pwc-archive/papers-with-abstracts huggingface dataset:
Compositional Segmentation of Cardiac Images Leveraging Metadata
Authors: Abbas Khan, Muhammad Asad, Martin Benning, Caroline Roney, Gregory Slabaugh
Date: 2024-10-30 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Cardiac image segmentation is essential for automated cardiac function assessment and monitoring of changes in cardiac structures over time. Inspired by coarse-to-fine approaches in image analysis, we propose a novel multitask compositional segmentation approach that can simultaneously localize the heart in a cardiac image and perform part-based segmentation of different regions of interest. We demonstrate that this compositional approach achieves better results than direct segmentation of the anatomies. Further, we propose a novel Cross-Modal Feature Integration (CMFI) module to leverage the metadata related to cardiac imaging collected during image acquisition. We perform experiments on two different modalities, MRI and ultrasound, using public datasets, Multi-disease, Multi-View, and Multi-Centre (M&Ms-2) and Multi-structure Ultrasound Segmentation (CAMUS) data, to showcase the efficiency of the proposed compositional segmentation method and Cross-Modal Feature Integration module incorporating metadata within the proposed compositional segmentation network. The source code is available: https://github.com/kabbas570/CompSeg-MetaData.
Why Fine-grained Labels in Pretraining Benefit Generalization?
Authors: Guan Zhe Hong, Yin Cui, Ariel Fuxman, Stanley Chan, Enming Luo
Date: 2024-10-30 00:00:00

Recent studies show that pretraining a deep neural network with fine-grained labeled data, followed by fine-tuning on coarse-labeled data for downstream tasks, often yields better generalization than pretraining with coarse-labeled data. While there is ample empirical evidence supporting this, the theoretical justification remains an open problem. This paper addresses this gap by introducing a "hierarchical multi-view" structure to confine the input data distribution. Under this framework, we prove that: 1) coarse-grained pretraining only allows a neural network to learn the common features well, while 2) fine-grained pretraining helps the network learn the rare features in addition to the common ones, leading to improved accuracy on hard downstream test samples.
NASM: Neural Anisotropic Surface Meshing
Authors: Hongbo Li, Haikuan Zhu, Sikai Zhong, Ningna Wang, Cheng Lin, Xiaohu Guo, Shiqing Xin, Wenping Wang, Jing Hua, Zichun Zhong
Date: 2024-10-30 00:00:00
Tasks: Graph Neural Network

This paper introduces a new learning-based method, NASM, for anisotropic surface meshing. Our key idea is to propose a graph neural network to embed an input mesh into a high-dimensional (high-d) Euclidean embedding space to preserve curvature-based anisotropic metric by using a dot product loss between high-d edge vectors. This can dramatically reduce the computational time and increase the scalability. Then, we propose a novel feature-sensitive remeshing on the generated high-d embedding to automatically capture sharp geometric features. We define a high-d normal metric, and then derive an automatic differentiation on a high-d centroidal Voronoi tessellation (CVT) optimization with the normal metric to simultaneously preserve geometric features and curvature anisotropy that exhibit in the original 3D shapes. To our knowledge, this is the first time that a deep learning framework and a large dataset are proposed to construct a high-d Euclidean embedding space for 3D anisotropic surface meshing. Experimental results are evaluated and compared with the state-of-the-art in anisotropic surface meshing on a large number of surface models from Thingi10K dataset as well as tested on extensive unseen 3D shapes from Multi-Garment Network dataset and FAUST human dataset.
Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification
Authors: Pengkun Liu, Shuna Ni, Stanislav I. Stoliarov, Pingbo Tang
Date: 2024-10-30 00:00:00
Tasks: Point Cloud Segmentation

