mtybilly/Instruct2DS huggingface dataset:
RankDetNet: Delving Into Ranking Constraints for Object Detection
Authors: Ji Liu, Dong Li, Rongzhang Zheng, Lu Tian, Yi Shan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Modern object detection approaches cast detecting objects as optimizing two subtasks of classification and localization simultaneously. Existing methods often learn the classification task by optimizing each proposal separately and neglect the relationship among different proposals. Such detection paradigm also encounters the mismatch between classification and localization due to the inherent discrepancy of their optimization targets. In this work, we propose a ranking-based optimization algorithm for harmoniously learning to rank and localize proposals in lieu of the classification task. To this end, we comprehensively investigate three types of ranking constraints, i.e., global ranking, class-specific ranking and IoU-guided ranking losses. The global ranking loss encourages foreground samples to rank higher than background. The class-specific ranking loss ensures that positive samples rank higher than negative ones for each specific class. The IoU-guided ranking loss aims to align each pair of confidence scores with the associated pair of IoU overlap between two positive samples of a specific class. Our ranking constraints can sufficiently explore the relationships between samples from three different perspectives. They are easy-to-implement, compatible with mainstream detection frameworks and computation-free for inference. Experiments demonstrate that our RankDetNet consistently surpasses prior anchor-based and anchor-free baselines, e.g., improving RetinaNet baseline by 2.5% AP on the COCO test-dev set without bells and whistles. We also apply the proposed ranking constraints for 3D object detection and achieve improved performance, which further validates the superiority and generality of our method.
Back to the Feature: Learning Robust Camera Localization From Pixels To Pose
Authors: Paul-Edouard Sarlin, Ajaykumar Unagar, Mans Larsson, Hugo Germain, Carl Toft, Viktor Larsson, Marc Pollefeys, Vincent Lepetit, Lars Hammarstrand, Fredrik Kahl, Torsten Sattler
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms. We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. Our approach is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching by jointly refining keypoints and poses with little overhead. The code will be publicly available at github.com/cvg/pixloc.
Learning Parallel Dense Correspondence From Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction
Authors: Jiapeng Tang, Dan Xu, Kui Jia, Lei Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds. Despite the recent success achieved by extending deep implicit representations into 4D space, it is still a great challenge in two respects, i.e. how to design a flexible framework for learning robust spatio-temporal shape representations from 4D point clouds, and develop an efficient mechanism for capturing shape dynamics. In this work, we present a novel pipeline to learn a temporal evolution of the 3D human shape through spatially continuous transformation functions among cross-frame occupancy fields. The key idea is to parallelly establish the dense correspondence between predicted occupancy fields at different time steps via explicitly learning continuous displacement vector fields from robust spatio-temporal shape representations. Extensive comparisons against previous state-of-the-arts show the superior accuracy of our approach for 4D human reconstruction in the problems of 4D shape auto-encoding and completion, and a much faster network inference with about 8 times speedup demonstrates the significant efficiency of our approach.
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
Authors: Aditya Prakash, Kashyap Chitta, Andreas Geiger
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. However, for the actual driving task, the global context of the 3D scene is key, e.g. a change in traffic light state can affect the behavior of a vehicle geometrically distant from that traffic light. Geometry alone may therefore be insufficient for effectively fusing representations in end-to-end driving models. In this work, we demonstrate that imitation learning policies based on existing sensor fusion methods under-perform in the presence of a high density of dynamic agents and complex scenarios, which require global contextual reasoning, such as handling traffic oncoming from multiple directions at uncontrolled intersections. Therefore, we propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. We experimentally validate the efficacy of our approach in urban settings involving complex scenarios using the CARLA urban driving simulator. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
Authors: Bin Yan, Houwen Peng, Kan Wu, Dong Wang, Jianlong Fu, Huchuan Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployments in resource-constrained applications. In this work, we present LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers. Comprehensive experiments show that our LightTrack is effective. It can find trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ and Ocean, while using much fewer model Flops and parameters. Moreover, when deployed on resource-constrained mobile chipsets, the discovered trackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack runs 12x faster than Ocean, while using 13x fewer parameters and 38x fewer Flops. Such improvements might narrow the gap between academic models and industrial deployments in object tracking task. LightTrack is released at here.
