pwc-archive/papers-with-abstracts huggingface dataset:
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation
Authors: Dong Nie, Rui Lan, Ling Wang, Xiaofeng Ren
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Autonomous Driving, Decoder, Image Segmentation, Point Cloud Segmentation, Segmentation, Semantic Segmentation

    Semantic segmentation of point cloud data is a critical task for autonomous driving and other applications. Recent advances of point cloud segmentation are mainly driven by new designs of local aggregation operators and point sampling methods. Unlike image segmentation, few efforts have been made to understand the fundamental issue of scale and how scales should interact and be fused. In this work, we investigate how to efficiently and effectively integrate features at varying scales and varying stages in a point cloud segmentation network. In particular, we open up the commonly used encoder-decoder architecture, and design scale pyramid architectures that allow information to flow more freely and systematically, both laterally and upward/downward in scale. Moreover, a cross-scale attention feature learning block has been designed to enhance the multi-scale feature fusion which occurs everywhere in the network. Such a design of multi-scale processing and fusion gains large improvements in accuracy without adding much additional computation. When built on top of the popular KPConv network, we see consistent improvements on a wide range of datasets, including achieving state-of-the-art performance on NPM3D and S3DIS. Moreover, the pyramid architecture is generic and can be applied to other network designs: we show an example of similar improvements over RandLANet.
Deterministic Point Cloud Registration via Novel Transformation Decomposition
Authors: Wen Chen, Haoang Li, Qiang Nie, Yun-hui Liu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Point Cloud Registration

    Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF). Simultaneously estimating these 6 DOF is time-consuming due to high-dimensional parameter space. To solve this problem, it is common to decompose 6 DOF, i.e. independently compute 3-DOF rotation and 3-DOF translation. However, high non-linearity of 3-DOF rotation still limits the algorithm efficiency, especially when the number of correspondences is large. In contrast, we propose to decompose 6 DOF into (2+1) and (1+2) DOF. Specifically, (2+1) DOF represent 2-DOF rotation axis and 1-DOF displacement along this rotation axis. (1+2) DOF indicate 1-DOF rotation angle and 2-DOF displacement orthogonal to the above rotation axis. To compute these DOF, we design a novel two-stage strategy based on inlier set maximization. By leveraging branch and bound, we first search for (2+1) DOF, and then the remaining (1+2) DOF. Thanks to the proposed transformation decomposition and two-stage search strategy, our method is deterministic and leads to low computational complexity. We extensively compare our method with state-of-the-art approaches. Our method is more accurate and robust than the approaches that provide similar efficiency to ours. Our method is more efficient than the approaches whose accuracy and robustness are comparable to ours.
Fisher Information Guidance for Learned Time-of-Flight Imaging
Authors: Jiaqu Li, Tao Yue, Sijie Zhao, Xuemei Hu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    Indirect Time-of-Flight (ToF) imaging is widely applied in practice for its superiorities on cost and spatial resolution. However, lower signal-to-noise ratio (SNR) of measurement leads to larger error in ToF imaging, especially for imaging scenes with strong ambient light or long distance. In this paper, we propose a Fisher-information guided framework to jointly optimize the coding functions (light modulation and sensor demodulation functions) and the reconstruction network of iToF imaging, with the supervision of the proposed discriminative fisher loss. By introducing the differentiable modeling of physical imaging process considering various real factors and constraints, e.g., light-falloff with distance, physical implementability of coding functions, etc., followed by a dual-branch depth reconstruction neural network, the proposed method could learn the optimal iToF imaging system in an end-to-end manner. The effectiveness of the proposed method is extensively verified with both simulations and prototype experiments.
Global-Aware Registration of Less-Overlap RGB-D Scans
Authors: Che Sun, Yunde Jia, Yi Guo, Yuwei Wu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    We propose a novel method of registering less-overlap RGB-D scans. Our method learns global information of a scene to construct a panorama, and aligns RGB-D scans to the panorama to perform registration. Different from existing methods that use local feature points to register less-overlap RGB-D scans and mismatch too much, we use global information to guide the registration, thereby alleviating the mismatching problem by preserving global consistency of alignments. To this end, we build a scene inference network to construct the panorama representing global information. We introduce a reinforcement learning strategy to iteratively align RGB-D scans with the panorama and refine the panorama representation, which reduces the noise of global information and preserves global consistency of both geometric and photometric alignments. Experimental results on benchmark datasets including SUNCG, Matterport, and ScanNet show the superiority of our method.
Exposure Normalization and Compensation for Multiple-Exposure Correction
Authors: Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, Zhiwei Xiong
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Exposure Correction, Image Enhancement

