mtybilly/Instruct2DS huggingface dataset:
What Can Style Transfer and Paintings Do for Model Robustness?
Authors: Hubert Lin, Mitchell van Zuijlen, Sylvia C. Pont, Maarten W.A. Wijntjes, Kavita Bala
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: A common strategy for improving model robustness is through data augmentations. Data augmentations encourage models to learn desired invariances, such as invariance to horizontal flipping or small changes in color. Recent work has shown that arbitrary style transfer can be used as a form of data augmentation to encourage invariance to textures by creating painting-like images from photographs. However, a stylized photograph is not quite the same as an artist-created painting. Artists depict perceptually meaningful cues in paintings so that humans can recognize salient components in scenes, an emphasis which is not enforced in style transfer. Therefore, we study how style transfer and paintings differ in their impact on model robustness. First, we investigate the role of paintings as style images for stylization-based data augmentation. We find that style transfer functions well even without paintings as style images. Second, we show that learning from paintings as a form of perceptual data augmentation can improve model robustness. Finally, we investigate the invariances learned from stylization and from paintings, and show that models learn different invariances from these differing forms of data. Our results provide insights into how stylization improves model robustness, and provide evidence that artist-created paintings can be a valuable source of data for model robustness.
Taming Transformers for High-Resolution Image Synthesis
Authors: Patrick Esser, Robin Rombach, Bjorn Ommer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions. This makes them expressive, but also computationally infeasible for long sequences, such as high-resolution images. We demonstrate how combining the effectiveness of the inductive bias of CNNs with the expressivity of transformers enables them to model and thereby synthesize high-resolution images. We show how to (i) use CNNs to learn a context-rich vocabulary of image constituents, and in turn (ii) utilize transformers to efficiently model their composition within high-resolution images. Our approach is readily applied to conditional synthesis tasks, where both non-spatial information, such as object classes, and spatial information, such as segmentations, can control the generated image. In particular, we present the first results on semantically-guided synthesis of megapixel images with transformers.
Learning the Predictability of the Future
Authors: Didac Suris, Ruoshi Liu, Carl Vondrick
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data which features are predictable. Based on the observation that hyperbolic geometry naturally and compactly encodes hierarchical structure, we propose a predictive model in hyperbolic space. When the model is most confident, it will predict at a concrete level of the hierarchy, but when the model is not confident, it learns to automatically select a higher level of abstraction. Experiments on two established datasets show the key role of hierarchical representations for action prediction. Although our representation is trained with unlabeled video, visualizations show that action hierarchies emerge in the representation.
Multiple Instance Captioning: Learning Representations From Histopathology Textbooks and Articles
Authors: Jevgenij Gamper, Nasir Rajpoot
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH on the other hand contains dense diagnostic and morphological descriptions for a range of stains, tissue types and pathologies. Using intrinsic dimensionality estimation, we show that ARCH is the only CP dataset to (ARCH-)rival its computer vision analog MS-COCO Captions. We conjecture that an encoder pre-trained on dense image captions learns transferable representations for most CP tasks. We support the conjecture with evidence that ARCH representation transfers to a variety of pathology sub-tasks better than ImageNet features or representations obtained via self-supervised or multi-task learning on pathology images alone. We release our best model and invite other researchers to test it on their CP tasks.
Beyond Max-Margin: Class Margin Equilibrium for Few-Shot Object Detection
Authors: Bohao Li, Boyu Yang, Chang Liu, Feng Liu, Rongrong Ji, Qixiang Ye
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Few-shot object detection has made encouraging progress by reconstructing novel class objects using the feature representation learned upon a set of base classes. However, an implicit contradiction about reconstruction and classification is unfortunately ignored. On the one hand, to precisely reconstruct novel classes, the distributions of base classes should be close to those of novel classes (min-margin). On the other hand, to perform accurate classification, the distributions of either two classes must be far away from each other (max-margin). In this paper, we propose a class margin equilibrium (CME) approach, with the aim to optimize both feature space partition and novel class reconstruction in a systematic way. CME first converts the few-shot detection problem to the few-shot classification problem by using a fully connection layer to decouple localization features. CME then reserves adequate margin space for novel classes by introducing simple-yet-effective class margin loss during feature learning. Finally, CME pursues margin equilibrium by disturbing the features of novel class instances in an adversarial min-max fashion. Experiments on Pascal VOC and MS-COCO datasets show that CME improves two baseline detectors (up to 5% in average), achieving new state-of-the-art performance.
