pwc-archive/papers-with-abstracts huggingface dataset:
DisControlFace: Adding Disentangled Control to Diffusion Autoencoder for One-shot Explicit Facial Image Editing
Authors: Haozhe Jia, Yan Li, Hengfei Cui, Di Xu, Yuwang Wang, Tao Yu
Date: 2023-12-11 00:00:00

In this work, we focus on exploring explicit fine-grained control of generative facial image editing, all while generating faithful facial appearances and consistent semantic details, which however, is quite challenging and has not been extensively explored, especially under an one-shot scenario. We identify the key challenge as the exploration of disentangled conditional control between high-level semantics and explicit parameters (e.g., 3DMM) in the generation process, and accordingly propose a novel diffusion-based editing framework, named DisControlFace. Specifically, we leverage a Diffusion Autoencoder (Diff-AE) as the semantic reconstruction backbone. To enable explicit face editing, we construct an Exp-FaceNet that is compatible with Diff-AE to generate spatial-wise explicit control conditions based on estimated 3DMM parameters. Different from current diffusion-based editing methods that train the whole conditional generative model from scratch, we freeze the pre-trained weights of the Diff-AE to maintain its semantically deterministic conditioning capability and accordingly propose a random semantic masking (RSM) strategy to effectively achieve an independent training of Exp-FaceNet. This setting endows the model with disentangled face control meanwhile reducing semantic information shift in editing. Our model can be trained using 2D in-the-wild portrait images without requiring 3D or video data and perform robust editing on any new facial image through a simple one-shot fine-tuning. Comprehensive experiments demonstrate that DisControlFace can generate realistic facial images with better editing accuracy and identity preservation over state-of-the-art methods. Project page: https://discontrolface.github.io/
NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation
Authors: Saeejith Nair, Chi-en Amy Tai, Yuhao Chen, Alexander Wong
Date: 2023-12-11 00:00:00
Tasks: Diversity

Manually tracking nutritional intake via food diaries is error-prone and burdensome. Automated computer vision techniques show promise for dietary monitoring but require large and diverse food image datasets. To address this need, we introduce NutritionVerse-Synth (NV-Synth), a large-scale synthetic food image dataset. NV-Synth contains 84,984 photorealistic meal images rendered from 7,082 dynamically plated 3D scenes. Each scene is captured from 12 viewpoints and includes perfect ground truth annotations such as RGB, depth, semantic, instance, and amodal segmentation masks, bounding boxes, and detailed nutritional information per food item. We demonstrate the diversity of NV-Synth across foods, compositions, viewpoints, and lighting. As the largest open-source synthetic food dataset, NV-Synth highlights the value of physics-based simulations for enabling scalable and controllable generation of diverse photorealistic meal images to overcome data limitations and drive advancements in automated dietary assessment using computer vision. In addition to the dataset, the source code for our data generation framework is also made publicly available at https://saeejithnair.github.io/nvsynth.
SP-DiffDose: A Conditional Diffusion Model for Radiation Dose Prediction Based on Multi-Scale Fusion of Anatomical Structures, Guided by SwinTransformer and Projector
Authors: Linjie Fu, Xia Li, Xiuding Cai, Yingkai Wang, Xueyao Wang, Yu Yao, Yali Shen
Date: 2023-12-11 00:00:00
Tasks: Prediction

Radiation therapy serves as an effective and standard method for cancer treatment. Excellent radiation therapy plans always rely on high-quality dose distribution maps obtained through repeated trial and error by experienced experts. However, due to individual differences and complex clinical situations, even seasoned expert teams may need help to achieve the best treatment plan every time quickly. Many automatic dose distribution prediction methods have been proposed recently to accelerate the radiation therapy planning process and have achieved good results. However, these results suffer from over-smoothing issues, with the obtained dose distribution maps needing more high-frequency details, limiting their clinical application. To address these limitations, we propose a dose prediction diffusion model based on SwinTransformer and a projector, SP-DiffDose. To capture the direct correlation between anatomical structure and dose distribution maps, SP-DiffDose uses a structural encoder to extract features from anatomical images, then employs a conditional diffusion process to blend noise and anatomical images at multiple scales and gradually map them to dose distribution maps. To enhance the dose prediction distribution for organs at risk, SP-DiffDose utilizes SwinTransformer in the deeper layers of the network to capture features at different scales in the image. To learn good representations from the fused features, SP-DiffDose passes the fused features through a designed projector, improving dose prediction accuracy. Finally, we evaluate SP-DiffDose on an internal dataset. The results show that SP-DiffDose outperforms existing methods on multiple evaluation metrics, demonstrating the superiority and generalizability of our method.
Recent Advances in Deterministic Human Motion Prediction: A Review
Authors: Tenghao Deng, Yan Sun
Date: 2023-12-11 00:00:00
Tasks: Autonomous Driving, Human motion prediction, motion prediction