Unsupervised Disentanglement of Linear-Encoded Facial Semantics
Authors: Yutong Zheng, Yu-Kai Huang, Ran Tao, Zhiqiang Shen, Marios Savvides
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized 3D deformable facial reconstruction method to decompose single-view GAN generations into multiple semantics. Latent representations are then extracted to capture interpretable facial semantics. In this work, we make it possible to get rid of labels for disentangling meaningful facial semantics. Also, we demonstrate that the guided extrapolation along the disentangled representations can help with data augmentation, which sheds light on handling unbalanced data. Finally, we provide an analysis of our learned localized facial representations and illustrate that the semantic information is encoded, which surprisingly complies with human intuition. The overall unsupervised design brings more flexibility to representation learning in the wild.
Learning Position and Target Consistency for Memory-Based Video Object Segmentation
Authors: Li Hu, Peng Zhang, Bang Zhang, Pan Pan, Yinghui Xu, Rong Jin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to learn position and target consistency framework for memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task.
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
Authors: Pan Zhang, Bo Zhang, Ting Zhang, Dong Chen, Yong Wang, Fang Wen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods.
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
Authors: Zhihao Xia, Michael Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce a neural network-based method to denoise pairs of images taken in quick succession in low-light environments, with and without a flash. Our goal is to produce a high-quality rendering of the scene that preserves the color and mood from the ambient illumination of the noisy no-flash image, while recovering surface texture and detail revealed by the flash. Our network outputs a gain map and a field of kernels, the latter obtained by linearly mixing elements of a per-image low-rank kernel basis. We first apply the kernel field to the no-flash image, and then multiply the result with the gain map to create the final output. We show our network effectively learns to produce high-quality images by combining a smoothed out estimate of the scene's ambient appearance from the no-flash image, with high-frequency albedo details extracted from the flash input. Our experiments show significant improvements over alternative captures without a flash, and baseline denoisers that use flash no-flash pairs. In particular, our method produces images that are both noise-free and contain accurate ambient colors without the sharp shadows or strong specular highlights visible in the flash image.
Transformer Interpretability Beyond Attention Visualization
Authors: Hila Chefer, Shir Gur, Lior Wolf
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention maps or employ heuristic propagation along the attention graph. In this work, we propose a novel way to compute relevancy for Transformer networks. The method assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This propagation involves attention layers and skip connections, which challenge existing methods. Our solution is based on a specific formulation that is shown to maintain the total relevancy across layers. We benchmark our method on very recent visual Transformer networks, as well as on a text classification problem, and demonstrate a clear advantage over the existing explainability methods.
Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach
Authors: Giang Truong, Huu Le, David Suter, Erchuan Zhang, Syed Zulqarnain Gilani
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for highly contaminated datasets is, however, still challenging due to its underlying computational complexity. Recent attention has been focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems.
Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training
Authors: Yunxuan Wei, Shuhang Gu, Yawei Li, Radu Timofte, Longcun Jin, Hengjie Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: These days, unsupervised super-resolution (SR) is soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i.e. first generating synthetic low-resolution (LR) images Y^g corresponding to real-world high-resolution (HR) images X^r in the real-world LR domain Y^r, and then utilizing the pseudo pairs Y^g, X^r for training in a supervised manner. Unfortunately, since image translation itself is an extremely challenging task, the SR performance of these approaches is severely limited by the domain gap between generated synthetic LR images and real LR images. In this paper, we propose a novel domain-distance aware super-resolution (DASR) approach for unsupervised real-world image SR. The domain gap between training data (e.g. Y^g) and testing data (e.g. Y^r) is addressed with our domain-gap aware training and domain-distance weighted supervision strategies. Domain-gap aware training takes additional benefit from real data in the target domain while domain-distance weighted supervision brings forward the more rational use of labeled source domain data. The proposed method is validated on synthetic and real datasets and the experimental results show that DASR consistently outperforms state-of-the-art unsupervised SR approaches in generating SR outputs with more realistic and natural textures. Codes are available at https://github.com/ShuhangGu/DASR.
