pwc-archive/papers-with-abstracts huggingface dataset:
Refinement Module based on Parse Graph of Feature Map for Human Pose Estimation
Authors: Shibang Liu, Xuemei Xie, Guangming Shi
Date: 2025-01-19 00:00:00
Tasks: Keypoint Detection, Pose Estimation

The parse graph play a crucial role in enhancing the performance of human pose estimation (HPE). Its key advantage lies in its hierarchical structure, like a tree structure, and context relations among nodes, which enable more accurate for inference. To equip models with the advantage of parse graphs, many researchers predefine the parse graph of body structure for HPE. However, these frameworks struggle to adapt to instances that deviate from the predefined parse graph and they are often parameter-heavy. Unlike them, we view the feature map holistically, much like the human body. It can be optimized using parse graphs, where nodes' implicit feature representation boosts adaptability, avoiding rigid structural limitations. In this paper, we design the Refinement Module based on the Parse Graph of feature map (RMPG), which includes two stages: top-down decomposition and bottom-up combination. In the first stage, the feature map is constructed into a tree structure through recursive decomposition, with each node representing a sub-feature map, thereby achieving hierarchical modeling of features. In the second stage, context information is calculated and sub-feature maps with context are recursively connected to gradually build a refined feature map. Additionally, we design a hierarchical network with fewer parameters using multiple RMPG modules to model the context relations and hierarchies in the parse graph of body structure for HPE, some of which are supervised to obtain context relations among body parts. Our network achieves excellent results on multiple mainstream human pose datasets and the effectiveness of RMPG is proven on different methods. The code of RMPG will be open.
Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection
Authors: Zhipeng Yu, Qianqian Xu, Yangbangyan Jiang, Yingfei Sun, Qingming Huang
Date: 2025-01-19 00:00:00
Tasks: Face Recognition, Image Retrieval, Metric Learning

The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing noisy label learning methods designed for DML mainly discard suspicious noisy samples, resulting in a waste of the training data. To address this issue, we propose a noise-robust DML framework with SubGroup-based Positive-pair Selection (SGPS), which constructs reliable positive pairs for noisy samples to enhance the sample utilization. Specifically, SGPS first effectively identifies clean and noisy samples by a probability-based clean sample selectionstrategy. To further utilize the remaining noisy samples, we discover their potential similar samples based on the subgroup information given by a subgroup generation module and then aggregate them into informative positive prototypes for each noisy sample via a positive prototype generation module. Afterward, a new contrastive loss is tailored for the noisy samples with their selected positive pairs. SGPS can be easily integrated into the training process of existing pair-wise DML tasks, like image retrieval and face recognition. Extensive experiments on multiple synthetic and real-world large-scale label noise datasets demonstrate the effectiveness of our proposed method. Without any bells and whistles, our SGPS framework outperforms the state-of-the-art noisy label DML methods. Code is available at \url{https://github.com/smuelpeng/SGPS-NoiseFreeDML}.
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space
Authors: Linchao Pan, Can Gao, Jie zhou, Jinbao Wang
Date: 2025-01-19 00:00:00
Tasks: Contrastive Learning, Learning with noisy labels, Memorization

Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may occur during the training and inference stage. Such open-world noisy labels may significantly impact the performance of LNL methods. In this study, we propose a novel dual-space joint learning method to robustly handle the open-world noise. To mitigate model overfitting on closed-set and open-set noises, a dual representation space is constructed by two networks. One is a projection network that learns shared representations in the prototype space, while the other is a One-Vs-All (OVA) network that makes predictions using unique semantic representations in the class-independent space. Then, bi-level contrastive learning and consistency regularization are introduced in two spaces to enhance the detection capability for data with unknown classes. To benefit from the memorization effects across different types of samples, class-independent margin criteria are designed for sample identification, which selects clean samples, weights closed-set noise, and filters open-set noise effectively. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods and achieves an average accuracy improvement of 4.55\% and an AUROC improvement of 6.17\% on CIFAR80N.
Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis
Authors: Olaf Hellwich, Niek Andresen, Katharina Hohlbaum, Marcus N. Boon, Monika Kwiatkowski, Simon Matern, Patrik Reiske, Henning Sprekeler, Christa ThöneReineke, Lars Lewejohann, Huma Ghani Zada, Michael Brück, Soledad Traverso
Date: 2025-01-19 00:00:00