    Images captured with improper exposures usually bring unsatisfactory visual effects. Previous works mainly focus on either underexposure or overexposure correction, resulting in poor generalization to various exposures. An alternative solution is to mix the multiple exposure data for training a single network. However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance. The key point to address this issue lies in bridging different exposure representations. To achieve this goal, we design a multiple exposure correction framework based on an Exposure Normalization and Compensation (ENC) module. Specifically, the ENC module consists of an exposure normalization part for mapping different exposure features to the exposure-invariant feature space, and a compensation part for integrating the initial features unprocessed by exposure normalization part to ensure the completeness of information. Besides, to further alleviate the imbalanced performance caused by variations in the optimization process, we introduce a parameter regularization fine-tuning strategy to improve the performance of the worst-performed exposure without degrading other exposures. Our model empowered by ENC outperforms the existing methods by more than 2dB and is robust to multiple image enhancement tasks, demonstrating its effectiveness and generalization capability for real-world applications.
Convolution of Convolution: Let Kernels Spatially Collaborate
Authors: Rongzhen Zhao, Jian Li, Zhenzhi Wu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    In the biological visual pathway, especially the retina, neurons are tiled along spatial dimensions with the electrical coupling as their local association, while in a convolution layer, kernels are placed along the channel dimension singly. We propose Convolution of Convolution, associating kernels in a layer and letting them collaborate spatially. With this method, a layer can provide feature maps with extra transformations and learn its kernels together instead of isolatedly. It is only used during training, bringing in negligible extra costs; and can be re-parameterized to common convolution before testing, boosting performance gratuitously in tasks like classification, detection and segmentation. Our method works even better when large receptive fields are demanded. The code is available on site: https://github.com/Genera1Z/ConvolutionOfConvolution.
Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification
Authors: Zongbo Han, Fan Yang, Junzhou Huang, Changqing Zhang, Jianhua Yao
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Classification, Informativeness, Medical Diagnosis, Multi-modal Classification

    Integration of heterogeneous and high-dimensional data (e.g., multiomics) is becoming increasingly important. Existing multimodal classification algorithms mainly focus on improving performance by exploiting the complementarity from different modalities. However, conventional approaches are basically weak in providing trustworthy multimodal fusion, especially for safety-critical applications (e.g., medical diagnosis). For this issue, we propose a novel trustworthy multimodal classification algorithm termed Multimodal Dynamics, which dynamically evaluates both the feature-level and modality-level informativeness for different samples and thus trustworthily integrates multiple modalities. Specifically, a sparse gating is introduced to capture the information variation of each within-modality feature and the true class probability is employed to assess the classification confidence of each modality. Then a transparent fusion algorithm based on the dynamical informativeness estimation strategy is induced. To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification. Extensive experiments are conducted on multimodal medical classification datasets. In these experiments, superior performance and trustworthiness of our algorithm are clearly validated compared to the state-of-the-art methods.
Blind Face Restoration via Integrating Face Shape and Generative Priors
Authors: Feida Zhu, Junwei Zhu, Wenqing Chu, Xinyi Zhang, Xiaozhong Ji, Chengjie Wang, Ying Tai
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D Reconstruction, Blind Face Restoration, Decoder

    Blind face restoration, which aims to reconstruct high-quality images from low-quality inputs, can benefit many applications. Although existing generative-based methods achieve significant progress in producing high-quality images, they often fail to restore natural face shapes and high-fidelity facial details from severely-degraded inputs. In this work, we propose to integrate shape and generative priors to guide the challenging blind face restoration. Firstly, we set up a shape restoration module to recover reasonable facial geometry with 3D reconstruction. Secondly, a pretrained facial generator is adopted as decoder to generate photo-realistic high-resolution images. To ensure high-fidelity, hierarchical spatial features extracted from the low-quality inputs and rendered 3D images are inserted into the decoder with our proposed Adaptive Feature Fusion Block (AFFB). Moreover, we introduce hybrid-level losses to jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task. The proposed Shape and Generative Prior integrated Network (SGPN) can restore high-quality images with clear face shapes and realistic facial details. Experimental results on synthetic and real-world datasets demonstrate SGPN performs favorably against state-of-the-art blind face restoration methods.
Effective Conditioned and Composed Image Retrieval Combining CLIP-Based Features
Authors: Alberto Baldrati, Marco Bertini, Tiberio Uricchio, Alberto del Bimbo
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Composed Image Retrieval (CoIR), Contrastive Learning, Image Retrieval, Retrieval