In recent years, with the continuous advancement of deep learning and the emergence of large-scale human motion datasets, human motion prediction technology has gradually gained prominence in various fields such as human-computer interaction, autonomous driving, sports analysis, and personnel tracking. This article introduces common model architectures in this domain along with their respective advantages and disadvantages. It also systematically summarizes recent research innovations, focusing on in-depth discussions of relevant papers in these areas, thereby highlighting forward-looking insights into the field's development. Furthermore, this paper provides a comprehensive overview of existing methods, commonly used datasets, and evaluation metrics in this field. Finally, it discusses some of the current limitations in the field and proposes potential future research directions to address these challenges and promote further advancements in human motion prediction.
Dynamic Weighted Combiner for Mixed-Modal Image Retrieval
Authors: Fuxiang Huang, Lei Zhang, Xiaowei Fu, Suqi Song
Date: 2023-12-11 00:00:00
Tasks: Image Retrieval, Retrieval

Mixed-Modal Image Retrieval (MMIR) as a flexible search paradigm has attracted wide attention. However, previous approaches always achieve limited performance, due to two critical factors are seriously overlooked. 1) The contribution of image and text modalities is different, but incorrectly treated equally. 2) There exist inherent labeling noises in describing users' intentions with text in web datasets from diverse real-world scenarios, giving rise to overfitting. We propose a Dynamic Weighted Combiner (DWC) to tackle the above challenges, which includes three merits. First, we propose an Editable Modality De-equalizer (EMD) by taking into account the contribution disparity between modalities, containing two modality feature editors and an adaptive weighted combiner. Second, to alleviate labeling noises and data bias, we propose a dynamic soft-similarity label generator (SSG) to implicitly improve noisy supervision. Finally, to bridge modality gaps and facilitate similarity learning, we propose a CLIP-based mutual enhancement module alternately trained by a mixed-modality contrastive loss. Extensive experiments verify that our proposed model significantly outperforms state-of-the-art methods on real-world datasets. The source code is available at \url{https://github.com/fuxianghuang1/DWC}.
Joint Explicit and Implicit Cross-Modal Interaction Network for Anterior Chamber Inflammation Diagnosis
Authors: Qian Shao, Ye Dai, Haochao Ying, Kan Xu, Jinhong Wang, Wei Chi, Jian Wu
Date: 2023-12-11 00:00:00
Tasks: Clinical Knowledge, Diagnostic, Informativeness

Uveitis demands the precise diagnosis of anterior chamber inflammation (ACI) for optimal treatment. However, current diagnostic methods only rely on a limited single-modal disease perspective, which leads to poor performance. In this paper, we investigate a promising yet challenging way to fuse multimodal data for ACI diagnosis. Notably, existing fusion paradigms focus on empowering implicit modality interactions (i.e., self-attention and its variants), but neglect to inject explicit modality interactions, especially from clinical knowledge and imaging property. To this end, we propose a jointly Explicit and implicit Cross-Modal Interaction Network (EiCI-Net) for Anterior Chamber Inflammation Diagnosis that uses anterior segment optical coherence tomography (AS-OCT) images, slit-lamp images, and clinical data jointly. Specifically, we first develop CNN-Based Encoders and Tabular Processing Module (TPM) to extract efficient feature representations in different modalities. Then, we devise an Explicit Cross-Modal Interaction Module (ECIM) to generate attention maps as a kind of explicit clinical knowledge based on the tabular feature maps, then integrated them into the slit-lamp feature maps, allowing the CNN-Based Encoder to focus on more effective informativeness of the slit-lamp images. After that, the Implicit Cross-Modal Interaction Module (ICIM), a transformer-based network, further implicitly enhances modality interactions. Finally, we construct a considerable real-world dataset from our collaborative hospital and conduct sufficient experiments to demonstrate the superior performance of our proposed EiCI-Net compared with the state-of-the-art classification methods in various metrics.
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions
Authors: Yifan Liu, Tiecheng Song, Chengye Xian, Ruiyuan Chen, Yi Zhao, Rui Li, Tan Guo
Date: 2023-12-11 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Image Augmentation, Pseudo Label