Tracking mouse body parts in video is often incomplete due to occlusions such that - e.g. - subsequent action and behavior analysis is impeded. In this conceptual work, videos from several perspectives are integrated via global exterior camera orientation; body part positions are estimated by 3D triangulation and bundle adjustment. Consistency of overall 3D track reconstruction is achieved by introduction of a 3D mouse model, deep-learned body part movements, and global motion-track smoothness constraint. The resulting 3D body and body part track estimates are substantially more complete than the original single-frame-based body part detection, therefore, allowing improved animal behavior analysis.
Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction
Authors: CongCong Li, Jin Wang, Xiaomeng Wang, Xingchen Zhou, Wei Wu, Yuzhi Zhang, Tongyi Cao
Date: 2025-01-19 00:00:00
Tasks: 3DGS, Autonomous Driving, Transparent objects

3D car modeling is crucial for applications in autonomous driving systems, virtual and augmented reality, and gaming. However, due to the distinctive properties of cars, such as highly reflective and transparent surface materials, existing methods often struggle to achieve accurate 3D car reconstruction.To address these limitations, we propose Car-GS, a novel approach designed to mitigate the effects of specular highlights and the coupling of RGB and geometry in 3D geometric and shading reconstruction (3DGS). Our method incorporates three key innovations: First, we introduce view-dependent Gaussian primitives to effectively model surface reflections. Second, we identify the limitations of using a shared opacity parameter for both image rendering and geometric attributes when modeling transparent objects. To overcome this, we assign a learnable geometry-specific opacity to each 2D Gaussian primitive, dedicated solely to rendering depth and normals. Third, we observe that reconstruction errors are most prominent when the camera view is nearly orthogonal to glass surfaces. To address this issue, we develop a quality-aware supervision module that adaptively leverages normal priors from a pre-trained large-scale normal model.Experimental results demonstrate that Car-GS achieves precise reconstruction of car surfaces and significantly outperforms prior methods. The project page is available at https://lcc815.github.io/Car-GS.
Transfer Learning Strategies for Pathological Foundation Models: A Systematic Evaluation in Brain Tumor Classification
Authors: Ken Enda, Yoshitaka Oda, Zen-ichi Tanei, Kenichi Satoh, Hiroaki Motegi, Terasaka Shunsuke, Shigeru Yamaguchi, Takahiro Ogawa, Wang Lei, Masumi Tsuda, Shinya Tanaka
Date: 2025-01-19 00:00:00
Tasks: Brain Tumor Classification, Diagnostic, Robust classification, Transfer Learning

Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a systematic evaluation of transfer learning strategies for brain tumor classification using these models. We analyzed 254 cases comprising five major tumor types: glioblastoma, astrocytoma, oligodendroglioma, primary central nervous system lymphoma, and metastatic tumors. Comparing state-of-the-art foundation models with conventional approaches, we found that foundation models demonstrated robust classification performance with as few as 10 patches per case, despite the traditional assumption that extensive per-case image sampling is necessary. Furthermore, our evaluation revealed that simple transfer learning strategies like linear probing were sufficient, while fine-tuning often degraded model performance. These findings suggest a paradigm shift from "training encoders on extensive pathological data" to "querying pre-trained encoders with labeled datasets", providing practical implications for implementing AI-assisted diagnosis in clinical pathology.
HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
Authors: Pengcheng Dong, Wenbo Wan, Huaxiang Zhang, Shuai Li, Sujuan Hou, Jiande Sun
Date: 2025-01-19 00:00:00
Tasks: Action Recognition, Relation Classification, Skeleton Based Action Recognition, Video Understanding