Learning to Track Instances without Video Annotations
Authors: Yang Fu, Sifei Liu, Umar Iqbal, Shalini De Mello, Humphrey Shi, Jan Kautz
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semi-supervised framework by learning instance tracking networks with only a labeled image dataset and unlabeled video sequences. With an instance contrastive objective, we learn an embedding to discriminate each instance from the others. We show that even when only trained with images, the learned feature representation is robust to instance appearance variations, and is thus able to track objects steadily across frames. We further enhance the tracking capability of the embedding by learning correspondence from unlabeled videos in a self-supervised manner. In addition, we integrate this module into single-stage instance segmentation and pose estimation frameworks, which significantly reduce the computational complexity of tracking compared to two-stage networks. We conduct experiments on the YouTube-VIS and PoseTrack datasets. Without any video annotation efforts, our proposed method can achieve comparable or even better performance than most fully-supervised methods.
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination
Authors: Xudong Wang, Ziwei Liu, Stella X. Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced datasets. However, natural data could be highly correlated and long-tail distributed. Natural between-instance similarity conflicts with the presumed instance distinction, causing unstable training and poor performance. Our idea is to discover and integrate between-instance similarity into contrastive learning, not directly by instance grouping, but by cross-level discrimination (CLD) between instances and local instance groups. While invariant mapping of each instance is imposed by attraction within its augmented views, between-instance similarity emerges from common repulsion against instance groups. Our batch-wise and cross-view comparisons also greatly improve the positive/negative sample ratio of contrastive learning and achieve better invariant mapping. To effect both grouping and discrimination objectives, we impose them on features separately derived from a shared representation. In addition, we propose normalized projection heads and unsupervised hyper-parameter tuning for the first time. Our extensive experimentation demonstrates that CLD is a lean and powerful add-on to existing methods (e.g., NPID, MoCo, InfoMin, BYOL) on highly correlated, long-tail, or balanced datasets. It not only achieves new state-of-the-art on self-supervision, semi-supervision, and transfer learning benchmarks, but also beats MoCo v2 and SimCLR on every reported performance attained with a much larger compute. CLD effectively extends unsupervised learning to natural data and brings it closer to real-world applications.
Representation Learning via Global Temporal Alignment and Cycle-Consistency
Authors: Isma Hadji, Konstantinos G. Derpanis, Allan D. Jepson
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network. Our loss is based on a novel probabilistic path finding view of dynamic time warping (DTW) that contains the following three key features: (i) the local path routing decisions are contrastive and differentiable, (ii) pairwise distances are cast as probabilities that are contrastive as well, and (iii) our formulation naturally admits a global cycle consistency loss that verifies correspondences. For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization. We report significant performance increases over previous methods. In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval.
Personalized Outfit Recommendation With Learnable Anchors
Authors: Zhi Lu, Yang Hu, Yan Chen, Bing Zeng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The multimedia community has recently seen a tremendous surge of interest in the fashion recommendation problem. A lot of efforts have been made to model the compatibility between fashion items. Some have also studied users' personal preferences for the outfits. There is, however, another difficulty in the task that hasn't been dealt with carefully by previous work. Users that are new to the system usually only have several (less than 5) outfits available for learning. With such a limited number of training examples, it is challenging to model the user's preferences reliably. In this work, we propose a new solution for personalized outfit recommendation that is capable of handling this case. We use a stacked self-attention mechanism to model the high-order interactions among the items. We then embed the items in an outfit into a single compact representation within the outfit space. To accommodate the variety of users' preferences, we characterize each user with a set of anchors, i.e. a group of learnable latent vectors in the outfit space that are the representatives of the outfits the user likes. We also learn a set of general anchors to model the general preference shared by all users. Based on this representation of the outfits and the users, we propose a simple but effective strategy for the new user profiling tasks. Extensive experiments on large scale real-world datasets demonstrate the performance of our proposed method.
