pwc-archive/papers-with-abstracts huggingface dataset:
PANet: A Physics-guided Parametric Augmentation Net for Image Dehazing by Hazing
Authors: Chih-Ling Chang, Fu-Jen Tsai, Zi-Ling Huang, Lin Gu, Chia-Wen Lin
Date: 2024-04-14 00:00:00
Tasks: Image Dehazing

Image dehazing faces challenges when dealing with hazy images in real-world scenarios. A huge domain gap between synthetic and real-world haze images degrades dehazing performance in practical settings. However, collecting real-world image datasets for training dehazing models is challenging since both hazy and clean pairs must be captured under the same conditions. In this paper, we propose a Physics-guided Parametric Augmentation Network (PANet) that generates photo-realistic hazy and clean training pairs to effectively enhance real-world dehazing performance. PANet comprises a Haze-to-Parameter Mapper (HPM) to project hazy images into a parameter space and a Parameter-to-Haze Mapper (PHM) to map the resampled haze parameters back to hazy images. In the parameter space, we can pixel-wisely resample individual haze parameter maps to generate diverse hazy images with physically-explainable haze conditions unseen in the training set. Our experimental results demonstrate that PANet can augment diverse realistic hazy images to enrich existing hazy image benchmarks so as to effectively boost the performances of state-of-the-art image dehazing models.
TEXT2TASTE: A Versatile Egocentric Vision System for Intelligent Reading Assistance Using Large Language Model
Authors: Wiktor Mucha, Florin Cuconasu, Naome A. Etori, Valia Kalokyri, Giovanni Trappolini
Date: 2024-04-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, object-detection, Object Detection, Optical Character Recognition, Text Retrieval

The ability to read, understand and find important information from written text is a critical skill in our daily lives for our independence, comfort and safety. However, a significant part of our society is affected by partial vision impairment, which leads to discomfort and dependency in daily activities. To address the limitations of this part of society, we propose an intelligent reading assistant based on smart glasses with embedded RGB cameras and a Large Language Model (LLM), whose functionality goes beyond corrective lenses. The video recorded from the egocentric perspective of a person wearing the glasses is processed to localise text information using object detection and optical character recognition methods. The LLM processes the data and allows the user to interact with the text and responds to a given query, thus extending the functionality of corrective lenses with the ability to find and summarize knowledge from the text. To evaluate our method, we create a chat-based application that allows the user to interact with the system. The evaluation is conducted in a real-world setting, such as reading menus in a restaurant, and involves four participants. The results show robust accuracy in text retrieval. The system not only provides accurate meal suggestions but also achieves high user satisfaction, highlighting the potential of smart glasses and LLMs in assisting people with special needs.
Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics
Authors: Haosong Peng, Wei Feng, Hao Li, Yufeng Zhan, Ren Jin, Yuanqing Xia
Date: 2024-04-14 00:00:00
Tasks: Edge-computing

The advent of edge computing has made real-time intelligent video analytics feasible. Previous works, based on traditional model architecture (e.g., CNN, RNN, etc.), employ various strategies to filter out non-region-of-interest content to minimize bandwidth and computation consumption but show inferior performance in adverse environments. Recently, visual foundation models based on transformers have shown great performance in adverse environments due to their amazing generalization capability. However, they require a large amount of computation power, which limits their applications in real-time intelligent video analytics. In this paper, we find visual foundation models like Vision Transformer (ViT) also have a dedicated acceleration mechanism for video analytics. To this end, we introduce Arena, an end-to-end edge-assisted video inference acceleration system based on ViT. We leverage the capability of ViT that can be accelerated through token pruning by only offloading and feeding Patches-of-Interest to the downstream models. Additionally, we design an adaptive keyframe inference switching algorithm tailored to different videos, capable of adapting to the current video content to jointly optimize accuracy and bandwidth. Through extensive experiments, our findings reveal that Arena can boost inference speeds by up to 1.58\(\times\) and 1.82\(\times\) on average while consuming only 47\% and 31\% of the bandwidth, respectively, all with high inference accuracy.
Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms
Authors: Diandian Guo, Manxi Lin, Jialun Pei, He Tang, Yueming Jin, Pheng-Ann Heng
Date: 2024-04-14 00:00:00
Tasks: Graph Generation, Scene Graph Generation

