pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection
Authors: Yifan Wang, Wenbo Zhang, Lijun Wang, Ting Liu, Huchuan Lu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Deep Learning, object-detection, Object Detection, Saliency Detection, Salient Object Detection, Unsupervised Saliency Detection

    Deep learning-based image salient object detection (SOD) heavily relies on large-scale training data with pixel-wise labeling. High-quality labels involve intensive labor and are expensive to acquire. In this paper, we propose a novel multi-source uncertainty mining method to facilitate unsupervised deep learning from multiple noisy labels generated by traditional handcrafted SOD methods. We design an Uncertainty Mining Network (UMNet) which consists of multiple Merge-and-Split (MS) modules to recursively analyze the commonality and difference among multiple noisy labels and infer pixel-wise uncertainty map for each label. Meanwhile, we model the noisy labels using Gibbs distribution and propose a weighted uncertainty loss to jointly train the UMNet with the SOD network. As a consequence, our UMNet can adaptively select reliable labels for SOD network learning. Extensive experiments on benchmark datasets demonstrate that our method not only outperforms existing unsupervised methods, but also is on par with fully-supervised state-of-the-art models.
FMCNet: Feature-Level Modality Compensation for Visible-Infrared Person Re-Identification
Authors: Qiang Zhang, Changzhou Lai, Jianan Liu, Nianchang Huang, Jungong Han
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Person Re-Identification

    For Visible-Infrared person Re-IDentification (VI-ReID), existing modality-specific information compensation based models try to generate the images of missing modality from existing ones for reducing cross-modality discrepancy. However, because of the large modality discrepancy between visible and infrared images, the generated images usually have low qualities and introduce much more interfering information (e.g., color inconsistency). This greatly degrades the subsequent VI-ReID performance. Alternatively, we present a novel Feature-level Modality Compensation Network (FMCNet) for VIReID in this paper, which aims to compensate the missing modality-specific information in the feature level rather than in the image level, i.e., directly generating those missing modality-specific features of one modality from existing modality-shared features of the other modality. This will enable our model to mainly generate some discriminative person related modality-specific features and discard those non-discriminative ones for benefiting VI-ReID. For that, a single-modality feature decomposition module is first designed to decompose single-modality features into modality-specific ones and modality-shared ones. Then, a feature-level modality compensation module is present to generate those missing modality-specific features from existing modality-shared ones. Finally, a shared-specific feature fusion module is proposed to combine the existing and generated features for VI-ReID. The effectiveness of our proposed model is verified on two benchmark datasets.
Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space
Authors: Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    Tensor completion using multiway delay-embedding transform (MDT) (or Hankelization) suffers from the large memory requirement and high computational cost in spite of its high potentiality for the image modeling. Recent studies have shown high completion performance with a relatively small window size, but experiments with large window sizes require huge amount of memory and cannot be easily calculated. In this study, we address this serious computational issue, and propose its fast and efficient algorithm. Key techniques of the proposed method are based on two properties: (1) the signal after MDT can be diagonalized by Fourier transform, (2) an inverse MDT can be represented as a convolutional form. To use the properties, we modify MDT-Tucker, a method using Tucker decomposition with MDT, and introducing the fast and efficient algorithm. Our experiments show more than 100 times acceleration while maintaining high accuracy, and to realize the computation with large window size.
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior
Authors: Ruibo Li, Chi Zhang, Guosheng Lin, Zhe Wang, Chunhua Shen
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Motion Estimation, Self-Supervised Learning

    In this work, we focus on scene flow learning on point clouds in a self-supervised manner. A real-world scene can be well modeled as a collection of rigidly moving parts, therefore its scene flow can be represented as a combination of rigid motion of each part. Inspired by this observation, we propose to generate pseudo scene flow for self-supervised learning based on piecewise rigid motion estimation, in which the source point cloud is decomposed into a set of local regions and each region is treated as rigid. By rigidly aligning each region with its potential counterpart in the target point cloud, we obtain a region-specific rigid transformation to represent the flow, which together constitutes the pseudo scene flow labels of the entire scene to enable network training. Compared with most existing approaches relying on point-wise similarities for point matching, our method explicitly enforces region-wise rigid alignments, yielding locally rigid pseudo scene flow labels. We demonstrate the effectiveness of our self-supervised learning method on FlyingThings3D and KITTI datasets. Comprehensive experiments show that our method achieves new state-of-the-art performance in self-supervised scene flow learning, without any ground truth scene flow for supervision, even outperforming some supervised counterparts.
DiGS: Divergence Guided Shape Implicit Neural Representation for Unoriented Point Clouds
Authors: Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Stephen Gould
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Representation Learning, Surface Reconstruction