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
Authors: Zhizhong Huang, Junping Zhang, Hongming Shan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age groups into the same age group, called face age synthesis (FAS); however, the former lacks visual results for model interpretation while the latter suffers from artifacts compromising downstream recognition. Therefore, this paper proposes a unified, multi-task framework to jointly handle these two tasks, termed MTLFace, which can learn age-invariant identity-related representation while achieving pleasing face synthesis. Specifically, we first decompose the mixed face features into two uncorrelated components---identity- and age-related features---through an attention mechanism, and then decorrelate these two components using multi-task training and continuous domain adaption. In contrast to the conventional one-hot encoding that achieves group-level FAS, we propose a novel identity conditional module to achieve identity-level FAS, with a weight-sharing strategy to improve the age smoothness of synthesized faces. In addition, we collect and release a large cross-age face dataset with age and gender annotations to advance AIFR and FAS. Extensive experiments on five benchmark cross-age datasets demonstrate the superior performance of our proposed MTLFace over state-of-the-art methods for AIFR and FAS. We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild. The source code and dataset are available at https://github.com/Hzzone/MTLFace.
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
Authors: Yiding Yang, Zhou Ren, Haoxiang Li, Chunluan Zhou, Xinchao Wang, Gang Hua
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only then implementing data association and refinement. Despite the promising results achieved, such a strategy is inevitably prone to missed detections especially in heavily-cluttered scenes, since this tracking-by-detection paradigm is, by nature, largely dependent on visual evidences that are absent in the case of occlusion. In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion. Specifically, we derive this prediction of dynamics through a graph neural network (GNN) that explicitly accounts for both spatial-temporal and visual information. It takes as input the historical pose tracklets and directly predicts the corresponding poses in the following frame for each tracklet. The predicted poses will then be aggregated with the detected poses, if any, at the same frame so as to produce the final pose, potentially recovering the occluded joints missed by the estimator. Experiments on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed method achieves results superior to the state of the art on both human pose estimation and tracking tasks.
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation
Authors: Yahui Liu, Enver Sangineto, Yajing Chen, Linchao Bao, Haoxian Zhang, Nicu Sebe, Bruno Lepri, Wei Wang, Marco De Nadai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations.
Robust Instance Segmentation Through Reasoning About Multi-Object Occlusion
Authors: Xiaoding Yuan, Adam Kortylewski, Yihong Sun, Alan Yuille
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, we propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders and to classify objects based on their non-occluded parts. We extend their generative model to include multiple objects and introduce a framework for efficient inference in challenging occlusion scenarios. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates erroneous segmentations and estimates the occlusion order to correct them. The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification. Our experiments on the KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the effectiveness and robustness of our model at multi-object instance segmentation under occlusion. Code is publically available at https://github.com/XD7479/Multi-Object-Occlusion.
Architectural Adversarial Robustness: The Case for Deep Pursuit
Authors: George Cazenavette, Calvin Murdock, Simon Lucey
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by reframing each layer of a feed-forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deeper, real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.
Multi-Scale Aligned Distillation for Low-Resolution Detection
Authors: Lu Qi, Jason Kuen, Jiuxiang Gu, Zhe Lin, Yi Wang, Yukang Chen, Yanwei Li, Jiaya Jia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In instance-level detection tasks (e.g., object detection), reducing input resolution is an easy option to improve runtime efficiency. However, this option severely hurts the detection performance. This paper focuses on boosting the performance of a low-resolution model, by distilling knowledge from a high/multi-resolution model. We first identify the challenge of applying knowledge distillation to teacher and student networks that act on different input resolutions. To tackle the challenge, we explore the idea of spatially aligning feature maps between models of different input resolutions, by shifting the position of the feature pyramid structure. With the alignment idea, we introduce aligned multi-scale training to train a multi-scale teacher that can distill its knowledge seamlessly to a low-resolution student. Furthermore, we propose cross feature-level fusion to dynamically fuse the multi-resolution features of the same teacher, to better guide the student. On several instance-level detection tasks and datasets, the low-resolution models trained via our approach perform competitively with high-resolution models trained via conventional multi-scale training, while outperforming the latter's low-resolution models by 2.1% to 3.6% in mAP.
Deep Active Surface Models
Authors: Udaranga Wickramasinghe, Pascal Fua, Graham Knott
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Active Surface Models have a long history of being useful to model complex 3D surfaces. But only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them. In this paper, we advocate a much tighter integration. We introduce layers that implement them that can be integrated seamlessly into Graph Convolutional Networks to enforce sophisticated smoothness priors at an acceptable computational cost. We will show that the resulting Deep Active Surface Models outperform equivalent architectures that use traditional regularization loss terms to impose smoothness priors for 3D surface reconstruction from 2D images and for 3D volume segmentation.