Fire patterns, consisting of fire effects that offer insights into fire behavior and origin, are traditionally classified based on investigators' visual observations, leading to subjective interpretations. This study proposes a framework for quantitative fire pattern classification to support fire investigators, aiming for consistency and accuracy. The framework integrates four components. First, it leverages human-computer interaction to extract fire patterns from surfaces, combining investigator expertise with computational analysis. Second, it employs an aspect ratio-based random forest model to classify fire pattern shapes. Third, fire scene point cloud segmentation enables precise identification of fire-affected areas and the mapping of 2D fire patterns to 3D scenes. Lastly, spatial relationships between fire patterns and indoor elements support an interpretation of the fire scene. These components provide a method for fire pattern analysis that synthesizes qualitative and quantitative data. The framework's classification results achieve 93% precision on synthetic data and 83% on real fire patterns.
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Atomic Activity Recognition 2024
Authors: Ruyang Li, Tengfei Zhang, Heng Zhang, Tiejun Liu, Yanwei Wang, Xuelei Li
Date: 2024-10-30 00:00:00
Tasks: Activity Recognition, Data Augmentation, Object

This report presents our team's technical solution for participating in Track 3 of the 2024 ECCV ROAD++ Challenge. The task of Track 3 is atomic activity recognition, which aims to identify 64 types of atomic activities in road scenes based on video content. Our approach primarily addresses the challenges of small objects, discriminating between single object and a group of objects, as well as model overfitting in this task. Firstly, we construct a multi-branch activity recognition framework that not only separates different object categories but also the tasks of single object and object group recognition, thereby enhancing recognition accuracy. Subsequently, we develop various model ensembling strategies, including integrations of multiple frame sampling sequences, different frame sampling sequence lengths, multiple training epochs, and different backbone networks. Furthermore, we propose an atomic activity recognition data augmentation method, which greatly expands the sample space by flipping video frames and road topology, effectively mitigating model overfitting. Our methods rank first in the test set of Track 3 for the ROAD++ Challenge 2024, and achieve 69% mAP.
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
Authors: Mingkun Zhang, Keping Bi, Wei Chen, Quanrun Chen, Jiafeng Guo, Xueqi Cheng
Date: 2024-10-30 00:00:00
Tasks: Adversarial Defense, Disentanglement, Traffic Sign Recognition

Despite ongoing efforts to defend neural classifiers from adversarial attacks, they remain vulnerable, especially to unseen attacks. In contrast, humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors. Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation. For an adversarial example, we aim to discriminate the perturbations as non-causative factors and make predictions only based on the label-causative factors. Concretely, we propose a casual diffusion model (CausalDiff) that adapts diffusion models for conditional data generation and disentangles the two types of casual factors by learning towards a novel casual information bottleneck objective. Empirically, CausalDiff has significantly outperformed state-of-the-art defense methods on various unseen attacks, achieving an average robustness of 86.39% (+4.01%) on CIFAR-10, 56.25% (+3.13%) on CIFAR-100, and 82.62% (+4.93%) on GTSRB (German Traffic Sign Recognition Benchmark). The code is available at https://github.com/CAS-AISafetyBasicResearchGroup/CausalDiff.
PIP-MM: Pre-Integrating Prompt Information into Visual Encoding via Existing MLLM Structures
Authors: Tianxiang Wu, Minxin Nie, Ziqiang Cao
Date: 2024-10-30 00:00:00