    Shape implicit neural representations (INR) have recently shown to be effective in shape analysis and reconstruction tasks. Existing INRs require point coordinates to learn the implicit level sets of the shape. When a normal vector is available for each point, a higher fidelity representation can be learned, however normal vectors are often not provided as raw data. Furthermore, the method's initialization has been shown to play a crucial role for surface reconstruction. In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input. We show that incorporating a soft constraint on the divergence of the distance function favours smooth solutions that reliably orients gradients to match the unknown normal at each point, in some cases even better than approaches that use ground truth normal vectors directly. Additionally, we introduce a novel geometric initialization method for sinusoidal INRs that further improves convergence to the desired solution. We evaluate the effectiveness of our approach on the task of surface reconstruction and shape space learning and show SOTA performance compared to other unoriented methods.
Learnable Lookup Table for Neural Network Quantization
Authors: Longguang Wang, Xiaoyu Dong, Yingqian Wang, Li Liu, Wei An, Yulan Guo
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Computational Efficiency, image-classification, Image Classification, Image Super-Resolution, Point Cloud Classification, Quantization, Super-Resolution

    Neural network quantization aims at reducing bit-widths of weights and activations for memory and computational efficiency. Since a linear quantizer (i.e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e.g., exponential function) with learnable parameters to build the quantizer for joint optimization. However, these complicated quantizers introduce considerable computational overhead during inference since activation quantization should be conducted online. In this paper, we formulate the quantization process as a simple lookup operation and propose to learn lookup tables as quantizers. Specifically, we develop differentiable lookup tables and introduce several training strategies for optimization. Our lookup tables can be trained with the network in an end-to-end manner to fit the distributions in different layers and have very small additional computational cost. Comparison with previous methods show that quantized networks using our lookup tables achieve state-of-the-art performance on image classification, image super-resolution, and point cloud classification tasks.
Volumetric Bundle Adjustment for Online Photorealistic Scene Capture
Authors: Ronald Clark
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    Efficient photorealistic scene capture is a challenging task. Current online reconstruction systems can operate very efficiently, but images generated from the models captured by these systems are often not photorealistic. Recent approaches based on neural volume rendering can render novel views at high fidelity, but they often require a long time to train, making them impractical for applications that require real-time scene capture. In this paper, we propose a system that can reconstruct photorealistic models of complex scenes in an efficient manner. Our system processes images online, i.e. it can obtain a good quality estimate of both the scene geometry and appearance at roughly the same rate the video is captured. To achieve the efficiency, we propose a hierarchical feature volume using VDB grids. This representation is memory efficient and allows for fast querying of the scene information. Secondly, we introduce a novel optimization technique that improves the efficiency of the bundle adjustment which allows our system to converge to the target camera poses and scene geometry much faster. Experiments on real-world scenes show that our method outperforms existing systems in terms of efficiency and capture quality. To the best of our knowledge, this is the first method that can achieve online photorealistic scene capture.
PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors
Authors: Jérome Revaud, Vincent Leroy, Philippe Weinzaepfel, Boris Chidlovskii
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Visual Localization

    Existing approaches for learning local image descriptors have shown remarkable achievements in a wide range of geometric tasks. However, most of them require per-pixel correspondence-level supervision, which is difficult to acquire at scale and in high quality. In this paper, we propose to explicitly integrate two matching priors in a single loss in order to learn local descriptors without supervision. Given two images depicting the same scene, we extract pixel descriptors and build a correlation volume. The first prior enforces the local consistency of matches in this volume via a pyramidal structure iteratively constructed using a non-parametric module. The second prior exploits the fact that each descriptor should match with at most one descriptor from the other image. We combine our unsupervised loss with a standard self-supervised loss trained from synthetic image augmentations. Feature descriptors learned by the proposed approach outperform their fully- and self-supervised counterparts on various geometric benchmarks such as visual localization and image matching, achieving state-of-the-art performance. Project webpage: https://europe.naverlabs.com/research/3d-vision/pump
Smooth Maximum Unit: Smooth Activation Function for Deep Networks Using Smoothing Maximum Technique
Authors: Koushik Biswas, Sandeep Kumar, Shilpak Banerjee, Ashish Kumar Pandey
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Deep Learning, image-classification, Image Classification, Machine Translation, object-detection, Object Detection, Semantic Segmentation