Consistent Instance False Positive Improves Fairness in Face Recognition
Authors: Xingkun Xu, Yuge Huang, Pengcheng Shen, Shaoxin Li, Jilin Li, Feiyue Huang, Yong Li, Zhen Cui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Demographic bias is a significant challenge in practical face recognition systems. Several methods have been proposed to reduce the bias, which rely on accurate demographic annotations. However, such annotations are usually not available in real scenarios. Moreover, these methods are explicitly designed for a specific demographic group divided by a predefined attribute, which is typically not general across different demographic groups divided by various attributes, such as race, gender, and age. In this paper, we propose a false positive rate penalty loss, which mitigates face recognition bias by increasing the consistency of instance false positive rate (FPR). Specifically, we first define the instance FPR as the ratio between the number of the non-target similarities above a unified threshold and the total number of the non-target similarities. The unified threshold is estimated for a given total FPR. Then, we introduce an additional false positive penalty term into the softmaxbased losses to promote the consistency of instance FPRs. Compared with the previous debiasing methods, our method requires no demographic annotations and can mitigate the bias across demographic groups divided by various kinds of attribute which are no need to be predefined in training. Extensive experimental results on popular benchmarks demonstrate the superiority of our method over state-of-the art competitors.
Learning Dynamic Network Using a Reuse Gate Function in Semi-Supervised Video Object Segmentation
Authors: Hyojin Park, Jayeon Yoo, Seohyeong Jeong, Ganesh Venkatesh, Nojun Kwak
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame. This results in high-quality segmentation across challenging scenarios such as changes in appearance and occlusion. But it also leads to unnecessary computations for stationary or slow-moving objects where the change across frames is minimal. In this work, we exploit this observation by using temporal information to quickly identify frames with minimal change and skip the heavyweight mask generation step. To realize this efficiency, we propose a novel dynamic network that estimates change across frames and decides which path -- computing a full network or reusing previous frame's feature -- to choose depending on the expected similarity. Experimental results show that our approach significantly improves inference speed without much accuracy degradation on challenging Semi-VOS datasets -- DAVIS 16, DAVIS 17, and YouTube-VOS. Furthermore, our approach can be applied to multiple Semi-VOS methods demonstrating its generality. The code is available in https://github.com/HYOJINPARK/Reuse VOS.
RaScaNet: Learning Tiny Models by Raster-Scanning Images
Authors: Jaehyoung Yoo, Dongwook Lee, Changyong Son, Sangil Jung, ByungIn Yoo, Changkyu Choi, Jae-Joon Han, Bohyung Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Deploying deep convolutional neural networks on ultra-low power systems is challenging due to the extremely limited resources. Especially, the memory becomes a bottleneck as the systems put a hard limit on the size of on-chip memory. Because peak memory explosion in the lower layers is critical even in tiny models, the size of an input image should be reduced with sacrifice in accuracy. To overcome this drawback, we propose a novel Raster-Scanning Network, named RaScaNet, inspired by raster-scanning in image sensors. RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network. The proposed method operates on an ultra-low power system without input size reduction; it requires 15.9-24.3x smaller peak memory and 5.3-12.9x smaller weight memory than the state-of-the-art tiny models. Moreover, RaScaNet fully exploits on-chip SRAM and cache memory of the system as the sum of the peak memory and the weight memory does not exceed 60 KB, improving the power efficiency of the system. In our experiments, we demonstrate the binary classification performance of RaScaNet on Visual Wake Words and Pascal VOC datasets.
AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning
Authors: Madeleine Grunde-McLaughlin, Ranjay Krishna, Maneesh Agrawala
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Visual events are a composition of temporal actions involving actors spatially interacting with objects. When developing computer vision models that can reason about compositional spatio-temporal events, we need benchmarks that can analyze progress and uncover shortcomings. Existing video question answering benchmarks are useful, but they often conflate multiple sources of error into one accuracy metric and have strong biases that models can exploit, making it difficult to pinpoint model weaknesses. We present Action Genome Question Answering (AGQA), a new benchmark for compositional spatio-temporal reasoning. AGQA contains 192M unbalanced question answer pairs for 9.6K videos. We also provide a balanced subset of 3.9M question answer pairs, 3 orders of magnitude larger than existing benchmarks, that minimizes bias by balancing the answer distributions and types of question structures. Although human evaluators marked 86.02% of our question-answer pairs as correct, the best model achieves only 47.74% accuracy. In addition, AGQA introduces multiple training/test splits to test for various reasoning abilities, including generalization to novel compositions, to indirect references, and to more compositional steps. Using AGQA, we evaluate modern visual reasoning systems, demonstrating that the best models barely perform better than non-visual baselines exploiting linguistic biases and that none of the existing models generalize to novel compositions unseen during training.
Exploring intermediate representation for monocular vehicle pose estimation
Authors: Shichao Li, Zengqiang Yan, Hongyang Li, Kwang-Ting Cheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present a new learning-based framework to recover vehicle pose in SO(3) from a single RGB image. In contrast to previous works that map local appearance to observation angles, we explore a progressive approach by extracting meaningful Intermediate Geometrical Representations (IGRs) to estimate egocentric vehicle orientation. This approach features a deep model that transforms perceived intensities to IGRs, which are mapped to a 3D representation encoding object orientation in the camera coordinate system. Core problems are what IGRs to use and how to learn them more effectively. We answer the former question by designing IGRs based on an interpolated cuboid that derives from primitive 3D annotation readily. The latter question motivates us to incorporate geometry knowledge with a new loss function based on a projective invariant. This loss function allows unlabeled data to be used in the training stage to improve representation learning. Without additional labels, our system outperforms previous monocular RGB-based methods for joint vehicle detection and pose estimation on the KITTI benchmark, achieving performance even comparable to stereo methods. Code and pre-trained models are available at this HTTPS URL.
Shallow Feature Matters for Weakly Supervised Object Localization
Authors: Jun Wei, Qin Wang, Zhen Li, Sheng Wang, S. Kevin Zhou, Shuguang Cui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Weakly supervised object localization (WSOL) aims to localize objects by only utilizing image-level labels. Class activation maps (CAMs) are the commonly used features to achieve WSOL. However, previous CAM-based methods did not take full advantage of the shallow features, despite their importance for WSOL. Because shallow features are easily buried in background noise through conventional fusion. In this paper, we propose a simple but effective Shallow feature-aware Pseudo supervised Object Localization (SPOL) model for accurate WSOL, which makes the utmost of low-level features embedded in shallow layers. In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly. Besides, we further propose a general class-agnostic segmentation model to achieve the accurate object mask, by only using the initial CAMs as the pseudo label without any extra annotation. Eventually, a bounding box extractor is applied to the object mask to locate the target. Experiments verify that our SPOL outperforms the state-of-the-art on both CUB-200 and ImageNet-1K benchmarks, achieving 93.44% and 67.15% (i.e., 3.93% and 2.13% improvement) Top-5 localization accuracy, respectively.
Capturing Omni-Range Context for Omnidirectional Segmentation
Authors: Kailun Yang, Jiaming Zhang, Simon Reiss, Xinxin Hu, Rainer Stiefelhagen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street-scenes, omnidirectional cameras present themselves as a perfect fit in such systems. Most segmentation models for parsing urban environments operate on common, narrow Field of View (FoV) images. Transferring these models from the domain they were designed for to 360-degree perception, their performance drops dramatically, e.g., by an absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of FoV and structural distribution between the imaging domains, we introduce Efficient Concurrent Attention Networks (ECANets), directly capturing the inherent long-range dependencies in omnidirectional imagery. In addition to the learned attention-based contextual priors that can stretch across 360-degree images, we upgrade model training by leveraging multi-source and omni-supervised learning, taking advantage of both: Densely labeled and unlabeled data originating from multiple datasets. To foster progress in panoramic image segmentation, we put forward and extensively evaluate models on Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture diverse scenes from all around the globe. Our novel model, training regimen and multi-source prediction fusion elevate the performance (mIoU) to new state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS (69.0%) benchmarks.
PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation With Neural Positional Encoding and Distilled Matting Loss
Authors: Juan Luis Gonzalez, Munchurl Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we propose a self-supervised single-view pixel-level accurate depth estimation network, called PLADE-Net. The PLADE-Net is the first work that shows unprecedented accuracy levels, exceeding 95% in terms of the \delta^1 metric on the challenging KITTI dataset. Our PLADE-Net is based on a new network architecture with neural positional encoding and a novel loss function that borrows from the closed-form solution of the matting Laplacian to learn pixel-level accurate depth estimation from stereo images. Neural positional encoding allows our PLADE-Net to obtain more consistent depth estimates by letting the network reason about location-specific image properties such as lens and projection distortions. Our novel distilled matting Laplacian loss allows our network to predict sharp depths at object boundaries and more consistent depths in highly homogeneous regions. Our proposed method outperforms all previous self-supervised single-view depth estimation methods by a large margin on the challenging KITTI dataset, with unprecedented levels of accuracy. Furthermore, our PLADE-Net, naively extended for stereo inputs, outperforms the most recent self-supervised stereo methods, even without any advanced blocks like 1D correlations, 3D convolutions, or spatial pyramid pooling. We present extensive ablation studies and experiments that support our method's effectiveness on the KITTI, CityScapes, and Make3D datasets.
Reciprocal Landmark Detection and Tracking With Extremely Few Annotations
Authors: Jianzhe Lin, Ghazal Sahebzamani, Christina Luong, Fatemeh Taheri Dezaki, Mohammad Jafari, Purang Abolmaesumi, Teresa Tsang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machine learning framework to perform such localization is hindered given the sparse nature of gold standard labels; only few percent of cardiac cine series frames are normally manually labeled for clinical use. In this paper, we propose a new end-to-end reciprocal detection and tracking model that is specifically designed to handle the sparse nature of echocardiography labels. The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames. The superiority of the proposed reciprocal model is demonstrated using a series of experiments.
Practical Single-Image Super-Resolution Using Look-Up Table
Authors: Younghyun Jo, Seon Joo Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: A number of super-resolution (SR) algorithms from interpolation to deep neural networks (DNN) have emerged to restore or create missing details of the input low-resolution image. As mobile devices and display hardware develops, the demand for practical SR technology has increased. Current state-of-the-art SR methods are based on DNNs for better quality. However, they are feasible when executed by using a parallel computing module (e.g. GPUs), and have been difficult to apply to general uses such as end-user software, smartphones, and televisions. To this end, we propose an efficient and practical approach for the SR by adopting look-up table (LUT). We train a deep SR network with a small receptive field and transfer the output values of the learned deep model to the LUT. At test time, we retrieve the precomputed HR output values from the LUT for query LR input pixels. The proposed method can be performed very quickly because it does not require a large number of floating point operations. Experimental results show the efficiency and the effectiveness of our method. Especially, our method runs faster while showing better quality compared to bicubic interpolation.
Removing the Background by Adding the Background: Towards Background Robust Self-Supervised Video Representation Learning
Authors: Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J. Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, Xing Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself. However, some of the current methods tend to cheat from the background, i.e., the prediction is highly dependent on the video background instead of the motion, making the model vulnerable to background changes. To mitigate the model reliance towards the background, we propose to remove the background impact by adding the background. That is, given a video, we randomly select a static frame and add it to every other frames to construct a distracting video sample. Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes. We term our method as Background Erasing (BE). It is worth noting that the implementation of our method is so simple and neat and can be added to most of the SOTA methods without much efforts. Specifically, BE brings 16.4% and 19.1% improvements with MoCo on the severely biased datasets UCF101 and HMDB51, and 14.5% improvement on the less biased dataset Diving48.
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
Authors: Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: 6D pose estimation from a single RGB image is a fundamental task in computer vision. The current top-performing deep learning-based methods rely on an indirect strategy, i.e., first establishing 2D-3D correspondences between the coordinates in the image plane and object coordinate system, and then applying a variant of the PnP/RANSAC algorithm. However, this two-stage pipeline is not end-to-end trainable, thus is hard to be employed for many tasks requiring differentiable poses. On the other hand, methods based on direct regression are currently inferior to geometry-based methods. In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V datasets. Code is available at https://git.io/GDR-Net.