Can We Characterize Tasks Without Labels or Features?
Authors: Bram Wallace, Ziyang Wu, Bharath Hariharan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task. Methods, however, generally depend on two separate assumptions: the presence of labeled images and access to powerful "probe" networks that yield useful features. In this work, we demonstrate the current reliance on both of these aspects and develop algorithms to operate when either of these assumptions fail. In the unlabeled case, we show that pseudolabels from the probe network provide discriminative enough gradients to perform nearly-equal task selection even when the probe network is trained on imagery unrelated to the tasks. To compute the embedding with no probe network at all, we introduce the Task Tangent Kernel (TTK) which uses a kernelized distance across multiple random networks to achieve performance over double that of other methods with randomly initialized models. Code is available at https://github.com/BramSW/task_characterization_cvpr_2021/.
Scene Essence
Authors: Jiayan Qiu, Yiding Yang, Xinchao Wang, Dacheng Tao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an end-to-end learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the scene. To this end, we devise a novel approach that learns to partition the scene objects into two groups, essential ones and minor ones, under the supervision that if only the essential ones are kept while the minor ones are erased in the input image, a scene recognizer would preserve its original prediction. Specifically, we introduce a learnable graph neural network (GNN) for labelling scene objects, based on which the minor ones are wiped off by an off-the-shelf image inpainter. The features of the inpainted image derived in this way, together with those learned from the GNN with the minor-object nodes pruned, are expected to fool the scene discriminator. Both subjective and objective evaluations on Places365, SUN397, and MIT67 datasets demonstrate that, the learned Scene Essence yields a visually plausible image that convincingly retains the original scene category.
Visual Room Rearrangement
Authors: Luca Weihs, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within completely unseen environments. In this paper, we propose a new dataset and baseline models for the task of Rearrangement. We particularly focus on the task of Room Rearrangement: an agent begins by exploring a room and recording objects' initial configurations. We then remove the agent and change the poses and states (e.g., open/closed) of some objects in the room. The agent must restore the initial configurations of all objects in the room. Our dataset, named RoomR, includes 6,000 distinct rearrangement settings involving 72 different object types in 120 scenes. Our experiments show that solving this challenging interactive task that involves navigation and object interaction is beyond the capabilities of the current state-of-the-art techniques for embodied tasks and we are still very far from achieving perfect performance on these types of tasks.
VDSM: Unsupervised Video Disentanglement With State-Space Modeling and Deep Mixtures of Experts
Authors: Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. Unfortunately, disentanglement has been shown to be impossible without the incorporation of supervision or inductive bias. Given that supervision is often expensive or infeasible to acquire, we choose to incorporate structural inductive bias and present an unsupervised, deep State-Space-Model for Video Disentanglement (VDSM). The model disentangles latent time-varying and dynamic factors via the incorporation of hierarchical structure with a dynamic prior and a Mixture of Experts decoder. VDSM learns separate disentangled representations for the identity of the object or person in the video, and for the action being performed. We evaluate VDSM across a range of qualitative and quantitative tasks including identity and dynamics transfer, sequence generation, Frechet Inception Distance, and factor classification. VDSM achieves state-of-the-art performance and exceeds adversarial methods, even when the methods use additional supervision.
Rotation-Only Bundle Adjustment
Authors: Seong Hun Lee, Javier Civera
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure. When two calibrated cameras observe five or more of the same points, their relative rotation can be recovered independently of the translation. We extend this idea to multiple views, thereby decoupling the rotation estimation from the translation and structure estimation. Our approach provides several benefits such as complete immunity to inaccurate translations and structure, and the accuracy improvement when used with rotation averaging. We perform extensive evaluations on both synthetic and real datasets, demonstrating consistent and significant gains in accuracy when used with the state-of-the-art rotation averaging method.
