pwc-archive/papers-with-abstracts huggingface dataset:
Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs
Authors: Mahshid shiri, Alessandro Bruno, Daniele Loiacono
Date: 2025-03-13 00:00:00
Tasks: Image Generation

Generative Adversarial Networks (GANs) have many potential medical imaging applications. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models cannot scale to high-resolution or are susceptible to patchy artifacts. In this work, we propose an end-to-end novel GAN architecture that uses Conditional Random field (CRF) to model dependencies so that it can generate consistent 3D medical Images without exploiting memory. To achieve this purpose, the generator is divided into two parts during training, the first part produces an intermediate representation and CRF is applied to this intermediate representation to capture correlations. The second part of the generator produces a random sub-volume of image using a subset of the intermediate representation. This structure has two advantages: first, the correlations are modeled by using the features that the generator is trying to optimize. Second, the generator can generate full high-resolution images during inference. Experiments on Lung CTs and Brain MRIs show that our architecture outperforms state-of-the-art while it has lower memory usage and less complexity.
JPEG Compliant Compression for Both Human and Machine, A Report
Authors: Linfeng Ye
Date: 2025-03-13 00:00:00
Tasks: Image Compression

Deep Neural Networks (DNNs) have become an integral part of our daily lives, especially in vision-related applications. However, the conventional lossy image compression algorithms are primarily designed for the Human Vision System (HVS), which can non-trivially compromise the DNNs' validation accuracy after compression, as noted in \cite{liu2018deepn}. Thus developing an image compression algorithm for both human and machine (DNNs) is on the horizon. To address the challenge mentioned above, in this paper, we first formulate the image compression as a multi-objective optimization problem which take both human and machine prespectives into account, then we solve it by linear combination, and proposed a novel distortion measure for both human and machine, dubbed Human and Machine-Oriented Error (HMOE). After that, we develop Human And Machine Oriented Soft Decision Quantization (HMOSDQ) based on HMOE, a lossy image compression algorithm for both human and machine (DNNs), and fully complied with JPEG format. In order to evaluate the performance of HMOSDQ, finally we conduct the experiments for two pre-trained well-known DNN-based image classifiers named Alexnet \cite{Alexnet} and VGG-16 \cite{simonyan2014VGG} on two subsets of the ImageNet \cite{deng2009imagenet} validation set: one subset included images with shorter side in the range of 496 to 512, while the other included images with shorter side in the range of 376 to 384. Our results demonstrate that HMOSDQ outperforms the default JPEG algorithm in terms of rate-accuracy and rate-distortion performance. For the Alexnet comparing with the default JPEG algorithm, HMOSDQ can improve the validation accuracy by more than $0.81\%$ at $0.61$ BPP, or equivalently reduce the compression rate of default JPEG by $9.6\times$ while maintaining the same validation accuracy.
PolyRoof: Precision Roof Polygonization in Urban Residential Building with Graph Neural Networks
Authors: Chaikal Amrullah, Daniel Panangian, Ksenia Bittner
Date: 2025-03-13 00:00:00
Tasks: Position

The growing demand for detailed building roof data has driven the development of automated extraction methods to overcome the inefficiencies of traditional approaches, particularly in handling complex variations in building geometries. Re:PolyWorld, which integrates point detection with graph neural networks, presents a promising solution for reconstructing high-detail building roof vector data. This study enhances Re:PolyWorld's performance on complex urban residential structures by incorporating attention-based backbones and additional area segmentation loss. Despite dataset limitations, our experiments demonstrated improvements in point position accuracy (1.33 pixels) and line distance accuracy (14.39 pixels), along with a notable increase in the reconstruction score to 91.99%. These findings highlight the potential of advanced neural network architectures in addressing the challenges of complex urban residential geometries.
Multi-Domain Biometric Recognition using Body Embeddings
Authors: Anirudh Nanduri, Siyuan Huang, Rama Chellappa
Date: 2025-03-13 00:00:00
Tasks: Person Identification, Person Re-Identification, Triplet