    Deep learning researchers have a keen interest in proposing new novel activation functions that can boost neural network performance. A good choice of activation function can have a significant effect on improving network performance and training dynamics. Rectified Linear Unit (ReLU) is a popular hand-designed activation function and is the most common choice in the deep learning community due to its simplicity though ReLU has some drawbacks. In this paper, we have proposed two new novel activation functions based on approximation of the maximum function, and we call these functions Smooth Maximum Unit (SMU and SMU-1). We show that SMU and SMU-1 can smoothly approximate ReLU, Leaky ReLU, or more general Maxout family, and GELU is a particular case of SMU. Replacing ReLU by SMU, Top-1 classification accuracy improves by 6.22%, 3.39%, 3.51%, and 3.08% on the CIFAR100 dataset with ShuffleNet V2, PreActResNet-50, ResNet-50, and SeNet-50 models respectively. Also, our experimental evaluation shows that SMU and SMU-1 improve network performance in a variety of deep learning tasks like image classification, object detection, semantic segmentation, and machine translation compared to widely used activation functions.
Dual-Generator Face Reenactment
Authors: Gee-Sern Hsu, Chun-Hung Tsai, Hung-Yi Wu
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Face Reenactment

    We propose the Dual-Generator (DG) network for large-pose face reenactment. Given a source face and a reference face as inputs, the DG network can generate an output face that has the same pose and expression as of the reference face, and has the same identity as of the source face. As most approaches do not particularly consider large-pose reenactment, the proposed approach addresses this issue by incorporating a 3D landmark detector into the framework and considering a loss function to capture visible local shape variation across large pose. The DG network consists of two modules, the ID-preserving Shape Generator (IDSG) and the Reenacted Face Generator (RFG). The IDSG encodes the 3D landmarks of the reference face into a reference landmark code, and encodes the source face into a source face code. The reference landmark code and the source face code are concatenated and decoded to a set of target landmarks that exhibits the pose and expression of the reference face and preserves the identity of the source face.
Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of SO(3)
Authors: Marc Alexa
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1

    Super-Fibonacci spirals are an extension of Fibonacci spirals, enabling fast generation of an arbitrary but fixed number of 3D orientations. The algorithm is simple and fast. A comprehensive evaluation comparing to other methods shows that the generated sets of orientations have low discrepancy, minimal spurious components in the power spectrum, and almost identical Voronoi volumes. This makes them useful for a variety of applications in vision, robotics, machine learning, and in particular Monte Carlo sampling.
The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift
Authors: Sara Beery, Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, Jonathan Huang
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Management

    Generalization to novel domains is a fundamental challenge for computer vision. Near-perfect accuracy on benchmarks is common, but these models do not work as expected when deployed outside of the training distribution. To build computer vision systems that truly solve real-world problems at global scale, we need benchmarks that fully capture real-world complexity, including geographic domain shift, long-tailed distributions, and data noise. We propose urban forest monitoring as an ideal testbed for studying and improving upon these computer vision challenges, while simultaneously working towards filling a crucial environmental and societal need. Urban forests provide significant benefits to urban societies (e.g., cleaner air and water, carbon sequestration, and energy savings among others). However, planning and maintaining these forests is expensive. One particularly costly aspect of urban forest management is monitoring the existing trees in a city: e.g., tracking tree locations, species, and health. Monitoring efforts are currently based on tree censuses built by human experts, costing cities millions of dollars per census and thus collected infrequently. Previous investigations into automating urban forest monitoring focused on small datasets from single cities, covering only common categories. To address these shortcomings, we introduce a new large-scale dataset that joins public tree censuses from 23 cities with a large collection of street level and aerial imagery. Our Auto Arborist dataset contains over 2.5M trees and 344 genera and is >2 orders of magnitude larger than the closest dataset in the literature. We introduce baseline results on our dataset across modalities as well as metrics for the detailed analysis of generalization with respect to geographic distribution shifts, vital for such a system to be deployed at-scale.
RCP: Recurrent Closest Point for Point Cloud
Authors: Xiaodong Gu, Chengzhou Tang, Weihao Yuan, Zuozhuo Dai, Siyu Zhu, Ping Tan
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Motion Estimation, Point Cloud Registration, Scene Flow Estimation