In recent years, action recognition has received much attention and wide application due to its important role in video understanding. Most of the researches on action recognition methods focused on improving the performance via various deep learning methods rather than the classification of skeleton points. The topological modeling between skeleton points and body parts was seldom considered. Although some studies have used a data-driven approach to classify the topology of the skeleton point, the nature of the skeleton point in terms of kinematics has not been taken into consideration. Therefore, in this paper, we draw on the theory of kinematics to adapt the topological relations of the skeleton point and propose a topological relation classification based on body parts and distance from core of body. To synthesize these topological relations for action recognition, we propose a novel Hypergraph Fusion Graph Convolutional Network (HFGCN). In particular, the proposed model is able to focus on the human skeleton points and the different body parts simultaneously, and thus construct the topology, which improves the recognition accuracy obviously. We use a hypergraph to represent the categorical relationships of these skeleton points and incorporate the hypergraph into a graph convolution network to model the higher-order relationships among the skeleton points and enhance the feature representation of the network. In addition, our proposed hypergraph attention module and hypergraph graph convolution module optimize topology modeling in temporal and channel dimensions, respectively, to further enhance the feature representation of the network. We conducted extensive experiments on three widely used datasets.The results validate that our proposed method can achieve the best performance when compared with the state-of-the-art skeleton-based methods.
Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis
Authors: Md. Shaheenur Islam Sumon, Khandaker Reajul Islam, Tanzila Rafique, Gazi Shamim Hassan, Md. Sakib Abrar Hossain, Kanchon Kanti Podder, Noha Barhom, Faleh Tamimi, Abdulrahman Alqahtani, Muhammad E. H. Chowdhury
Date: 2025-01-19 00:00:00
Tasks: Attribute, Deep Learning

Cephalometric analysis is essential for the diagnosis and treatment planning of orthodontics. In lateral cephalograms, however, the manual detection of anatomical landmarks is a time-consuming procedure. Deep learning solutions hold the potential to address the time constraints associated with certain tasks; however, concerns regarding their performance have been observed. To address this critical issue, we proposed an end-to-end cascaded deep learning framework (Self-CepahloNet) for the task, which demonstrated benchmark performance over the ISBI 2015 dataset in predicting 19 dental landmarks. Due to their adaptive nodal capabilities, Self-ONN (self-operational neural networks) demonstrate superior learning performance for complex feature spaces over conventional convolutional neural networks. To leverage this attribute, we introduced a novel self-bottleneck in the HRNetV2 (High Resolution Network) backbone, which has exhibited benchmark performance on the ISBI 2015 dataset for the dental landmark detection task. Our first-stage results surpassed previous studies, showcasing the efficacy of our singular end-to-end deep learning model, which achieved a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2mm range for the Test1 and Test2 datasets. Moreover, the second stage significantly improved overall performance, yielding an impressive 82.25% average success rate for the datasets above within the same 2mm distance. Furthermore, external validation was conducted using the PKU cephalogram dataset. Our model demonstrated a commendable success rate of 75.95% within the 2mm range.
SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality
Authors: Roberto Daza, Lin Shengkai, Aythami Morales, Julian Fierrez, Katashi Nagao
Date: 2025-01-19 00:00:00

This work introduces SMARTe-VR, a platform for student monitoring in an immersive virtual reality environment designed for online education. SMARTe-VR aims to collect data for adaptive learning, focusing on facial biometrics and learning metadata. The platform allows instructors to create customized learning sessions with video lectures, featuring an interface with an AutoQA system to evaluate understanding, interaction tools (e.g., textbook highlighting and lecture tagging), and real-time feedback. Furthermore, we released a dataset that contains 5 research challenges with data from 10 users in VR-based TOEIC sessions. This data set, which spans more than 25 hours, includes facial features, learning metadata, 450 responses, difficulty levels of the questions, concept tags, and understanding labels. Alongside the database, we present preliminary experiments using Item Response Theory models, adapted for understanding detection using facial features. Two architectures were explored: a Temporal Convolutional Network for local features and a Multilayer Perceptron for global features.
AI Based Font Pair Suggestion Modelling For Graphic Design
Authors: Aryan Singh, Sumithra Bhakthavatsalam
Date: 2025-01-19 00:00:00
Tasks: Language Modeling, Language Modelling

