mtybilly/Instruct2DS huggingface dataset:
Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation
Authors: Zhenbo Yu, Bingbing Ni, Jingwei Xu, Junjie Wang, Chenglong Zhao, Wenjun Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In this work, we study the ambiguity problem in the task of unsupervised 3D human pose estimation from 2D counterpart. On one hand, without explicit annotation, the scale of 3D pose is difficult to be accurately captured (scale ambiguity). On the other hand, one 2D pose might correspond to multiple 3D gestures, where the lifting procedure is inherently ambiguous (pose ambiguity). Previous methods generally use temporal constraints (e.g., constant bone length and motion smoothness) to alleviate the above issues. However, these methods commonly enforce the outputs to fulfill multiple training objectives simultaneously, which often lead to sub-optimal results. In contrast to the majority of previous works, we propose to split the whole problem into two sub-tasks, i.e., optimizing 2D input poses via a scale estimation module and then mapping optimized 2D pose to 3D counterpart via a pose lifting module. Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively. These two modules are optimized via a iterative training scheme with corresponding temporal constraints, which effectively reduce the learning difficulty and lead to better performance. Results on the Human3.6M dataset demonstrate that our approach improves upon the prior art by 23.1% and also outperforms several weakly supervised approaches that rely on 3D annotations. Our project is available at https://sites.google.com/view/ambiguity-aware-hpe.
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition
Authors: Rasha Friji, Hassen Drira, Faten Chaieb, Hamza Kchok, Sebastian Kurtek
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Deep Learning architectures, albeit successful in mostcomputer vision tasks, were designed for data with an un-derlying Euclidean structure, which is not usually fulfilledsince pre-processed data may lie on a non-linear space.In this paper, we propose a geometry aware deep learn-ing approach using rigid and non rigid transformation opti-mization for skeleton-based action recognition. Skeleton se-quences are first modeled as trajectories on Kendall's shapespace and then mapped to the linear tangent space. The re-sulting structured data are then fed to a deep learning archi-tecture, which includes a layer that optimizes over rigid andnon rigid transformations of the 3D skeletons, followed bya CNN-LSTM network. The assessment on two large scaleskeleton datasets, namely NTU-RGB+D and NTU-RGB+D120, has proven that the proposed approach outperformsexisting geometric deep learning methods and exceeds re-cently published approaches with respect to the majority of configurations.
Enhanced Boundary Learning for Glass-Like Object Segmentation
Authors: Hao He, Xiangtai Li, Guangliang Cheng, Jianping Shi, Yunhai Tong, Gaofeng Meng, Véronique Prinet, LuBin Weng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models will be available for further research.
Self-Supervised Pretraining of 3D Features on Any Point-Cloud
Authors: Zaiwei Zhang, Rohit Girdhar, Armand Joulin, Ishan Misra
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like image recognition, video understanding etc. However, pretraining is not widely used for 3D recognition tasks where state-of-the-art methods train models from scratch. A primary reason is the lack of large annotated datasets because 3D data labelling is time-consuming. Recent work shows that self-supervised learning is useful to pretrain models in 3D but requires multi-view data and point correspondences. We present a simple self-supervised pretraining method that can work with single-view depth scans acquired by varied sensors, without 3D registration and point correspondences. We pretrain standard point cloud and voxel based model architectures, and show that joint pretraining further improves performance. We evaluate our models on 9 benchmarks for object detection, semantic segmentation, and object classification, where they achieve state-of-the-art results. Most notably, we set a new state-of-the-art for object detection on ScanNet (69.0% mAP) and SUNRGBD (63.5% mAP). Our pretrained models are label efficient and improve performance for classes with few examples.