Biometric recognition becomes increasingly challenging as we move away from the visible spectrum to infrared imagery, where domain discrepancies significantly impact identification performance. In this paper, we show that body embeddings perform better than face embeddings for cross-spectral person identification in medium-wave infrared (MWIR) and long-wave infrared (LWIR) domains. Due to the lack of multi-domain datasets, previous research on cross-spectral body identification - also known as Visible-Infrared Person Re-Identification (VI-ReID) - has primarily focused on individual infrared bands, such as near-infrared (NIR) or LWIR, separately. We address the multi-domain body recognition problem using the IARPA Janus Benchmark Multi-Domain Face (IJB-MDF) dataset, which enables matching of short-wave infrared (SWIR), MWIR, and LWIR images against RGB (VIS) images. We leverage a vision transformer architecture to establish benchmark results on the IJB-MDF dataset and, through extensive experiments, provide valuable insights into the interrelation of infrared domains, the adaptability of VIS-pretrained models, the role of local semantic features in body-embeddings, and effective training strategies for small datasets. Additionally, we show that finetuning a body model, pretrained exclusively on VIS data, with a simple combination of cross-entropy and triplet losses achieves state-of-the-art mAP scores on the LLCM dataset.
Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques
Authors: Muhammad Waseem, Chung-Hsuan Huang, Muhammad Muzzammil Sajjad, Laraib Haider Naqvi, Yaqoob Majeed, Tanzeel Ur Rehman, Tayyaba Nadeem
Date: 2025-03-13 00:00:00
Tasks: Classification, Computational Efficiency, Quantization, Transfer Learning

Tomato maturity plays a pivotal role in optimizing harvest timing and ensuring product quality, but current methods struggle to achieve high accuracy along computational efficiency simultaneously. Existing deep learning approaches, while accurate, are often too computationally demanding for practical use in resource-constrained agricultural settings. In contrast, simpler techniques fail to capture the nuanced features needed for precise classification. This study aims to develop a computationally efficient tomato classification model using the ResNet-18 architecture optimized through transfer learning, pruning, and quantization techniques. Our objective is to address the dual challenge of maintaining high accuracy while enabling real-time performance on low-power edge devices. Then, these models were deployed on an edge device to investigate their performance for tomato maturity classification. The quantized model achieved an accuracy of 97.81%, with an average classification time of 0.000975 seconds per image. The pruned and auto-tuned model also demonstrated significant improvements in deployment metrics, further highlighting the benefits of optimization techniques. These results underscore the potential for a balanced solution that meets the accuracy and efficiency demands of modern agricultural production, paving the way for practical, real-world deployment in resource-limited environments.
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models
Authors: Akshat Ramachandran, Mingyu Lee, Huan Xu, Souvik Kundu, Tushar Krishna
Date: 2025-03-13 00:00:00
Tasks: channel selection, Contrastive Learning, Data Free Quantization, GPU, Mamba, Outlier Detection, Quantization

We present OuroMamba, the first data-free post-training quantization (DFQ) method for vision Mamba-based models (VMMs). We identify two key challenges in enabling DFQ for VMMs, (1) VMM's recurrent state transitions restricts capturing of long-range interactions and leads to semantically weak synthetic data, (2) VMM activations exhibit dynamic outlier variations across time-steps, rendering existing static PTQ techniques ineffective. To address these challenges, OuroMamba presents a two-stage framework: (1) OuroMamba-Gen to generate semantically rich and meaningful synthetic data. It applies contrastive learning on patch level VMM features generated through neighborhood interactions in the latent state space, (2) OuroMamba-Quant to employ mixed-precision quantization with lightweight dynamic outlier detection during inference. In specific, we present a thresholding based outlier channel selection strategy for activations that gets updated every time-step. Extensive experiments across vision and generative tasks show that our data-free OuroMamba surpasses existing data-driven PTQ techniques, achieving state-of-the-art performance across diverse quantization settings. Additionally, we implement efficient GPU kernels to achieve practical latency speedup of up to 2.36x. Code will be released soon.
Unlocking Open-Set Language Accessibility in Vision Models
Authors: Fawaz Sammani, Jonas Fischer, Nikos Deligiannis
Date: 2025-03-14 00:00:00

