pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection
Authors: Peng Ye, Chengyu Tao, Juan Du
Date: 2024-09-09 00:00:00
Tasks: Anomaly Detection

There are a variety of industrial products that possess periodic textures or surfaces, such as carbon fiber textiles and display panels. Traditional image-based quality inspection methods for these products require identifying the periodic patterns from normal images (without anomaly and noise) and subsequently detecting anomaly pixels with inconsistent appearances. However, it remains challenging to accurately extract the periodic pattern from a single image in the presence of unknown anomalies and measurement noise. To deal with this challenge, this paper proposes a novel self-representation of the periodic image defined on a set of continuous parameters. In this way, periodic pattern learning can be embedded into a joint optimization framework, which is named periodic-sparse decomposition, with simultaneously modeling the sparse anomalies and Gaussian noise. Finally, for the real-world industrial images that may not strictly satisfy the periodic assumption, we propose a novel pixel-level anomaly scoring strategy to enhance the performance of anomaly detection. Both simulated and real-world case studies demonstrate the effectiveness of the proposed methodology for periodic pattern learning and anomaly detection.
Decoupling Contact for Fine-Grained Motion Style Transfer
Authors: Xiangjun Tang, Linjun Wu, He Wang, Yiqian Wu, Bo Hu, Songnan Li, Xu Gong, Yuchen Liao, Qilong Kou, Xiaogang Jin
Date: 2024-09-09 00:00:00
Tasks: Motion Style Transfer, Style Transfer

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to express the style vividly while enhancing motion naturalness and quality. However, it is unknown how to decouple and control contact to achieve fine-grained control in motion style transfer. In this paper, we present a novel style transfer method for fine-grained control over contacts while achieving both motion naturalness and spatial-temporal variations of style. Based on our empirical evidence, we propose controlling contact indirectly through the hip velocity, which can be further decomposed into the trajectory and contact timing, respectively. To this end, we propose a new model that explicitly models the correlations between motions and trajectory/contact timing/style, allowing us to decouple and control each separately. Our approach is built around a motion manifold, where hip controls can be easily integrated into a Transformer-based decoder. It is versatile in that it can generate motions directly as well as be used as post-processing for existing methods to improve quality and contact controllability. In addition, we propose a new metric that measures a correlation pattern of motions based on our empirical evidence, aligning well with human perception in terms of motion naturalness. Based on extensive evaluation, our method outperforms existing methods in terms of style expressivity and motion quality.
Boosting CLIP Adaptation for Image Quality Assessment via Meta-Prompt Learning and Gradient Regularization
Authors: Xudong Li, Zihao Huang, Runze Hu, Yan Zhang, Liujuan Cao, Rongrong Ji
Date: 2024-09-09 00:00:00
Tasks: Image Quality Assessment, Meta-Learning, Prompt Learning

Image Quality Assessment (IQA) remains an unresolved challenge in the field of computer vision, due to complex distortion conditions, diverse image content, and limited data availability. The existing Blind IQA (BIQA) methods heavily rely on extensive human annotations to train models, which is both labor-intensive and costly due to the demanding nature of creating IQA datasets. To mitigate the dependence on labeled samples, this paper introduces a novel Gradient-Regulated Meta-Prompt IQA Framework (GRMP-IQA). This framework aims to fast adapt the powerful visual-language pre-trained model, CLIP, to downstream IQA tasks, significantly improving accuracy in scenarios with limited data. Specifically, the GRMP-IQA comprises two key modules: Meta-Prompt Pre-training Module and Quality-Aware Gradient Regularization. The Meta Prompt Pre-training Module leverages a meta-learning paradigm to pre-train soft prompts with shared meta-knowledge across different distortions, enabling rapid adaptation to various IQA tasks. On the other hand, the Quality-Aware Gradient Regularization is designed to adjust the update gradients during fine-tuning, focusing the model's attention on quality-relevant features and preventing overfitting to semantic information. Extensive experiments on five standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods under limited data setting, i.e., achieving SRCC values of 0.836 (vs. 0.760 on LIVEC) and 0.853 (vs. 0.812 on KonIQ). Notably, utilizing just 20\% of the training data, our GRMP-IQA outperforms most existing fully supervised BIQA methods.
Prim2Room: Layout-Controllable Room Mesh Generation from Primitives
Authors: Chengzeng Feng, Jiacheng Wei, Cheng Chen, Yang Li, Pan Ji, Fayao Liu, Hongdong Li, Guosheng Lin
Date: 2024-09-09 00:00:00
Tasks: Diversity, Retrieval