A comprehensive understanding of surgical scenes allows for monitoring of the surgical process, reducing the occurrence of accidents and enhancing efficiency for medical professionals. Semantic modeling within operating rooms, as a scene graph generation (SGG) task, is challenging since it involves consecutive recognition of subtle surgical actions over prolonged periods. To address this challenge, we propose a Tri-modal (i.e., images, point clouds, and language) confluence with Temporal dynamics framework, termed TriTemp-OR. Diverging from previous approaches that integrated temporal information via memory graphs, our method embraces two advantages: 1) we directly exploit bi-modal temporal information from the video streaming for hierarchical feature interaction, and 2) the prior knowledge from Large Language Models (LLMs) is embedded to alleviate the class-imbalance problem in the operating theatre. Specifically, our model performs temporal interactions across 2D frames and 3D point clouds, including a scale-adaptive multi-view temporal interaction (ViewTemp) and a geometric-temporal point aggregation (PointTemp). Furthermore, we transfer knowledge from the biomedical LLM, LLaVA-Med, to deepen the comprehension of intraoperative relations. The proposed TriTemp-OR enables the aggregation of tri-modal features through relation-aware unification to predict relations so as to generate scene graphs. Experimental results on the 4D-OR benchmark demonstrate the superior performance of our model for long-term OR streaming.
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling
Authors: Xuening Yuan, Hongyu Yang, Yueming Zhao, Di Huang
Date: 2024-04-14 00:00:00
Tasks: global-optimization, Image Generation, Text to 3D, Text to Image Generation, Text-to-Image Generation

Recent progress in text-to-3D creation has been propelled by integrating the potent prior of Diffusion Models from text-to-image generation into the 3D domain. Nevertheless, generating 3D scenes characterized by multiple instances and intricate arrangements remains challenging. In this study, we present DreamScape, a method for creating highly consistent 3D scenes solely from textual descriptions, leveraging the strong 3D representation capabilities of Gaussian Splatting and the complex arrangement abilities of large language models (LLMs). Our approach involves a 3D Gaussian Guide ($3{DG^2}$) for scene representation, consisting of semantic primitives (objects) and their spatial transformations and relationships derived directly from text prompts using LLMs. This compositional representation allows for local-to-global optimization of the entire scene. A progressive scale control is tailored during local object generation, ensuring that objects of different sizes and densities adapt to the scene, which addresses training instability issue arising from simple blending in the subsequent global optimization stage. To mitigate potential biases of LLM priors, we model collision relationships between objects at the global level, enhancing physical correctness and overall realism. Additionally, to generate pervasive objects like rain and snow distributed extensively across the scene, we introduce a sparse initialization and densification strategy. Experiments demonstrate that DreamScape offers high usability and controllability, enabling the generation of high-fidelity 3D scenes from only text prompts and achieving state-of-the-art performance compared to other methods.
Breast Cancer Image Classification Method Based on Deep Transfer Learning
Authors: Weimin WANG, Yufeng Li, Xu Yan, Mingxuan Xiao, Min Gao
Date: 2024-04-14 00:00:00
Tasks: Breast Cancer Detection, Classification, image-classification, Image Classification, Transfer Learning

To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. This algorithm is based on the DenseNet structure of deep neural networks, and constructs a network model by introducing attention mechanisms, and trains the enhanced dataset using multi-level transfer learning. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0\% in the test set, with a significantly improved classification accuracy compared to previous models, making it applicable to medical breast cancer detection tasks.
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection
Authors: Lewei Yao, Renjie Pi, Jianhua Han, Xiaodan Liang, Hang Xu, Wei zhang, Zhenguo Li, Dan Xu
Date: 2024-04-14 00:00:00
Proceeding: CVPR 2024 1
Tasks: Dense Captioning, Language Modelling, Large Language Model, Object, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection

Existing open-vocabulary object detectors typically require a predefined set of categories from users, significantly confining their application scenarios. In this paper, we introduce DetCLIPv3, a high-performing detector that excels not only at both open-vocabulary object detection, but also generating hierarchical labels for detected objects. DetCLIPv3 is characterized by three core designs: 1. Versatile model architecture: we derive a robust open-set detection framework which is further empowered with generation ability via the integration of a caption head. 2. High information density data: we develop an auto-annotation pipeline leveraging visual large language model to refine captions for large-scale image-text pairs, providing rich, multi-granular object labels to enhance the training. 3. Efficient training strategy: we employ a pre-training stage with low-resolution inputs that enables the object captioner to efficiently learn a broad spectrum of visual concepts from extensive image-text paired data. This is followed by a fine-tuning stage that leverages a small number of high-resolution samples to further enhance detection performance. With these effective designs, DetCLIPv3 demonstrates superior open-vocabulary detection performance, \eg, our Swin-T backbone model achieves a notable 47.0 zero-shot fixed AP on the LVIS minival benchmark, outperforming GLIPv2, GroundingDINO, and DetCLIPv2 by 18.0/19.6/6.6 AP, respectively. DetCLIPv3 also achieves a state-of-the-art 19.7 AP in dense captioning task on VG dataset, showcasing its strong generative capability.
FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model
Authors: Jiawei Chen, Xiao Yang, Yinpeng Dong, Hang Su, Zhaoxia Yin
Date: 2024-04-14 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition, FAD

Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems. However, due to limited practicality, complex deployment, and the additional computational overhead, it is necessary to implement both detection techniques within a unified framework. This paper aims to achieve this goal by breaking through two primary obstacles: 1) the suboptimal face feature representation and 2) the scarcity of training data. To address the limited performance caused by existing feature representations, motivated by the rich structural and detailed features of face diffusion models, we propose FaceCat, the first approach leveraging the diffusion model to simultaneously enhance the performance of FAS and FAD. Specifically, FaceCat elaborately designs a hierarchical fusion mechanism to capture rich face semantic features of the diffusion model. These features then serve as a robust foundation for a lightweight head, designed to execute FAS and FAD simultaneously. Due to the limitations in feature representation that arise from relying solely on single-modality image data, we further propose a novel text-guided multi-modal alignment strategy that utilizes text prompts to enrich feature representation, thereby enhancing performance. To combat data scarcity, we build a comprehensive dataset with a wide range of 28 attack types, offering greater potential for a unified framework in facial security. Extensive experiments validate the effectiveness of FaceCat generalizes significantly better and obtains excellent robustness against common input transformations.
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
Authors: Chengxi Han, Chen Wu, HaoNan Guo, Meiqi Hu, Jiepan Li, Hongruixuan Chen
Date: 2024-04-14 00:00:00
Tasks: Change Detection, Edge Detection

The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks. However, there is still a lot of space to study for precise detection, especially the edge integrity and internal holes phenomenon of change features. In order to solve these problems, we design the Change Guiding Network (CGNet), to tackle the insufficient expression problem of change features in the conventional U-Net structure adopted in previous methods, which causes inaccurate edge detection and internal holes. Change maps from deep features with rich semantic information are generated and used as prior information to guide multi-scale feature fusion, which can improve the expression ability of change features. Meanwhile, we propose a self-attention module named Change Guide Module (CGM), which can effectively capture the long-distance dependency among pixels and effectively overcome the problem of the insufficient receptive field of traditional convolutional neural networks. On four major CD datasets, we verify the usefulness and efficiency of the CGNet, and a large number of experiments and ablation studies demonstrate the effectiveness of CGNet. We're going to open-source our code at https://github.com/ChengxiHAN/CGNet-CD.
HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images
Authors: Chengxi Han, Chen Wu, HaoNan Guo, Meiqi Hu, Hongruixuan Chen
Date: 2024-04-14 00:00:00
Tasks: Change Detection, Deep Learning

Benefiting from the developments in deep learning technology, deep-learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the change detection (CD) task. However, the performance of existing deep-learning-based CD methods is hindered by the imbalance between changed and unchanged pixels. To tackle this problem, a progressive foreground-balanced sampling strategy on the basis of not adding change information is proposed in this article to help the model accurately learn the features of the changed pixels during the early training process and thereby improve detection performance.Furthermore, we design a discriminative Siamese network, hierarchical attention network (HANet), which can integrate multiscale features and refine detailed features. The main part of HANet is the HAN module, which is a lightweight and effective self-attention mechanism. Extensive experiments and ablation studies on two CDdatasets with extremely unbalanced labels validate the effectiveness and efficiency of the proposed method.
Coreset Selection for Object Detection
Authors: Hojun Lee, Suyoung Kim, JunHoo Lee, Jaeyoung Yoo, Nojun Kwak
Date: 2024-04-14 00:00:00
Tasks: Diversity, image-classification, Image Classification, Object, object-detection, Object Detection