    3D motion estimation including scene flow and point cloud registration has drawn increasing interest. Inspired by 2D flow estimation, recent methods employ deep neural networks to construct the cost volume for estimating accurate 3D flow. However, these methods are limited by the fact that it is difficult to define a search window on point clouds because of the irregular data structure. In this paper, we avoid this irregularity by a simple yet effective method. We decompose the problem into two interlaced stages, where the 3D flows are optimized point-wisely at the first stage and then globally regularized in a recurrent network at the second stage. Therefore, the recurrent network only receives the regular point-wise information as the input. In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task. For 3D scene flow estimation, we make comparisons on the widely used FlyingThings3D and KITTI datasets. For point cloud registration, we follow previous works and evaluate the data pairs with large pose and partially overlapping from ModelNet40. The results show that our method outperforms the previous method and achieves a new state-of-the-art performance on both 3D scene flow estimation and point cloud registration, which demonstrates the superiority of the proposed zero-order method on irregular point cloud data. Our source code is available at https://github.com/gxd1994/RCP.
Discrete Time Convolution for Fast Event-Based Stereo
Authors: Kaixuan Zhang, Kaiwei Che, JianGuo Zhang, Jie Cheng, Ziyang Zhang, Qinghai Guo, Luziwei Leng
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Depth Estimation, Stereo Matching

    Inspired by biological retina, dynamical vision sensor transmits events of instantaneous changes of pixel intensity, giving it a series of advantages over traditional frame-based camera, such as high dynamical range, high temporal resolution and low power consumption. However, extracting information from highly asynchronous event data is a challenging task. Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics. Adopting channel-wise parameterization, temporal dynamics of the model is synchronized on the same feature map and diverges across different ones, enabling it to embed data in a variety of temporal scales. Abstracted from CTC, we further develop discrete time convolution (DTC) which accelerates the process with lower computational cost. We apply these methods to event-based multi-view stereo matching where they surpass state-of-the-art methods on benchmark criteria of the MVSEC dataset. Spatially sparse event data often leads to inaccurate estimation of edges and local contours. To address this problem, we propose a dual-path architecture in which the feature map is complemented by underlying edge information from original events extracted with spatially-adaptive denormalization. We demonstrate the superiority of our model in terms of speed (up to 110 FPS), accuracy and robustness, showing a great potential for real-time fast depth estimation. Finally, we perform experiments on the recent DSEC dataset to demonstrate the general usage of our model.
Likert Scoring With Grade Decoupling for Long-Term Action Assessment
Authors: Angchi Xu, Ling-An Zeng, Wei-Shi Zheng
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Action Assessment, Action Quality Assessment, Decoder

    Long-term action quality assessment is a task of evaluating how well an action is performed, namely, estimating a quality score from a long video. Intuitively, longterm actions generally involve parts exhibiting different levels of skill, and we call the levels of skill as performance grades. For example, technical highlights and faults may appear in the same long-term action. Hence, the final score should be determined by the comprehensive effect of different grades exhibited in the video. To explore this latent relationship, we design a novel Likert scoring paradigm inspired by the Likert scale in psychometrics, in which we quantify the grades explicitly and generate the final quality score by combining the quantitative values and the corresponding responses estimated from the video, instead of performing direct regression. Moreover, we extract gradespecific features, which will be used to estimate the responses of each grade, through a Transformer decoder architecture with diverse learnable queries. The whole model is named as Grade-decoupling Likert Transformer (GDLT), and we achieve state-of-the-art results on two long-term action assessment datasets.
Locality-Aware Inter- and Intra-Video Reconstruction for Self-Supervised Correspondence Learning
Authors: Liulei Li, Tianfei Zhou, Wenguan Wang, Lu Yang, Jianwu Li, Yi Yang
Date: 2022-01-01 00:00:00
Proceeding: CVPR 2022 1
Tasks: Position, Representation Learning, Video Reconstruction

    Our target is to learn visual correspondence from unlabeled videos. We develop LIIR, a locality-aware inter-and intra-video reconstruction framework that fills in three missing pieces, i.e., instance discrimination, location awareness, and spatial compactness, of self-supervised correspondence learning puzzle. First, instead of most existing efforts focusing on intra-video self-supervision only, we exploit cross video affinities as extra negative samples within a unified, inter-and intra-video reconstruction scheme. This enables instance discriminative representation learning by contrasting desired intra-video pixel association against negative inter-video correspondence. Second, we merge position information into correspondence matching, and design a position shifting strategy to remove the side-effect of position encoding during inter-video affinity computation, making our LIIR location-sensitive. Third, to make full use of the spatial continuity nature of video data, we impose a compactness-based constraint on correspondence matching, yielding more sparse and reliable solutions. The learned representation surpasses self-supervised state-of-the-arts on label propagation tasks including objects, semantic parts, and keypoints.