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations
Authors: Wolfgang Stammer, Patrick Schramowski, Kristian Kersting
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space. These "visual" explanations are often insufficient, as the model's actual concept remains elusive. Moreover, without insights into the model's semantic concept, it is difficult --if not impossible-- to intervene on the model's behavior via its explanations, called Explanatory Interactive Learning. Consequently, we propose to intervene on a Neuro-Symbolic scene representation, which allows one to revise the model on the semantic level, e.g. "never focus on the color to make your decision". We compiled a novel con-founded visual scene data set, the CLEVR-Hans data set,capturing complex compositions of different objects. The results of our experiments on CLEVR-Hans demonstrate that our semantic explanations, i.e. compositional explanations at a per-object level, can identify confounders that are not identifiable using "visual" explanations only. More importantly, feedback on this semantic level makes it possible to revise the model from focusing on these factors.
Polygonal Point Set Tracking
Authors: Gunhee Nam, Miran Heo, Seoung Wug Oh, Joon-Young Lee, Seon Joo 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 novel learning-based polygonal point set tracking method. Compared to existing video object segmentation (VOS) methods that propagate pixel-wise object mask information, we propagate a polygonal point set over frames. Specifically, the set is defined as a subset of points in the target contour, and our goal is to track corresponding points on the target contour. Those outputs enable us to apply various visual effects such as motion tracking, part deformation, and texture mapping. To this end, we propose a new method to track the corresponding points between frames by the global-local alignment with delicately designed losses and regularization terms. We also introduce a novel learning strategy using synthetic and VOS datasets that makes it possible to tackle the problem without developing the point correspondence dataset. Since the existing datasets are not suitable to validate our method, we build a new polygonal point set tracking dataset and demonstrate the superior performance of our method over the baselines and existing contour-based VOS methods. In addition, we present visual-effects applications of our method on part distortion and text mapping.
Deformed Implicit Field: Modeling 3D Shapes With Learned Dense Correspondence
Authors: Yu Deng, Jiaolong Yang, Xin Tong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for each shape instance. Shape correspondences can be easily established using their deformation fields. Our neural network, dubbed DIF-Net, jointly learns a shape latent space and these fields for 3D objects belonging to a category without using any correspondence or part label. The learned DIF-Net can also provides reliable correspondence uncertainty measurement reflecting shape structure discrepancy. Experiments show that DIF-Net not only produces high-fidelity 3D shapes but also builds high-quality dense correspondences across different shapes. We also demonstrate several applications such as texture transfer and shape editing, where our method achieves compelling results that cannot be achieved by previous methods.
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing
Authors: Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Panyue Chen, Ping Zhao, Quanshi Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different network architectures. We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency. We conduct comparative studies based on such metrics to verify the hypotheses. We further use the verified hypotheses to revise architectures of existing DNNs and improve their utilities. Experiments demonstrate the effectiveness of our method. The code will be released when this paper is accepted.
Tracking Pedestrian Heads in Dense Crowd
Authors: Ramana Sundararaman, Cedric De Almeida Braga, Eric Marchand, Julien Pettre
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (Main Conference Track), 2021

Abstract: Tracking humans in crowded video sequences is an important constituent of visual scene understanding. Increasing crowd density challenges visibility of humans, limiting the scalability of existing pedestrian trackers to higher crowd densities. For that reason, we propose to revitalize head tracking with Crowd of Heads Dataset (CroHD), consisting of 9 sequences of 11,463 frames with over 2,276,838 heads and 5,230 tracks annotated in diverse scenes. For evaluation, we proposed a new metric, IDEucl, to measure an algorithm's efficacy in preserving a unique identity for the longest stretch in image coordinate space, thus building a correspondence between pedestrian crowd motion and the performance of a tracking algorithm. Moreover, we also propose a new head detector, HeadHunter, which is designed for small head detection in crowded scenes. We extend HeadHunter with a Particle Filter and a color histogram based re-identification module for head tracking. To establish this as a strong baseline, we compare our tracker with existing state-of-the-art pedestrian trackers on CroHD and demonstrate superiority, especially in identity preserving tracking metrics. With a light-weight head detector and a tracker which is efficient at identity preservation, we believe our contributions will serve useful in advancement of pedestrian tracking in dense crowds. We make our dataset, code and models publicly available at https://project.inria.fr/crowdscience/project/dense-crowd-head-tracking/.