We propose Prim2Room, a novel framework for controllable room mesh generation leveraging 2D layout conditions and 3D primitive retrieval to facilitate precise 3D layout specification. Diverging from existing methods that lack control and precision, our approach allows for detailed customization of room-scale environments. To overcome the limitations of previous methods, we introduce an adaptive viewpoint selection algorithm that allows the system to generate the furniture texture and geometry from more favorable views than predefined camera trajectories. Additionally, we employ non-rigid depth registration to ensure alignment between generated objects and their corresponding primitive while allowing for shape variations to maintain diversity. Our method not only enhances the accuracy and aesthetic appeal of generated 3D scenes but also provides a user-friendly platform for detailed room design.
Memoryless Multimodal Anomaly Detection via Student-Teacher Network and Signed Distance Learning
Authors: Zhongbin Sun, Xiaolong Li, Yiran Li, Yue Ma
Date: 2024-09-09 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Unsupervised anomaly detection is a challenging computer vision task, in which 2D-based anomaly detection methods have been extensively studied. However, multimodal anomaly detection based on RGB images and 3D point clouds requires further investigation. The existing methods are mainly inspired by memory bank based methods commonly used in 2D-based anomaly detection, which may cost extra memory for storing mutimodal features. In present study, a novel memoryless method MDSS is proposed for multimodal anomaly detection, which employs a light-weighted student-teacher network and a signed distance function to learn from RGB images and 3D point clouds respectively, and complements the anomaly information from the two modalities. Specifically, a student-teacher network is trained with normal RGB images and masks generated from point clouds by a dynamic loss, and the anomaly score map could be obtained from the discrepancy between the output of student and teacher. Furthermore, the signed distance function learns from normal point clouds to predict the signed distances between points and surface, and the obtained signed distances are used to generate anomaly score map. Subsequently, the anomaly score maps are aligned to generate the final anomaly score map for detection. The experimental results indicate that MDSS is comparable but more stable than the SOTA memory bank based method Shape-guided, and furthermore performs better than other baseline methods.
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data
Authors: Rasoul Jafari Gohari, Laya Aliahmadipour, Ezat Valipour
Date: 2024-09-09 00:00:00
Tasks: Brain Tumor Classification, Federated Learning, Knowledge Distillation

Brain is one the most complex organs in the human body. Due to its complexity, classification of brain tumors still poses a significant challenge, making brain tumors a particularly serious medical issue. Techniques such as Machine Learning (ML) coupled with Magnetic Resonance Imaging (MRI) have paved the way for doctors and medical institutions to classify different types of tumors. However, these techniques suffer from limitations that violate patients privacy. Federated Learning (FL) has recently been introduced to solve such an issue, but the FL itself suffers from limitations like communication costs and dependencies on model architecture, forcing all models to have identical architectures. In this paper, we propose FedBrain-Distill, an approach that leverages Knowledge Distillation (KD) in an FL setting that maintains the users privacy and ensures the independence of FL clients in terms of model architecture. FedBrain-Distill uses an ensemble of teachers that distill their knowledge to a simple student model. The evaluation of FedBrain-Distill demonstrated high-accuracy results for both Independent and Identically Distributed (IID) and non-IID data with substantial low communication costs on the real-world Figshare brain tumor dataset. It is worth mentioning that we used Dirichlet distribution to partition the data into IID and non-IID data. All the implementation details are accessible through our Github repository.
Driving with Prior Maps: Unified Vector Prior Encoding for Autonomous Vehicle Mapping
Authors: Shuang Zeng, Xinyuan Chang, Xinran Liu, Zheng Pan, Xing Wei
Date: 2024-09-09 00:00:00
Tasks: Autonomous Vehicles

High-Definition Maps (HD maps) are essential for the precise navigation and decision-making of autonomous vehicles, yet their creation and upkeep present significant cost and timeliness challenges. The online construction of HD maps using on-board sensors has emerged as a promising solution; however, these methods can be impeded by incomplete data due to occlusions and inclement weather. This paper proposes the PriorDrive framework to addresses these limitations by harnessing the power of prior maps, significantly enhancing the robustness and accuracy of online HD map construction. Our approach integrates a variety of prior maps, such as OpenStreetMap's Standard Definition Maps (SD maps), outdated HD maps from vendors, and locally constructed maps from historical vehicle data. To effectively encode this prior information into online mapping models, we introduce a Hybrid Prior Representation (HPQuery) that standardizes the representation of diverse map elements. At the core of PriorDrive is the Unified Vector Encoder (UVE), which employs hybrid prior embedding and a dual encoding mechanism to process vector data. Furthermore, we propose a segment-level and point-level pre-training strategy that enables the UVE to learn the prior distribution of vector data, thereby improving the encoder's generalizability and performance. Through extensive testing on the nuScenes, Argoverse 2 and OpenLane-V2, we demonstrate that PriorDrive is highly compatible with various online mapping models and substantially improves map prediction capabilities. The integration of prior maps through the PriorDrive framework offers a robust solution to the challenges of single-perception data, paving the way for more reliable autonomous vehicle navigation.
A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs
Authors: Kiran Purohit, Anurag Reddy Parvathgari, Sourangshu Bhattacharya
Date: 2024-08-22 00:00:00
Tasks: Network Pruning