Point Cloud Upsampling via Disentangled Refinement
Authors: Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disentangle the task based on its multi-objective nature and formulate two cascaded sub-networks, a dense generator and a spatial refiner. The dense generator infers a coarse but dense output that roughly describes the underlying surface, while the spatial refiner further fine-tunes the coarse output by adjusting the location of each point. Specifically, we design a pair of local and global refinement units in the spatial refiner to evolve a coarse feature map. Also, in the spatial refiner, we regress a per-point offset vector to further adjust the coarse outputs in fine scale. Extensive qualitative and quantitative results on both synthetic and real-scanned datasets demonstrate the superiority of our method over the state-of-the-arts.
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion
Authors: Cheng Chi, Qingjie Wang, Tianyu Hao, Peng Guo, Xin Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Precise estimation of optical flow, stereo depth and camera motion are important for the real-world 3D scene understanding and visual perception. Since the three tasks are tightly coupled with the inherent 3D geometric constraints, current studies have demonstrated that the three tasks can be improved through jointly optimizing geometric loss functions of several individual networks. In this paper, we show that effective feature-level collaboration of the networks for the three respective tasks could achieve much greater performance improvement for all three tasks than only loss-level joint optimization. Specifically, we propose a single network to combine and improve the three tasks. The network extracts the features of two consecutive stereo images, and simultaneously estimates optical flow, stereo depth and camera motion. The whole network mainly contains four parts: (I) a feature-sharing encoder to extract features of input images, which can enhance features' representation ability; (II) a pooled decoder to estimate both optical flow and stereo depth; (III) a camera pose estimation module which fuses optical flow and stereo depth information; (IV) a cost volume complement module to improve the performance of optical flow in static and occluded regions. Our method achieves state-of-the-art performance among the joint unsupervised methods, including optical flow and stereo depth estimation on KITTI 2012 and 2015 benchmarks, and camera motion estimation on KITTI VO dataset.
A Generalized Loss Function for Crowd Counting and Localization
Authors: Jia Wan, Ziquan Liu, Antoni B. Chan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Previous work shows that a better density map representation can improve the performance of crowd counting. In this paper, we investigate learning the density map representation through an unbalanced optimal transport problem, and propose a generalized loss function to learn density maps for crowd counting and localization. We prove that pixel-wise L2 loss and Bayesian loss are special cases and suboptimal solutions to our proposed loss function. A perspective-guided transport cost function is further proposed to better handle the perspective transformation in crowd images. Since the predicted density will be pushed toward annotation positions, the density map prediction will be sparse and can naturally be used for localization. Finally, the proposed loss outperforms other losses on four large-scale datasets for counting, and achieves the best localization performance on NWPU-Crowd and UCF-QNRF.
Learning Fine-Grained Segmentation of 3D Shapes Without Part Labels
Authors: Xiaogang Wang, Xun Sun, Xinyu Cao, Kai Xu, Bin Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Existing learning-based approaches to 3D shape segmentation usually formulate it as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of labels. This assumption, however, is unrealistic for training fine-grained segmentation on large datasets since the annotation of fine-grained parts is extremely tedious. In this paper, we approach the problem with deep clustering, where the key idea is to learn part priors from a dataset with fine-grained segmentation but no part annotations. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part-based segmentation through minimizing a novel low rank loss. Further, since fine-grained parts can be very tiny, a 3D shape has to be densely sampled to ensure the tiny parts are well captured and segmented. To handle densely sampled point sets, we adopt a divide-and-conquer scheme. We first partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network (PriorNet) trained in a category-agnostic manner. We then train MergeNet, a graph convolution network, to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing significant advantage over the state-of-the-art ones.
Fine-Grained Shape-Appearance Mutual Learning for Cloth-Changing Person Re-Identification
Authors: Peixian Hong, Tao Wu, Ancong Wu, Xintong Han, Wei-Shi Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Recently, person re-identification (Re-ID) has achieved great progress. However, current methods largely depend on color appearance, which is not reliable when a person changes the clothes. Cloth-changing Re-ID is challenging since pedestrian images with clothes change exhibit large intra-class variation and small inter-class variation. Some significant features for identification are embedded in unobvious body shape differences across pedestrians. To explore such body shape cues for cloth-changing Re-ID, we propose a Fine-grained Shape-Appearance Mutual learning framework (FSAM), a two-stream framework that learns fine-grained discriminative body shape knowledge in a shape stream and transfers it to an appearance stream to complement the cloth-unrelated knowledge in the appearance features. Specifically, in the shape stream, FSAM learns fine-grained discriminative mask with the guidance of identities and extracts fine-grained body shape features by a pose-specific multi-branch network. To complement cloth-unrelated shape knowledge in the appearance stream, dense interactive mutual learning is performed across low-level and high-level features to transfer knowledge from shape stream to appearance stream, which enables the appearance stream to be deployed independently without extra computation for mask estimation. We evaluated our method on benchmark cloth-changing Re-ID datasets and achieved the start-of-the-art performance.
