCDD dataset). AnomalyCDD contains high-resolution (from 0.15 to 2.39 m/pixel), time-series (from 3 to 7 time steps), and large-scale images (1927.93 km2 in total) collected globally Furthermore, we developed a zero-shot baseline model (AnomalyCDM), which implements the AnomalyCD technique by extracting a general representation from the segment anything model (SAM) and conducting temporal comparison to distinguish the anomalous changes from normal changes. AnomalyCDM is designed as a two-stage workflow to enhance the efficiency, and has the ability to process the unseen images directly, without retraining for each scene.
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning
Authors: Shiru Wang, Yao Chen, Lesley A. Jarvis, Yucheng Tang, David J. Gladstone, Kimberley S. Samkoe, Brian W. Pogue, Petr Bruza, Rongxiao Zhang
Date: 2024-09-09 00:00:00
Tasks: Segmentation, Transfer Learning

Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during Radiation Therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However until now, no concerted analysis of this biological feature-based tracking was utilized because of the slow speed and accuracy of conventional image processing for feature segmentation. This study demonstrated the first deep learning framework for such an application, achieving video frame rate processing. To address the challenge of limited annotation of these features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet segmentation framework. Subsequently, a small Cherenkov dataset (1,483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on another 19 patients, including subcutaneous veins, scars, and pigmented skin. Average segmentation by the model achieved Dice score of 0.85 and required less than 0.7 milliseconds processing time per instance. The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.
Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification
Authors: Jiarui Li, Zhen Qiu, Yilin Yang, Yuqi Li, Zeyu Dong, Chuanguang Yang
Date: 2024-09-09 00:00:00
Tasks: Diversity, Person Re-Identification

The primary challenges in visible-infrared person re-identification arise from the differences between visible (vis) and infrared (ir) images, including inter-modal and intra-modal variations. These challenges are further complicated by varying viewpoints and irregular movements. Existing methods often rely on horizontal partitioning to align part-level features, which can introduce inaccuracies and have limited effectiveness in reducing modality discrepancies. In this paper, we propose a novel Prototype-Driven Multi-feature generation framework (PDM) aimed at mitigating cross-modal discrepancies by constructing diversified features and mining latent semantically similar features for modal alignment. PDM comprises two key components: Multi-Feature Generation Module (MFGM) and Prototype Learning Module (PLM). The MFGM generates diversity features closely distributed from modality-shared features to represent pedestrians. Additionally, the PLM utilizes learnable prototypes to excavate latent semantic similarities among local features between visible and infrared modalities, thereby facilitating cross-modal instance-level alignment. We introduce the cosine heterogeneity loss to enhance prototype diversity for extracting rich local features. Extensive experiments conducted on the SYSU-MM01 and LLCM datasets demonstrate that our approach achieves state-of-the-art performance. Our codes are available at https://github.com/mmunhappy/ICASSP2025-PDM.
Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery
Authors: Fan Zhang, Lingling Li, Licheng Jiao, Xu Liu, Fang Liu, Shuyuan Yang, Biao Hou
Date: 2024-09-09 00:00:00
Tasks: object-detection, Object Detection

Satellite imagery, due to its long-range imaging, brings with it a variety of scale-preferred tasks, such as the detection of tiny/small objects, making the precise localization and detection of small objects of interest a challenging task. In this article, we design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables ``synergistic focusing'' of multi-scale features. Based on our observations of KDN, we abstract a class of RCs with different connection strengths, called n21C, and generalize it to FPN-based multi-branch detectors. In a series of FPN experiments on the scale-preferred tasks, we found that the ``divide-and-conquer'' idea of FPN severely hampers the detector's learning in the right direction due to the large number of large-scale negative samples and interference from background noise. Moreover, these negative samples cannot be eliminated by the focal loss function. The RCs extends the multi-level feature's ``divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks, and enables synergistic effects of multi-level features on the specific learning goal. In addition, interference activations in two aspects are greatly reduced and the detector learns in a more correct direction. Extensive experiments of 17 well-designed detection architectures embedded with n21s on five different levels of scale-preferred tasks validate the effectiveness and efficiency of the RCs. Especially the simplest linear form of RC, E421C performs well in all tasks and it satisfies the scaling property of RGT. We hope that our approach will transfer a large number of well-designed detectors from the computer vision community to the remote sensing community.
G-NeLF: Memory- and Data-Efficient Hybrid Neural Light Field for Novel View Synthesis
Authors: Lutao Jiang, Lin Wang
Date: 2024-09-09 00:00:00
Tasks: NeRF, Novel View Synthesis

Following the burgeoning interest in implicit neural representation, Neural Light Field (NeLF) has been introduced to predict the color of a ray directly. Unlike Neural Radiance Field (NeRF), NeLF does not create a point-wise representation by predicting color and volume density for each point in space. However, the current NeLF methods face a challenge as they need to train a NeRF model first and then synthesize over 10K views to train NeLF for improved performance. Additionally, the rendering quality of NeLF methods is lower compared to NeRF methods. In this paper, we propose G-NeLF, a versatile grid-based NeLF approach that utilizes spatial-aware features to unleash the potential of the neural network's inference capability, and consequently overcome the difficulties of NeLF training. Specifically, we employ a spatial-aware feature sequence derived from a meticulously crafted grid as the ray's representation. Drawing from our empirical studies on the adaptability of multi-resolution hash tables, we introduce a novel grid-based ray representation for NeLF that can represent the entire space with a very limited number of parameters. To better utilize the sequence feature, we design a lightweight ray color decoder that simulates the ray propagation process, enabling a more efficient inference of the ray's color. G-NeLF can be trained without necessitating significant storage overhead and with the model size of only 0.95 MB to surpass previous state-of-the-art NeLF. Moreover, compared with grid-based NeRF methods, e.g., Instant-NGP, we only utilize one-tenth of its parameters to achieve higher performance. Our code will be released upon acceptance.