N-ImageNet: Towards Robust, Fine-Grained Object Recognition With Event Cameras
Authors: Junho Kim, Jaehyeok Bae, Gangin Park, Dongsu Zhang, Young Min Kim
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object recognition with event cameras. The dataset is collected using programmable hardware in which an event camera consistently moves around a monitor displaying images from ImageNet. N-ImageNet serves as a challenging benchmark for event-based object recognition, due to its large number of classes and samples. We empirically show that pretraining on N-ImageNet improves the performance of event-based classifiers and helps them learn with few labeled data. In addition, we present several variants of N-ImageNet to test the robustness of event-based classifiers under diverse camera trajectories and severe lighting conditions, and propose a novel event representation to alleviate the performance degradation. To the best of our knowledge, we are the first to quantitatively investigate the consequences caused by various environmental conditions on event-based object recognition algorithms. N-ImageNet and its variants are expected to guide practical implementations for deploying event-based object recognition algorithms in the real world.
Diagonal Attention and Style-Based GAN for Content-Style Disentanglement in Image Generation and Translation
Authors: Gihyun Kwon, Jong Chul Ye
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content feature vectors from random noises, the resulting spatial content control is primarily intended for minor spatial variations, and the disentanglement of global content and styles is by no means complete. Inspired by a mathematical understanding of normalization and attention, here we present a novel hierarchical adaptive Diagonal spatial ATtention (DAT) layers to separately manipulate the spatial contents from styles in a hierarchical manner. Using DAT and AdaIN, our method enables coarse-to-fine level disentanglement of spatial contents and styles. In addition, our generator can be easily integrated into the GAN inversion framework so that the content and style of translated images from multi-domain image translation tasks can be flexibly controlled. By using various datasets, we confirm that the proposed method not only outperforms the existing models in disentanglement scores, but also provides more flexible control over spatial features in the generated images.
Who's Waldo? Linking People Across Text and Images
Authors: Yuqing Cui, Apoorv Khandelwal, Yoav Artzi, Noah Snavely, Hadar Averbuch-Elor
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast to prior work in visual grounding, which is predominantly object-based, our new task masks out the names of people in captions in order to encourage methods trained on such image--caption pairs to focus on contextual cues (such as rich interactions between multiple people), rather than learning associations between names and appearances. To facilitate this task, we introduce a new dataset, Who's Waldo, mined automatically from image--caption data on Wikimedia Commons. We propose a Transformer-based method that outperforms several strong baselines on this task, and are releasing our data to the research community to spur work on contextual models that consider both vision and language.
Switchable K-Class Hyperplanes for Noise-Robust Representation Learning
Authors: Boxiao Liu, Guanglu Song, Manyuan Zhang, Haihang You, Yu Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Optimizing the K-class hyperplanes in the latent space has become the standard paradigm for efficient representation learning. However, it's almost impossible to find an optimal K-class hyperplane to accurately describe the latent space of massive noisy data. For this potential problem, we constructively propose a new method, named Switchable K-class Hyperplanes (SKH), to sufficiently describe the latent space by the mixture of K-class hyperplanes. It can directly replace the conventional single K-class hyperplane optimization as the new paradigm for noise-robust representation learning. When collaborated with the popular ArcFace on million-level data representation learning, we found that the switchable manner in SKH can effectively eliminate the gradient conflict generated by real-world label noise on a single K-class hyperplane. Moreover, combined with the margin-based loss functions (e.g. ArcFace), we propose a simple Posterior Data Clean strategy to reduce the model optimization deviation on clean dataset caused by the reduction of valid categories in each K-class hyperplane. Extensive experiments demonstrate that the proposed SKH easily achieves new state-of-the-art on IJB-B and IJB-C by encouraging noise-robust representation learning.
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
Authors: Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation. Initially designed for natural language processing tasks, Transformers have emerged as alternative architectures with innate global self-attention mechanisms to capture long-range dependencies. In this paper, we propose TransDepth, an architecture that benefits from both convolutional neural networks and transformers. To avoid the network losing its ability to capture local-level details due to the adoption of transformers, we propose a novel decoder that employs attention mechanisms based on gates. Notably, this is the first paper that applies transformers to pixel-wise prediction problems involving continuous labels (i.e., monocular depth prediction and surface normal estimation). Extensive experiments demonstrate that the proposed TransDepth achieves state-of-the-art performance on three challenging datasets. Our code is available at: https://github.com/ygjwd12345/TransDepth.