DeepSurfels: Learning Online Appearance Fusion
Authors: Marko Mihajlovic, Silvan Weder, Marc Pollefeys, Martin R. Oswald
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information. DeepSurfels combines explicit and neural building blocks to jointly encode geometry and appearance information. In contrast to established representations, DeepSurfels better represents high-frequency textures, is well-suited for online updates of appearance information, and can be easily combined with machine learning methods. We further present an end-to-end trainable online appearance fusion pipeline that fuses information from RGB images into the proposed scene representation and is trained using self-supervision imposed by the reprojection error with respect to the input images. Our method compares favorably to classical texture mapping approaches as well as recent learning-based techniques. Moreover, we demonstrate lower runtime, improved generalization capabilities, and better scalability to larger scenes compared to existing methods.
Joint Negative and Positive Learning for Noisy Labels
Authors: Youngdong Kim, Juseung Yun, Hyounguk Shon, Junmo Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Training of Convolutional Neural Networks (CNNs) with data with noisy labels is known to be a challenge. Based on the fact that directly providing the label to the data (Positive Learning; PL) has a risk of allowing CNNs to memorize the contaminated labels for the case of noisy data, the indirect learning approach that uses complementary labels (Negative Learning for Noisy Labels; NLNL) has proven to be highly effective in preventing overfitting to noisy data as it reduces the risk of providing faulty target. NLNL further employs a three-stage pipeline to improve convergence. As a result, filtering noisy data through the NLNL pipeline is cumbersome, increasing the training cost. In this study, we propose a novel improvement of NLNL, named Joint Negative and Positive Learning (JNPL), that unifies the filtering pipeline into a single stage. JNPL trains CNN via two losses, NL+ and PL+, which are improved upon NL and PL loss functions, respectively. We analyze the fundamental issue of NL loss function and develop new NL+ loss function producing gradient that enhances the convergence of noisy data. Furthermore, PL+ loss function is designed to enable faster convergence to expected-to-be-clean data. We show that the NL+ and PL+ train CNN simultaneously, significantly simplifying the pipeline, allowing greater ease of practical use compared to NLNL. With a simple semi-supervised training technique, our method achieves state-of-the-art accuracy for noisy data classification based on the superior filtering ability.
Generalizing Face Forgery Detection With High-Frequency Features
Authors: Yuchen Luo, Yong Zhang, Junchi Yan, Wei Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
Authors: Yawei Li, Wen Li, Martin Danelljan, Kai Zhang, Shuhang Gu, Luc Van Gool, Radu Timofte
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance. Based on that, we articulate the "heterogeneity hypothesis": with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity. The LW-DNA models are identified without extra computational cost or training time compared with the original network. This constraint leads to controlled experiments which direct the focus to the importance of layer-wise specific channel configurations. LW-DNA models come with advantages related to overfitting, i.e. the relative relationship between model complexity and dataset size. Experiments are conducted on various networks and datasets for image classification, visual tracking and image restoration. The resultant LW-DNA models consistently outperform the baseline models. Code is available at https://github.com/ofsoundof/Heterogeneity_Hypothesis.git.
Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
Authors: Siyan Dong, Qingnan Fan, He Wang, Ji Shi, Li Yi, Thomas Funkhouser, Baoquan Chen, Leonidas J. Guibas
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Localizing the camera in a known indoor environment is a key building block for scene mapping, robot navigation, AR, etc. Recent advances estimate the camera pose via optimization over the 2D/3D-3D correspondences established between the coordinates in 2D/3D camera space and 3D world space. Such a mapping is estimated with either a convolution neural network or a decision tree using only the static input image sequence, which makes these approaches vulnerable to dynamic indoor environments that are quite common yet challenging in the real world. To address the aforementioned issues, in this paper, we propose a novel outlier-aware neural tree which bridges the two worlds, deep learning and decision tree approaches. It builds on three important blocks: (a) a hierarchical space partition over the indoor scene to construct the decision tree; (b) a neural routing function, implemented as a deep classification network, employed for better 3D scene understanding; and (c) an outlier rejection module used to filter out dynamic points during the hierarchical routing process. Our proposed algorithm is evaluated on the RIO-10 benchmark developed for camera relocalization in dynamic indoor environments. It achieves robust neural routing through space partitions and outperforms the state-of-the-art approaches by around 30% on camera pose accuracy, while running comparably fast for evaluation.