One of the key challenges of AI generated designs in Microsoft Designer is selecting the most contextually relevant and novel fonts for the design suggestions. Previous efforts involved manually mapping design intent to fonts. Though this was high quality, this method does not scale for a large number of fonts (3000+) and numerous user intents for graphic design. In this work we create font visual embeddings, a font stroke width algorithm, a font category to font mapping dataset, an LLM-based category utilization description and a lightweight, low latency knowledge-distilled mini language model (Mini LM V2) to recommend multiple pairs of contextual heading and subheading fonts for beautiful and intuitive designs. We also utilize a weighted scoring mechanism, nearest neighbor approach and stratified sampling to rank the font pairs and bring novelty to the predictions.
Rethinking Early-Fusion Strategies for Improved Multimodal Image Segmentation
Authors: Zhengwen Shen, Yulian Li, Han Zhang, Yuchen Weng, Jun Wang
Date: 2025-01-19 00:00:00
Tasks: Decoder, Image Segmentation, Segmentation, Semantic Segmentation

RGB and thermal image fusion have great potential to exhibit improved semantic segmentation in low-illumination conditions. Existing methods typically employ a two-branch encoder framework for multimodal feature extraction and design complicated feature fusion strategies to achieve feature extraction and fusion for multimodal semantic segmentation. However, these methods require massive parameter updates and computational effort during the feature extraction and fusion. To address this issue, we propose a novel multimodal fusion network (EFNet) based on an early fusion strategy and a simple but effective feature clustering for training efficient RGB-T semantic segmentation. In addition, we also propose a lightweight and efficient multi-scale feature aggregation decoder based on Euclidean distance. We validate the effectiveness of our method on different datasets and outperform previous state-of-the-art methods with lower parameters and computation.
Green Video Camouflaged Object Detection
Authors: Xinyu Wang, Hong-Shuo Chen, Zhiruo Zhou, Suya You, Azad M. Madni, C. -C. Jay Kuo
Date: 2025-01-19 00:00:00
Tasks: Object, object-detection, Object Detection

Camouflaged object detection (COD) aims to distinguish hidden objects embedded in an environment highly similar to the object. Conventional video-based COD (VCOD) methods explicitly extract motion cues or employ complex deep learning networks to handle the temporal information, which is limited by high complexity and unstable performance. In this work, we propose a green VCOD method named GreenVCOD. Built upon a green ICOD method, GreenVCOD uses long- and short-term temporal neighborhoods (TN) to capture joint spatial/temporal context information for decision refinement. Experimental results show that GreenVCOD offers competitive performance compared to state-of-the-art VCOD benchmarks.
Know "No'' Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIP
Authors: Junsung Park, Jungbeom Lee, Jongyoon Song, Sangwon Yu, Dahuin Jung, Sungroh Yoon
Date: 2025-01-19 00:00:00
Tasks: Image Generation, Image Segmentation, Language Modeling, Language Modelling, Large Language Model, Negation, Semantic Segmentation, Sentence, Text to Image Generation, Text-to-Image Generation