    Conditioned and composed image retrieval extend CBIR systems by combining a query image with an additional text that expresses the intent of the user, describing additional requests w.r.t. the visual content of the query image. This type of search is interesting for e-commerce applications, e.g. to develop interactive multimodal searches and chatbots. In this demo, we present an interactive system based on a combiner network, trained using contrastive learning, that combines visual and textual features obtained from the OpenAI CLIP network to address conditioned CBIR. The system can be used to improve e-shop search engines. For example, considering the fashion domain it lets users search for dresses, shirts and toptees using a candidate start image and expressing some visual differences w.r.t. its visual content, e.g. asking to change color, pattern or shape. The proposed network obtains state-of-the-art performance on the FashionIQ dataset and on the more recent CIRR dataset, showing its applicability to the fashion domain for conditioned retrieval, and to more generic content considering the more general task of composed image retrieval.
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees
Authors: Ruikun Zhou, Thanin Quartz, Hans De Sterck, Jun Liu
Date: 2022-06-04 00:00:00
Tasks: valid

Learning for control of dynamical systems with formal guarantees remains a challenging task. This paper proposes a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn a neural Lyapunov function to certify a region of attraction (ROA) for the closed-loop system. The algorithmic structure consists of two neural networks and a satisfiability modulo theories (SMT) solver. The first neural network is responsible for learning the unknown dynamics. The second neural network aims to identify a valid Lyapunov function and a provably stabilizing nonlinear controller. The SMT solver then verifies that the candidate Lyapunov function indeed satisfies the Lyapunov conditions. We provide theoretical guarantees of the proposed learning framework in terms of the closed-loop stability for the unknown nonlinear system. We illustrate the effectiveness of the approach with a set of numerical experiments.
PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers
Authors: Jiacong Xu, Zixiang Xiong, Shankar P. Bhattacharyya
Date: 2022-06-04 00:00:00
Proceeding: CVPR 2023 1
Tasks: Real-Time Semantic Segmentation, Semantic Segmentation

Two-branch network architecture has shown its efficiency and effectiveness in real-time semantic segmentation tasks. However, direct fusion of high-resolution details and low-frequency context has the drawback of detailed features being easily overwhelmed by surrounding contextual information. This overshoot phenomenon limits the improvement of the segmentation accuracy of existing two-branch models. In this paper, we make a connection between Convolutional Neural Networks (CNN) and Proportional-Integral-Derivative (PID) controllers and reveal that a two-branch network is equivalent to a Proportional-Integral (PI) controller, which inherently suffers from similar overshoot issues. To alleviate this problem, we propose a novel three-branch network architecture: PIDNet, which contains three branches to parse detailed, context and boundary information, respectively, and employs boundary attention to guide the fusion of detailed and context branches. Our family of PIDNets achieve the best trade-off between inference speed and accuracy and their accuracy surpasses all the existing models with similar inference speed on the Cityscapes and CamVid datasets. Specifically, PIDNet-S achieves 78.6% mIOU with inference speed of 93.2 FPS on Cityscapes and 80.1% mIOU with speed of 153.7 FPS on CamVid.
Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity
Authors: Yunpeng Xiao, Youpeng Zhao, Ge Yang
Date: 2022-06-06 00:00:00

Morphology of mitochondria plays critical roles in mediating their physiological functions. Accurate segmentation of mitochondria from 3D electron microscopy (EM) images is essential to quantitative characterization of their morphology at the nanometer scale. Fully supervised deep learning models developed for this task achieve excellent performance but require substantial amounts of annotated data for training. However, manual annotation of EM images is laborious and time-consuming because of their large volumes, limited contrast, and low signal-to-noise ratios (SNRs). To overcome this challenge, we propose a semi-supervised deep learning model that segments mitochondria by leveraging the spatial continuity of their structural, morphological, and contextual information in both labeled and unlabeled images. We use random piecewise affine transformation to synthesize comprehensive and realistic mitochondrial morphology for augmentation of training data. Experiments on the EPFL dataset show that our model achieves performance similar as that of state-of-the-art fully supervised models but requires only ~20% of their annotated training data. Our semi-supervised model is versatile and can also accurately segment other spatially continuous structures from EM images. Data and code of this study are openly accessible at https://github.com/cbmi-group/MPP.
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks
Authors: Mingze Wang, Chao Ma
Date: 2022-06-05 00:00:00