Crater detection can provide valuable information for humans to explore the topography and understand the history of extraterrestrial planets. Due to the significantly varying scenario distributions, existing detection models trained on known labelled crater datasets are hardly effective when applied to new unlabelled planets. To address this issue, we propose a two-stage adaptive network (TAN) for semi-supervised cross-domain crater detection. Our network is built on the YOLOv5 detector, where a series of strategies are employed to enhance its cross-domain generalisation ability. In the first stage, we propose an attention-based scale-adaptive fusion (ASAF) strategy to handle objects with significant scale variances. Furthermore, we propose a smoothing hard example mining (SHEM) loss function to address the issue of overfitting on hard examples. In the second stage, we propose a sort-based pseudo-labelling fine-tuning (SPF) strategy for semi-supervised learning to mitigate the distributional differences between source and target domains. For both stages, we employ weak or strong image augmentation to suit different cross-domain tasks. Experimental results on benchmark datasets demonstrate that the proposed network can enhance domain adaptation ability for crater detection under varying scenario distributions.
ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction
Authors: Minghui Zhang, Hao Zheng, Yawen Huang, Ling Shao, Yun Gu
Date: 2023-12-11 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation, valid

Shape modeling of volumetric medical images is crucial for quantitative analysis and surgical planning in computer-aided diagnosis. To alleviate the burden of expert clinicians, reconstructed shapes are typically obtained from deep learning models, such as Convolutional Neural Networks (CNNs) or transformer-based architectures, followed by the marching cube algorithm. However, automatic shape reconstruction often falls short of perfection due to the limited resolution of images and the absence of shape prior constraints. To overcome these limitations, we propose the Reliable Shape Interaction with Implicit Template (ReShapeIT) network, which models anatomical structures in continuous space rather than discrete voxel grids. ReShapeIT represents an anatomical structure with an implicit template field shared within the same category, complemented by a deformation field. It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape. The valid template shape can then be utilized for implicit generalization. A Template Interaction Module (TIM) is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes. Experimental results on three datasets demonstrate the superiority of our approach in anatomical structure reconstruction. The Chamfer Distance/Earth Mover's Distance achieved by ReShapeIT are 0.225/0.318 on Liver, 0.125/0.067 on Pancreas, and 0.414/0.098 on Lung Lobe.
Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors
Authors: Kalibinuer Tiliwalidi
Date: 2023-12-11 00:00:00

Nowadays, the susceptibility of deep neural networks (DNNs) has garnered significant attention. Researchers are exploring patch-based physical attacks, yet traditional approaches, while effective, often result in conspicuous patches covering target objects. This leads to easy detection by human observers. Recently, novel camera-based physical attacks have emerged, leveraging camera patches to execute stealthy attacks. These methods circumvent target object modifications by introducing perturbations directly to the camera lens, achieving a notable breakthrough in stealthiness. However, prevailing camera-based strategies necessitate the deployment of multiple patches on the camera lens, which introduces complexity. To address this issue, we propose an Adversarial Camera Patch (ADCP).
Textual Prompt Guided Image Restoration
Authors: Qiuhai Yan, Aiwen Jiang, Kang Chen, Long Peng, Qiaosi Yi, Chunjie Zhang
Date: 2023-12-11 00:00:00
Tasks: Denoising, Image Restoration, Rain Removal

Image restoration has always been a cutting-edge topic in the academic and industrial fields of computer vision. Since degradation signals are often random and diverse, "all-in-one" models that can do blind image restoration have been concerned in recent years. Early works require training specialized headers and tails to handle each degradation of concern, which are manually cumbersome. Recent works focus on learning visual prompts from data distribution to identify degradation type. However, the prompts employed in most of models are non-text, lacking sufficient emphasis on the importance of human-in-the-loop. In this paper, an effective textual prompt guided image restoration model has been proposed. In this model, task-specific BERT is fine-tuned to accurately understand user's instructions and generating textual prompt guidance. Depth-wise multi-head transposed attentions and gated convolution modules are designed to bridge the gap between textual prompts and visual features. The proposed model has innovatively introduced semantic prompts into low-level visual domain. It highlights the potential to provide a natural, precise, and controllable way to perform image restoration tasks. Extensive experiments have been done on public denoising, dehazing and deraining datasets. The experiment results demonstrate that, compared with popular state-of-the-art methods, the proposed model can obtain much more superior performance, achieving accurate recognition and removal of degradation without increasing model's complexity. Related source codes and data will be publicly available on github site https://github.com/MoTong-AI-studio/TextPromptIR.
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity
Authors: Xudong Li, Timin Gao, Runze Hu, Yan Zhang, Shengchuan Zhang, Xiawu Zheng, Jingyuan Zheng, Yunhang Shen, Ke Li, Yutao Liu, Pingyang Dai, Rongrong Ji
Date: 2023-12-11 00:00:00
Tasks: Contrastive Learning, feature selection, Image Quality Assessment, No-Reference Image Quality Assessment, NR-IQA, Sensitivity