Visual classifiers offer high-dimensional feature representations that are challenging to interpret and analyze. Text, in contrast, provides a more expressive and human-friendly interpretable medium for understanding and analyzing model behavior. We propose a simple, yet powerful method for reformulating any visual classifier so that it can be accessed with open-set text queries without compromising its original performance. Our approach is label-free, efficient, and preserves the underlying classifier's distribution and reasoning processes. We thus unlock several text-based interpretability applications for any classifier. We apply our method on 40 visual classifiers and demonstrate two primary applications: 1) building both label-free and zero-shot concept bottleneck models and therefore converting any classifier to be inherently-interpretable and 2) zero-shot decoding of visual features into natural language. In both applications, we achieve state-of-the-art results, greatly outperforming existing works. Our method enables text approaches for interpreting visual classifiers.
Enhanced Multi-View Pedestrian Detection Using Probabilistic Occupancy Volume
Authors: Reef Alturki, Adrian Hilton, Jean-yves Guillemaut
Date: 2025-03-14 00:00:00
Tasks: 3D Reconstruction, multi-view detection, Pedestrian Detection

Occlusion poses a significant challenge in pedestrian detection from a single view. To address this, multi-view detection systems have been utilized to aggregate information from multiple perspectives. Recent advances in multi-view detection utilized an early-fusion strategy that strategically projects the features onto the ground plane, where detection analysis is performed. A promising approach in this context is the use of 3D feature-pulling technique, which constructs a 3D feature volume of the scene by sampling the corresponding 2D features for each voxel. However, it creates a 3D feature volume of the whole scene without considering the potential locations of pedestrians. In this paper, we introduce a novel model that efficiently leverages traditional 3D reconstruction techniques to enhance deep multi-view pedestrian detection. This is accomplished by complementing the 3D feature volume with probabilistic occupancy volume, which is constructed using the visual hull technique. The probabilistic occupancy volume focuses the model's attention on regions occupied by pedestrians and improves detection accuracy. Our model outperforms state-of-the-art models on the MultiviewX dataset, with an MODA of 97.3%, while achieving competitive performance on the Wildtrack dataset.
Rethinking Rotation-Invariant Recognition of Fine-grained Shapes from the Perspective of Contour Points
Authors: Yanjie Xu, Handing Xu, Tianmu Wang, Yaguan Li, Yunzhi Chen, Zhenguo Nie
Date: 2025-03-14 00:00:00

Rotation-invariant recognition of shapes is a common challenge in computer vision. Recent approaches have significantly improved the accuracy of rotation-invariant recognition by encoding the rotational invariance of shapes as hand-crafted image features and introducing deep neural networks. However, the methods based on pixels have too much redundant information, and the critical geometric information is prone to early leakage, resulting in weak rotation-invariant recognition of fine-grained shapes. In this paper, we reconsider the shape recognition problem from the perspective of contour points rather than pixels. We propose an anti-noise rotation-invariant convolution module based on contour geometric aware for fine-grained shape recognition. The module divides the shape contour into multiple local geometric regions(LGA), where we implement finer-grained rotation-invariant coding in terms of point topological relations. We provide a deep network composed of five such cascaded modules for classification and retrieval experiments. The results show that our method exhibits excellent performance in rotation-invariant recognition of fine-grained shapes. In addition, we demonstrate that our method is robust to contour noise and the rotation centers. The source code is available at https://github.com/zhenguonie/ANRICN_CGA.
Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection
Authors: Chuhan Zhang, Chaoyang Zhu, Pingcheng Dong, Long Chen, Dong Zhang
Date: 2025-03-14 00:00:00
Tasks: object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection, Transfer Learning