The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied. For a broad range of models and loss functions, including the most commonly used square loss and cross entropy loss, we prove an ``early stage convergence'' result. We show that the loss is decreased by a significant amount in the early stage of the training, and this decrease is fast. Furthurmore, for exponential type loss functions, and under some assumptions on the training data, we show global convergence of GD. Instead of relying on extreme over-parameterization, our study is based on a microscopic analysis of the activation patterns for the neurons, which helps us derive more powerful lower bounds for the gradient. The results on activation patterns, which we call ``neuron partition'', help build intuitions for understanding the behavior of neural networks' training dynamics, and may be of independent interest.
HIFI-Net: A Novel Network for Enhancement to Underwater Images
Authors: Jiajia Zhou, Junbin Zhuang, Yan Zheng, Di wu
Date: 2022-06-06 00:00:00

A novel network for enhancement to underwater images is proposed in this paper. It contains a Reinforcement Fusion Module for Haar wavelet images (RFM-Haar) based on Reinforcement Fusion Unit (RFU), which is used to fuse an original image and some important information within it. Fusion is achieved for better enhancement. As this network make "Haar Images into Fusion Images", it is called HIFI-Net. The experimental results show the proposed HIFI-Net performs best among many state-of-the-art methods on three datasets at three normal metrics and a new metric.
Robust Calibration with Multi-domain Temperature Scaling
Authors: Yaodong Yu, Stephen Bates, Yi Ma, Michael I. Jordan
Date: 2022-06-06 00:00:00
Tasks: Uncertainty Quantification

Uncertainty quantification is essential for the reliable deployment of machine learning models to high-stakes application domains. Uncertainty quantification is all the more challenging when training distribution and test distribution are different, even the distribution shifts are mild. Despite the ubiquity of distribution shifts in real-world applications, existing uncertainty quantification approaches mainly study the in-distribution setting where the train and test distributions are the same. In this paper, we develop a systematic calibration model to handle distribution shifts by leveraging data from multiple domains. Our proposed method -- multi-domain temperature scaling -- uses the heterogeneity in the domains to improve calibration robustness under distribution shift. Through experiments on three benchmark data sets, we find our proposed method outperforms existing methods as measured on both in-distribution and out-of-distribution test sets.
Understanding Self-Directed Learning in an Online Laboratory
Authors: Sungeun An, Spencer Rugaber, Jennifer Hammock, Ashok K. Goel
Date: 2022-06-06 00:00:00

We described a study on the use of an online laboratory for self-directed learning by constructing and simulating conceptual models of ecological systems. In this study, we could observe only the modeling behaviors and outcomes; the learning goals and outcomes were unknown. We used machine learning techniques to analyze the modeling behaviors of 315 learners and 822 conceptual models they generated. We derive three main conclusions from the results. First, learners manifest three types of modeling behaviors: observation (simulation focused), construction (construction focused), and full exploration (model construction, evaluation and revision). Second, while observation was the most common behavior among all learners, construction without evaluation was more common for less engaged learners and full exploration occurred mostly for more engaged learners. Third, learners who explored the full cycle of model construction, evaluation and revision generated models of higher quality. These modeling behaviors provide insights into self-directed learning at large.
Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AI
Authors: Fabrizia Auletta, Rachel W. Kallen, Mario di Bernardo, Micheal J. Richardson
Date: 2022-06-06 00:00:00
Tasks: Decision Making, Explainable artificial intelligence

This study uses supervised machine learning (SML) and explainable artificial intelligence (AI) to model, predict and understand human decision-making during skillful joint-action. Long short-term memory networks were trained to predict the target selection decisions of expert and novice actors completing a dyadic herding task. Results revealed that the trained models were expertise specific and could not only accurately predict the target selection decisions of expert and novice herders but could do so at timescales that preceded an actor's conscious intent. To understand what differentiated the target selection decisions of expert and novice actors, we then employed the explainable-AI technique, SHapley Additive exPlanation, to identify the importance of informational features (variables) on model predictions. This analysis revealed that experts were more influenced by information about the state of their co-herders compared to novices. The utility of employing SML and explainable-AI techniques for investigating human decision-making is discussed.