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empirically, we find that many image pairs with small feature spatial distances can have vastly different quality scores, indicating that the extracted features may contain a significant amount of quality-irrelevant noise. To address this issue, we propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective to remove harmful semantic noise features from the upstream task. Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize a distillation framework to expand the dataset and improve the model's generalization ability. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
BACTrack: Building Appearance Collection for Aerial Tracking
Authors: Xincong Liu, Tingfa Xu, Ying Wang, Zhinong Yu, Xiaoying Yuan, Haolin Qin, Jianan Li
Date: 2023-12-11 00:00:00
Tasks: GPU, Template Matching

Siamese network-based trackers have shown remarkable success in aerial tracking. Most previous works, however, usually perform template matching only between the initial template and the search region and thus fail to deal with rapidly changing targets that often appear in aerial tracking. As a remedy, this work presents Building Appearance Collection Tracking (BACTrack). This simple yet effective tracking framework builds a dynamic collection of target templates online and performs efficient multi-template matching to achieve robust tracking. Specifically, BACTrack mainly comprises a Mixed-Temporal Transformer (MTT) and an appearance discriminator. The former is responsible for efficiently building relationships between the search region and multiple target templates in parallel through a mixed-temporal attention mechanism. At the same time, the appearance discriminator employs an online adaptive template-update strategy to ensure that the collected multiple templates remain reliable and diverse, allowing them to closely follow rapid changes in the target's appearance and suppress background interference during tracking. Extensive experiments show that our BACTrack achieves top performance on four challenging aerial tracking benchmarks while maintaining an impressive speed of over 87 FPS on a single GPU. Speed tests on embedded platforms also validate our potential suitability for deployment on UAV platforms.
ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank
Authors: Zhanjie Zhang, Quanwei Zhang, Guangyuan Li, Wei Xing, Lei Zhao, Jiakai Sun, Zehua Lan, Junsheng Luan, Yiling Huang, Huaizhong Lin
Date: 2023-12-11 00:00:00
Tasks: Style Transfer

Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. Small model-based approaches can preserve the content strucuture, but fail to produce highly realistic stylized images and introduce artifacts and disharmonious patterns; Pre-trained large-scale model-based approaches can generate highly realistic stylized images but struggle with preserving the content structure. To address the above issues, we propose ArtBank, a novel artistic style transfer framework, to generate highly realistic stylized images while preserving the content structure of the content images. Specifically, to sufficiently dig out the knowledge embedded in pre-trained large-scale models, an Implicit Style Prompt Bank (ISPB), a set of trainable parameter matrices, is designed to learn and store knowledge from the collection of artworks and behave as a visual prompt to guide pre-trained large-scale models to generate highly realistic stylized images while preserving content structure. Besides, to accelerate training the above ISPB, we propose a novel Spatial-Statistical-based self-Attention Module (SSAM). The qualitative and quantitative experiments demonstrate the superiority of our proposed method over state-of-the-art artistic style transfer methods.
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline
Authors: Mehala Balamurali, Ehsan Mihankhah
Date: 2023-12-11 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Robust Object Detection

Accurate and efficient object detection is crucial for safe and efficient operation of earth-moving equipment in mining. Traditional 2D image-based methods face limitations in dynamic and complex mine environments. To overcome these challenges, 3D object detection using point cloud data has emerged as a comprehensive approach. However, training models for mining scenarios is challenging due to sensor height variations, viewpoint changes, and the need for diverse annotated datasets. This paper presents novel contributions to address these challenges. We introduce a synthetic dataset SimMining 3D [1] specifically designed for 3D object detection in mining environments. The dataset captures objects and sensors positioned at various heights within mine benches, accurately reflecting authentic mining scenarios. An automatic annotation pipeline through ROS interface reduces manual labor and accelerates dataset creation. We propose evaluation metrics accounting for sensor-to-object height variations and point cloud density, enabling accurate model assessment in mining scenarios. Real data tests validate our models effectiveness in object prediction. Our ablation study emphasizes the importance of altitude and height variation augmentations in improving accuracy and reliability. The publicly accessible synthetic dataset [1] serves as a benchmark for supervised learning and advances object detection techniques in mining with complimentary pointwise annotations for each scene. In conclusion, our work bridges the gap between synthetic and real data, addressing the domain shift challenge in 3D object detection for mining. We envision robust object detection systems enhancing safety and efficiency in mining and related domains.