The Multimodal Large Language Models (MLLMs) have activated the capabilitiesof Large Language Models (LLMs) in solving visual-language tasks by integratingvisual information. The prevailing approach in existing MLLMs involvesemploying an image encoder to extract visual features, converting thesefeatures into visual tokens via an adapter, and then integrating them with theprompt into the LLM. However, because the process of image encoding isprompt-agnostic, the extracted visual features only provide a coarsedescription of the image, impossible to focus on the requirements of theprompt. On one hand, it is easy for image features to lack information aboutthe prompt-specified objects, resulting in unsatisfactory responses. On theother hand, the visual features contain a large amount of irrelevantinformation, which not only increases the burden on memory but also worsens thegeneration effectiveness. To address the aforementioned issues, we propose\textbf{PIP-MM}, a framework that \textbf{P}re-\textbf{I}ntegrates\textbf{P}rompt information into the visual encoding process using existingmodules of MLLMs. Specifically, We utilize the frozen LLM in the MLLM tovectorize the input prompt, which summarizes the requirements of the prompt.Then, we input the prompt vector into our trained Multi-Layer Perceptron (MLP)to align with the visual input requirements, and subsequently replace the classembedding in the image encoder. Since our model only requires adding atrainable MLP, it can be applied to any MLLM. To validate the effectiveness ofPIP-MM, we conducted experiments on multiple benchmarks. Automated evaluationmetrics and manual assessments demonstrate the strong performance of PIP-MM.Particularly noteworthy is that our model maintains excellent generationresults even when half of the visual tokens are reduced.
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Authors: Xiangcen Wu, Yipei Wang, Qianye Yang, Natasha Thorley, Shonit Punwani, Veeru Kasivisvanathan, Ester Bonmati, Yipeng Hu
Date: 2024-10-30 00:00:00
Tasks: Diagnostic, Specificity

Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to develop deep learning models that improve the overall cancer diagnostic accuracy, by classifying radiologist-identified patients or lesions (i.e. radiologist-positives), as opposed to the existing models that are trained to discriminate over all patients. We develop a single voxel-level classification model, with a simple percentage threshold to determine positive cases, at levels of lesions, Barzell-zones and patients. Based on the presented experiments from two clinical data sets, consisting of histopathology-labelled MR images from more than 800 and 500 patients in the respective UCLA and UCL PROMIS studies, we show that the proposed strategy can improve the diagnostic accuracy, by augmenting the radiologist reading of the MR imaging. Among varying definition of clinical significance, the proposed strategy, for example, achieved a specificity of 44.1% (with AI assistance) from 36.3% (by radiologists alone), at a controlled sensitivity of 80.0% on the publicly available UCLA data set. This provides measurable clinical values in a range of applications such as reducing unnecessary biopsies, lowering cost in cancer screening and quantifying risk in therapies.
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Spatiotemporal Agent Detection 2024
Authors: Tengfei Zhang, Heng Zhang, Ruyang Li, Qi Deng, YaQian Zhao, RenGang Li
Date: 2024-10-30 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

This report presents our team's solutions for the Track 1 of the 2024 ECCV ROAD++ Challenge. The task of Track 1 is spatiotemporal agent detection, which aims to construct an "agent tube" for road agents in consecutive video frames. Our solutions focus on the challenges in this task, including extreme-size objects, low-light scenarios, class imbalance, and fine-grained classification. Firstly, the extreme-size object detection heads are introduced to improve the detection performance of large and small objects. Secondly, we design a dual-stream detection model with a low-light enhancement stream to improve the performance of spatiotemporal agent detection in low-light scenes, and the feature fusion module to integrate features from different branches. Subsequently, we develop a multi-branch detection framework to mitigate the issues of class imbalance and fine-grained classification, and we design a pre-training and fine-tuning approach to optimize the above multi-branch framework. Besides, we employ some common data augmentation techniques, and improve the loss function and upsampling operation. We rank first in the test set of Track 1 for the ROAD++ Challenge 2024, and achieve 30.82% average video-mAP.
RSNet: A Light Framework for The Detection of SAR Ship Detection
Authors: Hongyu Chen, Chengcheng Chen, Fei Wang, Yuhu Shi, Weiming Zeng
Date: 2024-10-30 00:00:00
Tasks: SAR Ship Detection