Deep convolutional neural networks (CNNs) have achieved impressive performance in many computer vision tasks. However, their large model sizes require heavy computational resources, making pruning redundant filters from existing pre-trained CNNs an essential task in developing efficient models for resource-constrained devices. Whole-network filter pruning algorithms prune varying fractions of filters from each layer, hence providing greater flexibility. Current whole-network pruning methods are either computationally expensive due to the need to calculate the loss for each pruned filter using a training dataset, or use various heuristic / learned criteria for determining the pruning fractions for each layer. This paper proposes a two-level hierarchical approach for whole-network filter pruning which is efficient and uses the classification loss as the final criterion. The lower-level algorithm (called filter-pruning) uses a sparse-approximation formulation based on linear approximation of filter weights. We explore two algorithms: orthogonal matching pursuit-based greedy selection and a greedy backward pruning approach. The backward pruning algorithm uses a novel closed-form error criterion for efficiently selecting the optimal filter at each stage, thus making the whole algorithm much faster. The higher-level algorithm (called layer-selection) greedily selects the best-pruned layer (pruning using the filter-selection algorithm) using a global pruning criterion. We propose algorithms for two different global-pruning criteria: (1) layer-wise relative error (HBGS), and (2) final classification error (HBGTS). Our suite of algorithms outperforms state-of-the-art pruning methods on ResNet18, ResNet32, ResNet56, VGG16, and ResNext101. Our method reduces the RAM requirement for ResNext101 from 7.6 GB to 1.5 GB and achieves a 94% reduction in FLOPS without losing accuracy on CIFAR-10.
Lagrangian Hashing for Compressed Neural Field Representations
Authors: Shrisudhan Govindarajan, Zeno Sambugaro, Akhmedkhan, Shabanov, Towaki Takikawa, Daniel Rebain, Weiwei Sun, Nicola Conci, Kwang Moo Yi, Andrea Tagliasacchi
Date: 2024-09-09 00:00:00
Tasks: NeRF

We present Lagrangian Hashing, a representation for neural fields combining the characteristics of fast training NeRF methods that rely on Eulerian grids (i.e.~InstantNGP), with those that employ points equipped with features as a way to represent information (e.g. 3D Gaussian Splatting or PointNeRF). We achieve this by incorporating a point-based representation into the high-resolution layers of the hierarchical hash tables of an InstantNGP representation. As our points are equipped with a field of influence, our representation can be interpreted as a mixture of Gaussians stored within the hash table. We propose a loss that encourages the movement of our Gaussians towards regions that require more representation budget to be sufficiently well represented. Our main finding is that our representation allows the reconstruction of signals using a more compact representation without compromising quality.
ICPR 2024 Competition on Safe Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions
Authors: Furqan Ahmed Shaik, Sandeep Nagar, Aiswarya Maturi, Harshit Kumar Sankhla, Dibyendu Ghosh, Anshuman Majumdar, Srikanth Vidapanakal, Kunal Chaudhary, Sunny Manchanda, Girish Varma
Date: 2024-09-09 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Segmentation, Semantic Segmentation

The ICPR 2024 Competition on Safe Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions served as a rigorous platform to evaluate and benchmark state-of-the-art semantic segmentation models under challenging conditions for autonomous driving. Over several months, participants were provided with the IDD-AW dataset, consisting of 5000 high-quality RGB-NIR image pairs, each annotated at the pixel level and captured under adverse weather conditions such as rain, fog, low light, and snow. A key aspect of the competition was the use and improvement of the Safe mean Intersection over Union (Safe mIoU) metric, designed to penalize unsafe incorrect predictions that could be overlooked by traditional mIoU. This innovative metric emphasized the importance of safety in developing autonomous driving systems. The competition showed significant advancements in the field, with participants demonstrating models that excelled in semantic segmentation and prioritized safety and robustness in unstructured and adverse conditions. The results of the competition set new benchmarks in the domain, highlighting the critical role of safety in deploying autonomous vehicles in real-world scenarios. The contributions from this competition are expected to drive further innovation in autonomous driving technology, addressing the critical challenges of operating in diverse and unpredictable environments.
FIF-UNet: An Efficient UNet Using Feature Interaction and Fusion for Medical Image Segmentation
Authors: Xiaolin Gou, Chuanlin Liao, Jizhe Zhou, Fengshuo Ye, Yi Lin
Date: 2024-09-09 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Nowadays, pre-trained encoders are widely used in medical image segmentation because of their ability to capture complex feature representations. However, the existing models fail to effectively utilize the rich features obtained by the pre-trained encoder, resulting in suboptimal segmentation results. In this work, a novel U-shaped model, called FIF-UNet, is proposed to address the above issue, including three plug-and-play modules. A channel spatial interaction module (CSI) is proposed to obtain informative features by establishing the interaction between encoder stages and corresponding decoder stages. A cascaded conv-SE module (CoSE) is designed to enhance the representation of critical features by adaptively assigning importance weights on different feature channels. A multi-level fusion module (MLF) is proposed to fuse the multi-scale features from the decoder stages, ensuring accurate and robust final segmentation. Comprehensive experiments on the Synapse and ACDC datasets demonstrate that the proposed FIF-UNet outperforms existing state-of-the-art methods, which achieves the highest average DICE of 86.05% and 92.58%, respectively.
Open-World Dynamic Prompt and Continual Visual Representation Learning
Authors: Youngeun Kim, Jun Fang, Qin Zhang, Zhaowei Cai, Yantao Shen, Rahul Duggal, Dripta S. Raychaudhuri, Zhuowen Tu, Yifan Xing, Onkar Dabeer
Date: 2024-09-09 00:00:00
Tasks: Continual Learning, Image Retrieval, Prompt Learning, Representation Learning