BlockPlanner: City Block Generation With Vectorized Graph Representation
Authors: Linning Xu, Yuanbo Xiangli, Anyi Rao, Nanxuan Zhao, Bo Dai, Ziwei Liu, Dahua Lin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: City modeling is the foundation for computational urban planning, navigation, and entertainment. In this work, we present the first generative model of city blocks named BlockPlanner, and showcase its ability to synthesize valid city blocks with varying land lots configurations. We propose a novel vectorized city block representation utilizing a ring topology and a two-tier graph to capture the global and local structures of a city block. Each land lot is abstracted into a vector representation covering both its 3D geometry and land use semantics. Such vectorized representation enables us to deploy a lightweight network to capture the underlying distribution of land lots configuration in a city block. To enforce intrinsic spatial constraints of a valid city block, a set of effective loss functions are imposed to shape rational results. We contribute a pilot city block dataset to demonstrate the effectiveness and efficiency of our representation and framework over the state-of-the-art. Notably, our BlockPlanner is also able to edit and manipulate city blocks, enabling several useful applications, e.g., topology refinement and footprint generation.
PCAM: Product of Cross-Attention Matrices for Rigid Registration of Point Clouds
Authors: Anh-Quan Cao, Gilles Puy, Alexandre Boulch, Renaud Marlet
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Rigid registration of point clouds with partial overlaps is a longstanding problem usually solved in two steps: (a) finding correspondences between the point clouds; (b) filtering these correspondences to keep only the most reliable ones to estimate the transformation. Recently, several deep nets have been proposed to solve these steps jointly. We built upon these works and propose PCAM: a neural network whose key element is a pointwise product of cross-attention matrices that permits to mix both low-level geometric and high-level contextual information to find point correspondences. These cross-attention matrices also permits the exchange of context information between the point clouds, at each layer, allowing the network construct better matching features within the overlapping regions. The experiments show that PCAM achieves state-of-the-art results among methods which, like us, solve steps (a) and (b) jointly via deepnets.
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis
Authors: Yi Zhou, Lei Huang, Tao Zhou, Ling Shao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: A medical imaging model is usually explored for the diagnosis of a single disease. However, with the expanding demand for multi-disease diagnosis in clinical applications, multi-function solutions need to be investigated. Previous works proposed to either exploit different disease labels to conduct transfer learning through fine-tuning, or transfer knowledge across different domains with similar diseases. However, these methods still cannot address the real clinical challenge - a multi-disease model is required but annotations for each disease are not always available. In this paper, we introduce the task of transferring knowledge from single-disease diagnosis (source domain) to enhance multi-disease diagnosis (target domain). A category-invariant cross-domain transfer (CCT) method is proposed to address this single-to-multiple extension. First, for domain-specific task learning, we present a confidence weighted pooling (CWP) to obtain coarse heatmaps for different disease categories. Then, conditioned on these heatmaps, category-invariant feature refinement (CIFR) blocks are proposed to better localize discriminative semantic regions related to the corresponding diseases. The category-invariant characteristic enables transferability from the source domain to the target domain. We validate our method in two popular areas: extending diabetic retinopathy to identifying multiple ocular diseases, and extending glioma identification to the diagnosis of other brain tumors.