Recent advancements in synthetic aperture radar (SAR) ship detection using deep learning have significantly improved accuracy and speed, yet effectively detecting small objects in complex backgrounds with fewer parameters remains a challenge. This letter introduces RSNet, a lightweight framework constructed to enhance ship detection in SAR imagery. To ensure accuracy with fewer parameters, we proposed Waveletpool-ContextGuided (WCG) as its backbone, guiding global context understanding through multi-scale wavelet features for effective detection in complex scenes. Additionally, Waveletpool-StarFusion (WSF) is introduced as the neck, employing a residual wavelet element-wise multiplication structure to achieve higher dimensional nonlinear features without increasing network width. The Lightweight-Shared (LS) module is designed as detect components to achieve efficient detection through lightweight shared convolutional structure and multi-format compatibility. Experiments on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID) demonstrate that RSNet achieves a strong balance between lightweight design and detection performance, surpassing many state-of-the-art detectors, reaching 72.5\% and 67.6\% in \textbf{\(\mathbf{mAP_{.50:.95}}\) }respectively with 1.49M parameters. Our code will be released soon.
Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping
Authors: Qianxu Wang, Congyue Deng, Tyler Ga Wei Lum, Yuanpei Chen, Yaodong Yang, Jeannette Bohg, Yixin Zhu, Leonidas Guibas
Date: 2024-10-30 00:00:00
Tasks: Decoder

One-shot transfer of dexterous grasps to novel scenes with object and context variations has been a challenging problem. While distilled feature fields from large vision models have enabled semantic correspondences across 3D scenes, their features are point-based and restricted to object surfaces, limiting their capability of modeling complex semantic feature distributions for hand-object interactions. In this work, we propose the \textit{neural attention field} for representing semantic-aware dense feature fields in the 3D space by modeling inter-point relevance instead of individual point features. Core to it is a transformer decoder that computes the cross-attention between any 3D query point with all the scene points, and provides the query point feature with an attention-based aggregation. We further propose a self-supervised framework for training the transformer decoder from only a few 3D pointclouds without hand demonstrations. Post-training, the attention field can be applied to novel scenes for semantics-aware dexterous grasping from one-shot demonstration. Experiments show that our method provides better optimization landscapes by encouraging the end-effector to focus on task-relevant scene regions, resulting in significant improvements in success rates on real robots compared with the feature-field-based methods.
DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes
Authors: Jialiang Zhang, Haoran Liu, Danshi Li, Xinqiang Yu, Haoran Geng, Yufei Ding, Jiayi Chen, He Wang
Date: 2024-10-30 00:00:00
Tasks: Benchmarking

Grasping in cluttered scenes remains highly challenging for dexterous hands due to the scarcity of data. To address this problem, we present a large-scale synthetic benchmark, encompassing 1319 objects, 8270 scenes, and 427 million grasps. Beyond benchmarking, we also propose a novel two-stage grasping method that learns efficiently from data by using a diffusion model that conditions on local geometry. Our proposed generative method outperforms all baselines in simulation experiments. Furthermore, with the aid of test-time-depth restoration, our method demonstrates zero-shot sim-to-real transfer, attaining 90.7% real-world dexterous grasping success rate in cluttered scenes.
LumiSculpt: A Consistency Lighting Control Network for Video Generation
Authors: Yuxin Zhang, Dandan Zheng, Biao Gong, Jingdong Chen, Ming Yang, WeiMing Dong, Changsheng Xu
Date: 2024-10-30 00:00:00
Tasks: Video Generation

Lighting plays a pivotal role in ensuring the naturalness of video generation, significantly influencing the aesthetic quality of the generated content. However, due to the deep coupling between lighting and the temporal features of videos, it remains challenging to disentangle and model independent and coherent lighting attributes, limiting the ability to control lighting in video generation. In this paper, inspired by the established controllable T2I models, we propose LumiSculpt, which, for the first time, enables precise and consistent lighting control in T2V generation models.LumiSculpt equips the video generation with strong interactive capabilities, allowing the input of custom lighting reference image sequences. Furthermore, the core learnable plug-and-play module of LumiSculpt facilitates remarkable control over lighting intensity, position, and trajectory in latent video diffusion models based on the advanced DiT backbone.Additionally, to effectively train LumiSculpt and address the issue of insufficient lighting data, we construct LumiHuman, a new lightweight and flexible dataset for portrait lighting of images and videos. Experimental results demonstrate that LumiSculpt achieves precise and high-quality lighting control in video generation.
EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models
Authors: Shangquan Sun, Wenqi Ren, Zikun Liu, Hyunhee Park, Rui Wang, Xiaochun Cao
Date: 2024-10-30 00:00:00
Tasks: Deblurring, Ensemble Learning, Image Restoration, Rain Removal, Super-Resolution