In pursuit of detecting unstinted objects that extend beyond predefined categories, prior arts of open-vocabulary object detection (OVD) typically resort to pretrained vision-language models (VLMs) for base-to-novel category generalization. However, to mitigate the misalignment between upstream image-text pretraining and downstream region-level perception, additional supervisions are indispensable, eg, image-text pairs or pseudo annotations generated via self-training strategies. In this work, we propose CCKT-Det trained without any extra supervision. The proposed framework constructs a cyclic and dynamic knowledge transfer from language queries and visual region features extracted from VLMs, which forces the detector to closely align with the visual-semantic space of VLMs. Specifically, 1) we prefilter and inject semantic priors to guide the learning of queries, and 2) introduce a regional contrastive loss to improve the awareness of queries on novel objects. CCKT-Det can consistently improve performance as the scale of VLMs increases, all while requiring the detector at a moderate level of computation overhead. Comprehensive experimental results demonstrate that our method achieves performance gain of +2.9% and +10.2% AP50 over previous state-of-the-arts on the challenging COCO benchmark, both without and with a stronger teacher model. The code is provided at https://github.com/ZCHUHan/CCKT-Det.
Observation-Graph Interaction and Key-Detail Guidance for Vision and Language Navigation
Authors: Yifan Xie, Binkai Ou, Fei Ma, Yaohua Liu
Date: 2025-03-14 00:00:00
Tasks: cross-modal alignment, Navigate, Vision and Language Navigation

Vision and Language Navigation (VLN) requires an agent to navigate through environments following natural language instructions. However, existing methods often struggle with effectively integrating visual observations and instruction details during navigation, leading to suboptimal path planning and limited success rates. In this paper, we propose OIKG (Observation-graph Interaction and Key-detail Guidance), a novel framework that addresses these limitations through two key components: (1) an observation-graph interaction module that decouples angular and visual information while strengthening edge representations in the navigation space, and (2) a key-detail guidance module that dynamically extracts and utilizes fine-grained location and object information from instructions. By enabling more precise cross-modal alignment and dynamic instruction interpretation, our approach significantly improves the agent's ability to follow complex navigation instructions. Extensive experiments on the R2R and RxR datasets demonstrate that OIKG achieves state-of-the-art performance across multiple evaluation metrics, validating the effectiveness of our method in enhancing navigation precision through better observation-instruction alignment.
Comparative Analysis of Advanced AI-based Object Detection Models for Pavement Marking Quality Assessment during Daytime
Authors: Gian Antariksa, Rohir Chakraborty, Shriyank Somvanshi, Subasish Das, Mohammad Jalayer, Deep Rameshkumar Patel, David Mills
Date: 2025-03-14 00:00:00
Tasks: Computational Efficiency, object-detection, Object Detection

Visual object detection utilizing deep learning plays a vital role in computer vision and has extensive applications in transportation engineering. This paper focuses on detecting pavement marking quality during daytime using the You Only Look Once (YOLO) model, leveraging its advanced architectural features to enhance road safety through precise and real-time assessments. Utilizing image data from New Jersey, this study employed three YOLOv8 variants: YOLOv8m, YOLOv8n, and YOLOv8x. The models were evaluated based on their prediction accuracy for classifying pavement markings into good, moderate, and poor visibility categories. The results demonstrated that YOLOv8n provides the best balance between accuracy and computational efficiency, achieving the highest mean Average Precision (mAP) for objects with good visibility and demonstrating robust performance across various Intersections over Union (IoU) thresholds. This research enhances transportation safety by offering an automated and accurate method for evaluating the quality of pavement markings.
EmoDiffusion: Enhancing Emotional 3D Facial Animation with Latent Diffusion Models
Authors: Yixuan Zhang, Qing Chang, Yuxi Wang, Guang Chen, Zhaoxiang Zhang, Junran Peng
Date: 2025-03-14 00:00:00