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition
Authors: Lei Fan, Peixi Xiong, Wei Wei, Ying Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Intelligent agents with visual sensors are allowed to actively explore their observations for better recognition performance. This task is referred to as Active Recognition (AR). Currently, most methods toward AR are implemented under a fixed-category setting, which constrains their applicability in realistic scenarios that need to incrementally learn new classes without retraining from scratch. Further, collecting massive data for novel categories is expensive. To address this demand, in this paper, we propose a unified framework towards Few-sample Lifelong Active Recognition (FLAR), which aims at performing active recognition on progressively arising novel categories that only have few training samples. Three difficulties emerge with FLAR: the lifelong recognition policy learning, the knowledge preservation of old categories, and the lack of training samples. To this end, our approach integrates prototypes, a robust representation for limited training samples, into a reinforcement learning solution, which motivates the agent to move towards views resulting in more discriminative features. Catastrophic forgetting during lifelong learning is then alleviated with knowledge distillation. Extensive experiments across two datasets, respectively for object and scene recognition, demonstrate that even without large training samples, the proposed approach could learn to actively recognize novel categories in a class-incremental behavior.
VLGrammar: Grounded Grammar Induction of Vision and Language
Authors: Yining Hong, Qing Li, Song-Chun Zhu, Siyuan Huang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Cognitive grammar suggests that the acquisition of language grammar is grounded within visual structures. While grammar is an essential representation of natural language, it also exists ubiquitously in vision to represent the hierarchical part-whole structure. In this work, we study grounded grammar induction of vision and language in a joint learning framework. Specifically, we present VLGrammar, a method that uses compound probabilistic context-free grammars (compound PCFGs) to induce the language grammar and the image grammar simultaneously. We propose a novel contrastive learning framework to guide the joint learning of both modules. To provide a benchmark for the grounded grammar induction task, we collect a large-scale dataset, PartIt, which contains human-written sentences that describe part-level semantics for 3D objects. Experiments on the PartIt dataset show that VLGrammar outperforms all baselines in image grammar induction and language grammar induction. The learned VLGrammar naturally benefits related downstream tasks. Specifically, it improves the image unsupervised clustering accuracy by 30%, and performs well in image retrieval and text retrieval. Notably, the induced grammar shows superior generalizability by easily generalizing to unseen categories.
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Authors: Yinbo Chen, Zhuang Liu, Huijuan Xu, Trevor Darrell, Xiaolong Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to have a key advantage of making the training objective consistent with the testing objective. However, some recent works report that by training for whole-classification, i.e. classification on the whole label-set, it can get comparable or even better embedding than many meta-learning algorithms. The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear. In this paper, we explore a simple process: meta-learning over a whole-classification pre-trained model on its evaluation metric. We observe this simple method achieves competitive performance to state-of-the-art methods on standard benchmarks. Our further analysis shed some light on understanding the trade-offs between the meta-learning objective and the whole-classification objective in few-shot learning.
CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds
Authors: Eric-Tuan Lê, Minhyuk Sung, Duygu Ceylan, Radomir Mech, Tamy Boubekeur, Niloy J. Mitra
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Representing human-made objects as a collection of base primitives has a long history in computer vision and reverse engineering. In the case of high-resolution point cloud scans, the challenge is to be able to detect both large primitives as well as those explaining the detailed parts. While the classical RANSAC approach requires case-specific parameter tuning, state-of-the-art networks are limited by memory consumption of their backbone modules such as PointNet++, and hence fail to detect the fine-scale primitives. We present Cascaded Primitive Fitting Networks (CPFN) that relies on an adaptive patch sampling network to assemble detection results of global and local primitive detection networks. As a key enabler, we present a merging formulation that dynamically aggregates the primitives across global and local scales. Our evaluation demonstrates that CPFN improves the state-of-the-art SPFN performance by 13-14% on high-resolution point cloud datasets and specifically improves the detection of fine-scale primitives by 20-22%. Our code is available at: https://github.com/erictuanle/CPFN
PARTS: Unsupervised Segmentation With Slots, Attention and Independence Maximization
Authors: Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: From an early age, humans perceive the visual world as composed of coherent objects with distinctive properties such as shape, size, and color. There is great interest in building models that are able to learn similar structure, ideally in an unsupervised manner. Learning such structure from complex 3D scenes that include clutter, occlusions, interactions, and camera motion is still an open challenge. We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner. Our model (named PARTS) builds on recent approaches that utilize iterative amortized inference and transition dynamics for deep generative models. We achieve dramatic improvements in performance by introducing several novel contributions. We introduce a recurrent slot-attention like encoder which allows for top-down influence during inference. Unlike prior work, we eschew using an auto-regressive prior when modeling image sequences, and demonstrate that a fixed frame-independent prior is superior for the purpose of scene segmentation and representation learning. We demonstrate our model's success on three different video datasets (the popular benchmark CLEVRER; a simulated 3D Playroom environment; and a real-world Robotics Arm dataset). Finally, we analyze the contributions of the various model components and the representations learned by the model.