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on this topic. Therefore, we introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently, we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP$_{50}$ when selecting 200 images.
GCC: Generative Calibration Clustering
Authors: Haifeng Xia, Hai Huang, Zhengming Ding
Date: 2024-04-14 00:00:00
Tasks: Clustering, Contrastive Learning, Deep Clustering, Metric Learning, Representation Learning, valid

Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space. This core demand inspires the exploration of contrastive learning and subspace clustering. However, these solutions always rely on the basic assumption that there are sufficient and category-balanced samples for generating valid high-level representation. This hypothesis actually is too strict to be satisfied for real-world applications. To overcome such a challenge, the natural strategy is utilizing generative models to augment considerable instances. How to use these novel samples to effectively fulfill clustering performance improvement is still difficult and under-explored. In this paper, we propose a novel Generative Calibration Clustering (GCC) method to delicately incorporate feature learning and augmentation into clustering procedure. First, we develop a discriminative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment to boost the conditional diffusion generation. Extensive experimental results on three benchmarks validate the effectiveness and advantage of our proposed method over the state-of-the-art methods.
Exploring Generative AI for Sim2Real in Driving Data Synthesis
Authors: Haonan Zhao, Yiting Wang, Thomas Bashford-Rogers, Valentina Donzella, Kurt Debattista
Date: 2024-04-14 00:00:00

Datasets are essential for training and testing vehicle perception algorithms. However, the collection and annotation of real-world images is time-consuming and expensive. Driving simulators offer a solution by automatically generating various driving scenarios with corresponding annotations, but the simulation-to-reality (Sim2Real) domain gap remains a challenge. While most of the Generative Artificial Intelligence (AI) follows the de facto Generative Adversarial Nets (GANs)-based methods, the recent emerging diffusion probabilistic models have not been fully explored in mitigating Sim2Real challenges for driving data synthesis. To explore the performance, this paper applied three different generative AI methods to leverage semantic label maps from a driving simulator as a bridge for the creation of realistic datasets. A comparative analysis of these methods is presented from the perspective of image quality and perception. New synthetic datasets, which include driving images and auto-generated high-quality annotations, are produced with low costs and high scene variability. The experimental results show that although GAN-based methods are adept at generating high-quality images when provided with manually annotated labels, ControlNet produces synthetic datasets with fewer artefacts and more structural fidelity when using simulator-generated labels. This suggests that the diffusion-based approach may provide improved stability and an alternative method for addressing Sim2Real challenges.
Probabilistic Directed Distance Fields for Ray-Based Shape Representations
Authors: Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson
Date: 2024-04-13 00:00:00
Tasks: 3D Reconstruction

In modern computer vision, the optimal representation of 3D shape continues to be task-dependent. One fundamental operation applied to such representations is differentiable rendering, as it enables inverse graphics approaches in learning frameworks. Standard explicit shape representations (voxels, point clouds, or meshes) are often easily rendered, but can suffer from limited geometric fidelity, among other issues. On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability. In this work, we devise Directed Distance Fields (DDFs), a novel neural shape representation that builds upon classical distance fields. The fundamental operation in a DDF maps an oriented point (position and direction) to surface visibility and depth. This enables efficient differentiable rendering, obtaining depth with a single forward pass per pixel, as well as differential geometric quantity extraction (e.g., surface normals), with only additional backward passes. Using probabilistic DDFs (PDDFs), we show how to model inherent discontinuities in the underlying field. We then apply DDFs to several applications, including single-shape fitting, generative modelling, and single-image 3D reconstruction, showcasing strong performance with simple architectural components via the versatility of our representation. Finally, since the dimensionality of DDFs permits view-dependent geometric artifacts, we conduct a theoretical investigation of the constraints necessary for view consistency. We find a small set of field properties that are sufficient to guarantee a DDF is consistent, without knowing, for instance, which shape the field is expressing.