Speech-driven 3D facial animation seeks to produce lifelike facial expressions that are synchronized with the speech content and its emotional nuances, finding applications in various multimedia fields. However, previous methods often overlook emotional facial expressions or fail to disentangle them effectively from the speech content. To address these challenges, we present EmoDiffusion, a novel approach that disentangles different emotions in speech to generate rich 3D emotional facial expressions. Specifically, our method employs two Variational Autoencoders (VAEs) to separately generate the upper face region and mouth region, thereby learning a more refined representation of the facial sequence. Unlike traditional methods that use diffusion models to connect facial expression sequences with audio inputs, we perform the diffusion process in the latent space. Furthermore, we introduce an Emotion Adapter to evaluate upper face movements accurately. Given the paucity of 3D emotional talking face data in the animation industry, we capture facial expressions under the guidance of animation experts using LiveLinkFace on an iPhone. This effort results in the creation of an innovative 3D blendshape emotional talking face dataset (3D-BEF) used to train our network. Extensive experiments and perceptual evaluations validate the effectiveness of our approach, confirming its superiority in generating realistic and emotionally rich facial animations.
FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection
Authors: Ming Deng, Sijin Sun, Zihao Li, Xiaochuan Hu, Xing Wu
Date: 2025-03-14 00:00:00
Tasks: Decoder, Mamba, object-detection, Object Detection

Camouflaged Object Detection (COD) is challenging due to the strong similarity between camouflaged objects and their surroundings, which complicates identification. Existing methods mainly rely on spatial local features, failing to capture global information, while Transformers increase computational costs.To address this, the Frequency-Assisted Mamba-Like Linear Attention Network (FMNet) is proposed, which leverages frequency-domain learning to efficiently capture global features and mitigate ambiguity between objects and the background. FMNet introduces the Multi-Scale Frequency-Assisted Mamba-Like Linear Attention (MFM) module, integrating frequency and spatial features through a multi-scale structure to handle scale variations while reducing computational complexity. Additionally, the Pyramidal Frequency Attention Extraction (PFAE) module and the Frequency Reverse Decoder (FRD) enhance semantics and reconstruct features. Experimental results demonstrate that FMNet outperforms existing methods on multiple COD datasets, showcasing its advantages in both performance and efficiency. Code available at https://anonymous.4open.science/r/FMNet-3CE5.
Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data
Authors: Lilin Zhang, Chengpei Wu, Ning Yang
Date: 2025-03-14 00:00:00
Proceeding: CVPR 2025 1

Existing adversarial training (AT) methods often suffer from incomplete perturbation, meaning that not all non-robust features are perturbed when generating adversarial examples (AEs). This results in residual correlations between non-robust features and labels, leading to suboptimal learning of robust features. However, achieving complete perturbation, i.e., perturbing as many non-robust features as possible, is challenging due to the difficulty in distinguishing robust and non-robust features and the sparsity of labeled data. To address these challenges, we propose a novel approach called Weakly Supervised Contrastive Adversarial Training (WSCAT). WSCAT ensures complete perturbation for improved learning of robust features by disrupting correlations between non-robust features and labels through complete AE generation over partially labeled data, grounded in information theory. Extensive theoretical analysis and comprehensive experiments on widely adopted benchmarks validate the superiority of WSCAT.
ACMo: Attribute Controllable Motion Generation
Authors: Mingjie Wei, Xuemei Xie, Guangming Shi
Date: 2025-03-14 00:00:00
Tasks: Attribute, Motion Generation

Attributes such as style, fine-grained text, and trajectory are specific conditions for describing motion. However, existing methods often lack precise user control over motion attributes and suffer from limited generalizability to unseen motions. This work introduces an Attribute Controllable Motion generation architecture, to address these challenges via decouple any conditions and control them separately. Firstly, we explored the Attribute Diffusion Model to imporve text-to-motion performance via decouple text and motion learning, as the controllable model relies heavily on the pre-trained model. Then, we introduce Motion Adpater to quickly finetune previously unseen motion patterns. Its motion prompts inputs achieve multimodal text-to-motion generation that captures user-specified styles. Finally, we propose a LLM Planner to bridge the gap between unseen attributes and dataset-specific texts via local knowledage for user-friendly interaction. Our approach introduces the capability for motion prompts for stylize generation, enabling fine-grained and user-friendly attribute control while providing performance comparable to state-of-the-art methods. Project page: https://mjwei3d.github.io/ACMo/