Fine-Grained Semantics-Aware Representation Enhancement for Self-Supervised Monocular Depth Estimation
Authors: Hyunyoung Jung, Eunhyeok Park, Sungjoo Yoo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Self-supervised monocular depth estimation has been widely studied, owing to its practical importance and recent promising improvements. However, most works suffer from limited supervision of photometric consistency, especially in weak texture regions and at object boundaries. To overcome this weakness, we propose novel ideas to improve self-supervised monocular depth estimation by leveraging cross-domain information, especially scene semantics. We focus on incorporating implicit semantic knowledge into geometric representation enhancement and suggest two ideas: a metric learning approach that exploits the semantics-guided local geometry to optimize intermediate depth representations and a novel feature fusion module that judiciously utilizes cross-modality between two heterogeneous feature representations. We comprehensively evaluate our methods on the KITTI dataset and demonstrate that our method outperforms state-of-the-art methods. The source code is available at https://github.com/hyBlue/FSRE-Depth.
Learning Signed Distance Field for Multi-View Surface Reconstruction
Authors: Jingyang Zhang, Yao Yao, Long Quan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Recent works on implicit neural representations have shown promising results for multi-view surface reconstruction. However, most approaches are limited to relatively simple geometries and usually require clean object masks for reconstructing complex and concave objects. In this work, we introduce a novel neural surface reconstruction framework that leverages the knowledge of stereo matching and feature consistency to optimize the implicit surface representation. More specifically, we apply a signed distance field (SDF) and a surface light field to represent the scene geometry and appearance respectively. The SDF is directly supervised by geometry from stereo matching, and is refined by optimizing the multi-view feature consistency and the fidelity of rendered images. Our method is able to improve the robustness of geometry estimation and support reconstruction of complex scene topologies. Extensive experiments have been conducted on DTU, EPFL and Tanks and Temples datasets. Compared to previous state-of-the-art methods, our method achieves better mesh reconstruction in wide open scenes without masks as input.
Pose Correction for Highly Accurate Visual Localization in Large-Scale Indoor Spaces
Authors: Janghun Hyeon, Joohyung Kim, Nakju Doh
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Indoor visual localization is significant for various applications such as autonomous robots, augmented reality, and mixed reality. Recent advances in visual localization have demonstrated their feasibility in large-scale indoor spaces through coarse-to-fine methods that typically employ three steps: image retrieval, pose estimation, and pose selection. However, further research is needed to improve the accuracy of large-scale indoor visual localization. We demonstrate that the limitations in the previous methods can be attributed to the sparsity of image positions in the database, which causes view-differences between a query and a retrieved image from the database. In this paper, to address this problem, we propose a novel module, named pose correction, that enables re-estimation of the pose with local feature matching in a similar view by reorganizing the local features. This module enhances the accuracy of the initially estimated pose and assigns more reliable ranks. Furthermore, the proposed method achieves a new state-of-the-art performance with an accuracy of more than 90% within 1.0m in the challenging indoor benchmark dataset InLoc for the first time.
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction
Authors: Zhian Liu, Yongwei Nie, Chengjiang Long, Qing Zhang, Guiqing Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In this paper, we propose HF2-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between video frame and optical flow, to predict the next frame given several previous frames. By CVAE, the quality of flow reconstruction essentially influences that of frame prediction. Therefore, poorly reconstructed optical flows of abnormal events further deteriorate the quality of the final predicted future frame, making the anomalies more detectable. Experimental results demonstrate the effectiveness of the proposed method. Code is available at https://github.com/LiUzHiAn/hf2vad.