Facial Action Unit Detection With Transformers
Authors: Geethu Miriam Jacob, Bjorn Stenger
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The Facial Action Coding System is a taxonomy for fine-grained facial expression analysis. This paper proposes a method for detecting Facial Action Units (FAU), which define particular face muscle activity, from an input image. FAU detection is formulated as a multi-task learning problem, where image features and attention maps are input to a branch for each action unit to extract discriminative feature embeddings, using a new loss function, the Center Contrastive (CC) loss. We employ a new FAU correlation network, based on a transformer encoder architecture, to capture the relationships between different action units for the wide range of expressions in the training data. The resulting features are shown to yield high classification performance. We validate our design choices, including the use of CC loss and Tversky loss functions, in ablative experiments. We show that the proposed method outperforms state-of-theart techniques on two public datasets, BP4D and DISFA, with an absolute improvement of the F1-score of over 2% on each.
Exploiting Aliasing for Manga Restoration
Authors: Minshan Xie, Menghan Xia, Tien-Tsin Wong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative two-stage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SE-Net) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method.
Discovering Hidden Physics Behind Transport Dynamics
Authors: Peirong Liu, Lin Tian, Yubo Zhang, Stephen Aylward, Yueh Lee, Marc Niethammer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by injecting a tracer. An advection-diffusion equation is widely used to describe these transport phenomena. Our goal is estimating the underlying physics of advection-diffusion equations, expressed as velocity and diffusion tensor fields. We propose a learning framework (YETI) building on an auto-encoder structure between 2D and 3D image time-series, which incorporates the advection-diffusion model. To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields. Instead of directly learning these velocity and diffusion tensor fields, we introduce representations that assure incompressible flow and symmetric positive semi-definite diffusion fields and demonstrate the additional benefits of these representations on improving estimation accuracy. We further use transfer learning to apply YETI on a public brain magnetic resonance (MR) perfusion dataset of stroke patients and show its ability to successfully distinguish stroke lesions from normal brain regions via the estimated velocity and diffusion tensor fields.
Cross-View Gait Recognition With Deep Universal Linear Embeddings
Authors: Shaoxiong Zhang, Yunhong Wang, Annan Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Gait is considered an attractive biometric identifier for its non-invasive and non-cooperative features compared with other biometric identifiers such as fingerprint and iris. At present, cross-view gait recognition methods always establish representations from various deep convolutional networks for recognition and ignore the potential dynamical information of the gait sequences. If assuming that pedestrians have different walking patterns, gait recognition can be performed by calculating their dynamical features from each view. This paper introduces the Koopman operator theory to gait recognition, which can find an embedding space for a global linear approximation of a nonlinear dynamical system. Furthermore, a novel framework based on convolutional variational autoencoder and deep Koopman embedding is proposed to approximate the Koopman operators, which is used as dynamical features from the linearized embedding space for cross-view gait recognition. It gives solid physical interpretability for a gait recognition system. Experiments on a large public dataset, OU-MVLP, prove the effectiveness of the proposed method.
Tuning IR-Cut Filter for Illumination-Aware Spectral Reconstruction From RGB
Authors: Bo Sun, Junchi Yan, Xiao Zhou, Yinqiang Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager. It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. To improve accuracy, data-driven algorithms have been proposed to retrieve the best response curves of existing RGB cameras, or even to design brand new three-channel response curves. Instead, this paper explores the filter-array based color imaging mechanism of existing RGB cameras, and proposes to design the IR-cut filter properly for improved spectral recovery, which stands out as an in-between solution with better trade-off between reconstruction accuracy and implementation complexity. We further propose a deep learning based spectral reconstruction method, which allows to recover the illumination spectrum as well. Experiment results with both synthetic and real images under daylight illumination have shown the benefits of our IR-cut filter tuning method and our illumination-aware spectral reconstruction method.