While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation - such as failing to differentiate concepts like "parking" from "no parking" - poses substantial challenges. By analyzing the data used in the public CLIP model's pre-training, we posit this limitation stems from a lack of negation-inclusive data. To address this, we introduce data generation pipelines that employ a large language model (LLM) and a multimodal LLM to produce negation-inclusive captions. Fine-tuning CLIP with data generated from our pipelines, we develop NegationCLIP, which enhances negation awareness while preserving the generality. Moreover, to enable a comprehensive evaluation of negation understanding, we propose NegRefCOCOg-a benchmark tailored to test VLMs' ability to interpret negation across diverse expressions and positions within a sentence. Experiments on various CLIP architectures validate the effectiveness of our data generation pipelines in enhancing CLIP's ability to perceive negation accurately. Additionally, NegationCLIP's enhanced negation awareness has practical applications across various multimodal tasks, demonstrated by performance gains in text-to-image generation and referring image segmentation.
Explainable Adversarial Attacks on Coarse-to-Fine Classifiers
Authors: Akram Heidarizadeh, Connor Hatfield, Lorenzo Lazzarotto, HanQin Cai, George Atia
Date: 2025-01-19 00:00:00

Traditional adversarial attacks typically aim to alter the predicted labels of input images by generating perturbations that are imperceptible to the human eye. However, these approaches often lack explainability. Moreover, most existing work on adversarial attacks focuses on single-stage classifiers, but multi-stage classifiers are largely unexplored. In this paper, we introduce instance-based adversarial attacks for multi-stage classifiers, leveraging Layer-wise Relevance Propagation (LRP), which assigns relevance scores to pixels based on their influence on classification outcomes. Our approach generates explainable adversarial perturbations by utilizing LRP to identify and target key features critical for both coarse and fine-grained classifications. Unlike conventional attacks, our method not only induces misclassification but also enhances the interpretability of the model's behavior across classification stages, as demonstrated by experimental results.
A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial Differences
Authors: Sijun Dong, Fangcheng Zuo, Geng Chen, Siming Fu, Xiaoliang Meng
Date: 2025-01-19 00:00:00
Tasks: Change Detection, Earth Observation

Change detection in remote sensing imagery is a critical technique for Earth observation, primarily focusing on pixel-level segmentation of change regions between bi-temporal images. The essence of pixel-level change detection lies in determining whether corresponding pixels in bi-temporal images have changed. In deep learning, the spatial and channel dimensions of feature maps represent different information from the original images. In this study, we found that in change detection tasks, difference information can be computed not only from the spatial dimension of bi-temporal features but also from the channel dimension. Therefore, we designed the Channel-Spatial Difference Weighting (CSDW) module as an aggregation-distribution mechanism for bi-temporal features in change detection. This module enhances the sensitivity of the change detection model to difference features. Additionally, bi-temporal images share the same geographic location and exhibit strong inter-image correlations. To construct the correlation between bi-temporal images, we designed a decoding structure based on the Layer-Exchange (LE) method to enhance the interaction of bi-temporal features. Comprehensive experiments on the CLCD, PX-CLCD, LEVIR-CD, and S2Looking datasets demonstrate that the proposed LENet model significantly improves change detection performance. The code and pre-trained models will be available at: https://github.com/dyzy41/lenet.
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction
Authors: Liyan Sun, Shaocong Yu, Chi Zhang, Xinghao Ding
Date: 2025-01-18 00:00:00
Tasks: MRI Reconstruction, Self-Supervised Learning

Reconstructing MR images using deep neural networks from undersampled k-space data without using fully sampled training references offers significant value in practice, which is a self-supervised regression problem calling for effective prior knowledge and supervision. The Siamese architectures are motivated by the definition "invariance" and shows promising results in unsupervised visual representative learning. Building homologous transformed images and avoiding trivial solutions are two major challenges in Siamese-based self-supervised model. In this work, we explore Siamese architecture for MRI reconstruction in a self-supervised training fashion called SiamRecon. We show the proposed approach mimics an expectation maximization algorithm. The alternative optimization provide effective supervision signal and avoid collapse. The proposed SiamRecon achieves the state-of-the-art reconstruction accuracy in the field of self-supervised learning on both single-coil brain MRI and multi-coil knee MRI.