PointBA: Towards Backdoor Attacks in 3D Point Cloud
Authors: Xinke Li, Zhirui Chen, Yue Zhao, Zekun Tong, Yabang Zhao, Andrew Lim, Joey Tianyi Zhou
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: 3D deep learning has been increasingly more popular for a variety of tasks including many safety-critical applications. However, recently several works raise the security issues of 3D deep models. Although most of them consider adversarial attacks, we identify that backdoor attack is indeed a more serious threat to 3D deep learning systems but remains unexplored. We present the backdoor attacks in 3D point cloud with a unified framework that exploits the unique properties of 3D data and networks. In particular, we design two attack approaches on point cloud: the poison-label backdoor attack (PointPBA) and the clean-label backdoor attack (PointCBA). The first one is straightforward and effective in practice, while the latter is more sophisticated assuming there are certain data inspections. The attack algorithms are mainly motivated and developed by 1) the recent discovery of 3D adversarial samples suggesting the vulnerability of deep models under spatial transformation; 2) the proposed feature disentanglement technique that manipulates the feature of the data through optimization methods and its potential to embed a new task. Extensive experiments show the efficacy of the PointPBA with over 95% success rate across various 3D datasets and models, and the more stealthy PointCBA with around 50% success rate. Our proposed backdoor attack in 3D point cloud is expected to perform as a baseline for improving the robustness of 3D deep models.
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering
Authors: Qingxing Cao, Wentao Wan, Keze Wang, Xiaodan Liang, Liang Lin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Generalization on out-of-distribution (OOD) test data is an essential but underexplored topic in visual question answering. Current state-of-the-art VQA models often exploit the biased correlation between data and labels, which results in a large performance drop when the test and training data have different distributions. Inspired by the fact that humans can recognize novel concepts by composing existed concepts and capsule network's ability of representing part-whole hierarchies, we propose to use capsules to represent parts and introduce "Linguistically Routing" to merge parts with human-prior hierarchies. Specifically, we first fuse visual features with a single question word as atomic parts. Then we introduce the "Linguistically Routing" to reweight the capsule connections between two layers such that: 1) the lower layer capsules can transfer their outputs to the most compatible higher capsules, and 2) two capsules can be merged if their corresponding words are merged in the question parse tree. The routing process maximizes the above unary and binary potentials across multiple layers and finally carves a tree structure inside the capsule network. We evaluate our proposed routing method on the CLEVR compositional generation test, the VQA-CP2 dataset and the VQAv2 dataset. The experimental results show that our proposed method can improve current VQA models on OOD split without losing performance on the in-domain test data.
Neural Articulated Radiance Field
Authors: Atsuhiro Noguchi, Xiao Sun, Stephen Lin, Tatsuya Harada
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present Neural Articulated Radiance Field (NARF), a novel deformable 3D representation for articulated objects learned from images. While recent advances in 3D implicit representation have made it possible to learn models of complex objects, learning pose-controllable representations of articulated objects remains a challenge, as current methods require 3D shape supervision and are unable to render appearance. In formulating an implicit representation of 3D articulated objects, our method considers only the rigid transformation of the most relevant object part in solving for the radiance field at each 3D location. In this way, the proposed method represents pose-dependent changes without significantly increasing the computational complexity. NARF is fully differentiable and can be trained from images with pose annotations. Moreover, through the use of an autoencoder, it can learn appearance variations over multiple instances of an object class. Experiments show that the proposed method is efficient and can generalize well to novel poses. The code is available for research purposes at https://github.com/nogu-atsu/NARF
Region Similarity Representation Learning
Authors: Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, Trevor Darrell
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present Region Similarity Representation Learning (ReSim), a new approach to self-supervised representation learning for localization-based tasks such as object detection and segmentation. While existing work has largely focused on learning global representations for an entire image, ReSim learns both regional representations for localization as well as semantic image-level representations. ReSim operates by sliding a fixed-sized window across the overlapping area between two views (e.g., image crops), aligning these areas with their corresponding convolutional feature map regions, and then maximizing the feature similarity across views. As a result, ReSim learns spatially and semantically consistent feature representation throughout the convolutional feature maps of a neural network. A shift or scale of an image region, e.g., a shift or scale of an object, has a corresponding change in the feature maps; this allows downstream tasks to leverage these representations for localization. Through object detection, instance segmentation, and dense pose estimation experiments, we illustrate how ReSim learns representations which significantly improve the localization and classification performance compared to a competitive MoCo-v2 baseline: +2:7 APbb75 VOC, +1:1 AP75 COCO, and +1:9 APmk Cityscapes. We will release our code and pre-trained models.