Image restoration has experienced significant advancements due to the development of deep learning. Nevertheless, it encounters challenges related to ill-posed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning, as a powerful machine learning technique, aims to address these deviations by combining the predictions of multiple base models. Most existing works adopt ensemble learning during the design of restoration models, while only limited research focuses on the inference-stage ensemble of pre-trained restoration models. Regression-based methods fail to enable efficient inference, leading researchers in academia and industry to prefer averaging as their choice for post-training ensemble. To address this, we reformulate the ensemble problem of image restoration into Gaussian mixture models (GMMs) and employ an expectation maximization (EM)-based algorithm to estimate ensemble weights for aggregating prediction candidates. We estimate the range-wise ensemble weights on a reference set and store them in a lookup table (LUT) for efficient ensemble inference on the test set. Our algorithm is model-agnostic and training-free, allowing seamless integration and enhancement of various pre-trained image restoration models. It consistently outperforms regression based methods and averaging ensemble approaches on 14 benchmarks across 3 image restoration tasks, including super-resolution, deblurring and deraining. The codes and all estimated weights have been released in Github.
AdaptiveISP: Learning an Adaptive Image Signal Processor for Object Detection
Authors: Yujin Wang, Tianyi Xu, Fan Zhang, Tianfan Xue, Jinwei Gu
Date: 2024-10-30 00:00:00
Tasks: Deep Reinforcement Learning, object-detection, Object Detection

Image Signal Processors (ISPs) convert raw sensor signals into digital images, which significantly influence the image quality and the performance of downstream computer vision tasks. Designing ISP pipeline and tuning ISP parameters are two key steps for building an imaging and vision system. To find optimal ISP configurations, recent works use deep neural networks as a proxy to search for ISP parameters or ISP pipelines. However, these methods are primarily designed to maximize the image quality, which are sub-optimal in the performance of high-level computer vision tasks such as detection, recognition, and tracking. Moreover, after training, the learned ISP pipelines are mostly fixed at the inference time, whose performance degrades in dynamic scenes. To jointly optimize ISP structures and parameters, we propose AdaptiveISP, a task-driven and scene-adaptive ISP. One key observation is that for the majority of input images, only a few processing modules are needed to improve the performance of downstream recognition tasks, and only a few inputs require more processing. Based on this, AdaptiveISP utilizes deep reinforcement learning to automatically generate an optimal ISP pipeline and the associated ISP parameters to maximize the detection performance. Experimental results show that AdaptiveISP not only surpasses the prior state-of-the-art methods for object detection but also dynamically manages the trade-off between detection performance and computational cost, especially suitable for scenes with large dynamic range variations. Project website: https://openimaginglab.github.io/AdaptiveISP/.
Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder
Authors: Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi, Laurent Caraffa, Flavian vasile, Jeremie Mary, Andrew Comport, Valerie Gouet-Brunet
Date: 2024-10-30 00:00:00
Tasks: 3D geometry, NeRF

While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interoperability with other latent-based 2D methods. The major challenge is that inverse graphics cannot be directly applied to such image latent spaces because they lack an underlying 3D geometry. In this paper, we propose an Inverse Graphics Autoencoder (IG-AE) that specifically addresses this issue. To this end, we regularize an image autoencoder with 3D-geometry by aligning its latent space with jointly trained latent 3D scenes. We utilize the trained IG-AE to bring NeRFs to the latent space with a latent NeRF training pipeline, which we implement in an open-source extension of the Nerfstudio framework, thereby unlocking latent scene learning for its supported methods. We experimentally confirm that Latent NeRFs trained with IG-AE present an improved quality compared to a standard autoencoder, all while exhibiting training and rendering accelerations with respect to NeRFs trained in the image space. Our project page can be found at https://ig-ae.github.io .