The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significant challenge in effectively generalizing to unseen test-time classes. To address this challenge, we introduce a new practical CL setting tailored for open-world visual representation learning. In this setting, subsequent data streams systematically introduce novel classes that are disjoint from those seen in previous training phases, while also remaining distinct from the unseen test classes. In response, we present Dynamic Prompt and Representation Learner (DPaRL), a simple yet effective Prompt-based CL (PCL) method. Our DPaRL learns to generate dynamic prompts for inference, as opposed to relying on a static prompt pool in previous PCL methods. In addition, DPaRL jointly learns dynamic prompt generation and discriminative representation at each training stage whereas prior PCL methods only refine the prompt learning throughout the process. Our experimental results demonstrate the superiority of our approach, surpassing state-of-the-art methods on well-established open-world image retrieval benchmarks by an average of 4.7% improvement in Recall@1 performance.
Fitting Skeletal Models via Graph-based Learning
Authors: Nicolás Gaggion, Enzo Ferrante, Beatriz Paniagua, Jared Vicory
Date: 2024-09-09 00:00:00
Tasks: Hippocampus

Skeletonization is a popular shape analysis technique that models an object's interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming process requiring much manual parameter tuning. Recently, machine learning-based methods have shown promise for generating s-reps from object boundaries. In this work, we propose a new skeletonization method which leverages graph convolutional networks to produce skeletal representations (s-reps) from dense segmentation masks. The method is evaluated on both synthetic data and real hippocampus segmentations, achieving promising results and fast inference.
Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems
Authors: Jianheng Liu, Chunran Zheng, Yunfei Wan, Bowen Wang, Yixi Cai, Fu Zhang
Date: 2024-09-09 00:00:00
Tasks: NeRF, Surface Reconstruction

This paper presents a unified surface reconstruction and rendering framework for LiDAR-visual systems, integrating Neural Radiance Fields (NeRF) and Neural Distance Fields (NDF) to recover both appearance and structural information from posed images and point clouds. We address the structural visible gap between NeRF and NDF by utilizing a visible-aware occupancy map to classify space into the free, occupied, visible unknown, and background regions. This classification facilitates the recovery of a complete appearance and structure of the scene. We unify the training of the NDF and NeRF using a spatial-varying scale SDF-to-density transformation for levels of detail for both structure and appearance. The proposed method leverages the learned NDF for structure-aware NeRF training by an adaptive sphere tracing sampling strategy for accurate structure rendering. In return, NeRF further refines structural in recovering missing or fuzzy structures in the NDF. Extensive experiments demonstrate the superior quality and versatility of the proposed method across various scenarios. To benefit the community, the codes will be released at \url{https://github.com/hku-mars/M2Mapping}.
RAL:Redundancy-Aware Lipreading Model Based on Differential Learning with Symmetric Views
Authors: Zejun Gu, Junxia jiang
Date: 2024-09-09 00:00:00
Tasks: Lipreading, Lip Reading

Lip reading involves interpreting a speaker's speech by analyzing sequences of lip movements. Currently, most models regard the left and right halves of the lips as a symmetrical whole, lacking a thorough investigation of their differences. However, the left and right halves of the lips are not always symmetrical, and the subtle differences between them contain rich semantic information. In this paper, we propose a differential learning strategy with symmetric views (DLSV) to address this issue. Additionally, input images often contain a lot of redundant information unrelated to recognition results, which can degrade the model's performance. We present a redundancy-aware operation (RAO) to reduce it. Finally, to leverage the relational information between symmetric views and within each view, we further design an adaptive cross-view interaction module (ACVI). Experiments on LRW and LRW-1000 datasets fully demonstrate the effectiveness of our approach.