Learning of Visual Relations: The Devil Is in the Tails
Authors: Alakh Desai, Tz-Ying Wu, Subarna Tripathi, Nuno Vasconcelos
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Significant effort has been recently devoted to modeling visual relations. This has mostly addressed the design of architectures, typically by adding parameters and increasing model complexity. However, visual relation learning is a long-tailed problem, due to the combinatorial nature of joint reasoning about groups of objects. Increasing model complexity is, in general, ill-suited for long-tailed problems due to their tendency to overfit. In this paper, we explore an alternative hypothesis, denoted the Devil is in the Tails. Under this hypothesis, better performance is achieved by keeping the model simple but improving its ability to cope with long-tailed distributions. To test this hypothesis, we devise a new approach for training visual relationships models, which is inspired by state-of-the-art long-tailed recognition literature. This is based on an iterative decoupled training scheme, denoted Decoupled Training for Devil in the Tails (DT2). DT2 employs a novel sampling approach, Alternating Class-Balanced Sampling (ACBS), to capture the interplay between the long-tailed entity and predicate distributions of visual relations. Results show that, with an extremely simple architecture, DT2-ACBS significantly outperforms much more complex state-of-the-art methods on scene graph generation tasks. This suggests that the development of sophisticated models must be considered in tandem with the long-tailed nature of the problem.
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Authors: Ruihuang Li, Xu Jia, Jianzhong He, Shuaijun Chen, Qinghua Hu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain. We propose a novel approach named T-SVDNet to address the task of Multi-source Domain Adaptation (MDA), which is featured by incorporating Tensor Singular Value Decomposition (T-SVD) into a neural network's training pipeline. Overall, high-order correlations among multiple domains are fully explored so as to better bridge the domain gap in this work. Specifically, we impose Tensor-Low-Rank (TLR) constraint on the tensor obtained by stacking up a group of prototypical similarity matrices, aiming at capturing consistent data structure across different domains. Furthermore, to avoid negative transfer brought by noisy source data, we propose a novel uncertainty-aware weighting strategy to adaptively assign weights to different source domains and samples based on the result of uncertainty estimation. Extensive experiments conducted on public benchmarks demonstrate the superiority of our model in addressing the task of MDA compared to state-of-the-art methods.
BuildingNet: Learning To Label 3D Buildings
Authors: Pratheba Selvaraju, Mohamed Nabail, Marios Loizou, Maria Maslioukova, Melinos Averkiou, Andreas Andreou, Siddhartha Chaudhuri, Evangelos Kalogerakis
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, and (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives. To create our dataset, we used crowdsourcing combined with expert guidance, resulting in 513K annotated mesh primitives, grouped into 292K semantic part components across 2K building models. The dataset covers several building categories, such as houses, churches, skyscrapers, town halls, libraries, and castles. We include a benchmark for evaluating mesh and point cloud labeling. Buildings have more challenging structural complexity compared to objects in existing benchmarks (e.g., ShapeNet, PartNet), thus, we hope that our dataset can nurture the development of algorithms that are able to cope with such large-scale geometric data for both vision and graphics tasks e.g., 3D semantic segmentation, part-based generative models, correspondences, texturing, and analysis of point cloud data acquired from real-world buildings. Finally, we show that our mesh-based graph neural network significantly improves performance over several baselines for labeling 3D meshes. Our project page www.buildingnet.org includes our dataset and code.
Student Customized Knowledge Distillation: Bridging the Gap Between Student and Teacher
Authors: Yichen Zhu, Yi Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Knowledge distillation (KD) transfers the dark knowledge from cumbersome networks (teacher) to lightweight (student) networks and expects the student to achieve more promising performance than training without the teacher's knowledge. However, a counter-intuitive argument is that better teachers do not make better students due to the capacity mismatch. To this end, we present a novel adaptive knowledge distillation method to complement traditional approaches. The proposed method, named as Student Customized Knowledge Distillation (SCKD), examines the capacity mismatch between teacher and student from the perspective of gradient similarity. We formulate the knowledge distillation as a multi-task learning problem so that the teacher transfers knowledge to the student only if the student can benefit from learning such knowledge. We validate our methods on multiple datasets with various teacher-student configurations on image classification, object detection, and semantic segmentation.
A Machine Teaching Framework for Scalable Recognition
Authors: Pei Wang, Nuno Vasconcelos
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We consider the scalable recognition problem in the fine-grained expert domain where large-scale data collection is easy whereas annotation is difficult. Existing solutions are typically based on semi-supervised or self-supervised learning. We propose an alternative new framework, MEMORABLE, based on machine teaching and online crowdsourcing platforms. A small amount of data is first labeled by experts and then used to teach online annotators for the classes of interest, who finally label the entire dataset. Preliminary studies show that the accuracy of classifiers trained on the final dataset is a function of the accuracy of the student annotators. A new machine teaching algorithm, CMaxGrad, is then proposed to enhance this accuracy by introducing explanations in a state-of-the-art machine teaching algorithm. For this, CMaxGrad leverages counterfactual explanations, which take into account student predictions, thereby proving feedback that is student-specific, explicitly addresses the causes of student confusion, and adapts to the level of competence of the student. Experiments show that both MEMORABLE and CMaxGrad outperform existing solutions to their respective problems.
Divide and Conquer for Single-Frame Temporal Action Localization
Authors: Chen Ju, Peisen Zhao, Siheng Chen, Ya Zhang, Yanfeng Wang, Qi Tian
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the one-stage framework but couple the counting goal and the localization goal. This paper proposes a novel two-stage framework for the STAL task with the spirit of divide and conquer. The instance counting stage leverages the location supervision to determine the number of action instances and divide a whole video into multiple video clips, so that each video clip contains only one complete action instance; and the location estimation stage leverages the category supervision to localize the action instance in each video clip. To efficiently represent the action instance in each video clip, we introduce the proposal-based representation, and design a novel differentiable mask generator to enable the end-to-end training supervised by category labels. On THUMOS14, GTEA, and BEOID datasets, our method outperforms state-of-the-art methods by 3.5%, 2.7%, 4.8% mAP on average. And extensive experiments verify the effectiveness of our method.
Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning Scheme
Authors: Xi Yang, Wangmeng Xiang, Hui Zeng, Lei Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Video super-resolution (VSR) aims to improve the spatial resolution of low-resolution (LR) videos. Existing VSR methods are mostly trained and evaluated on synthetic datasets, where the LR videos are uniformly downsampled from their high-resolution (HR) counterparts by some simple operators (e.g., bicubic downsampling). Such simple synthetic degradation models, however, cannot well describe the complex degradation processes in real-world videos, and thus the trained VSR models become ineffective in real-world applications. As an attempt to bridge the gap, we build a real-world video super-resolution (RealVSR) dataset by capturing paired LR-HR video sequences using the multi-camera system of iPhone 11 Pro Max. Since the LR-HR video pairs are captured by two separate cameras, there are inevitably certain misalignment and luminance/color differences between them. To more robustly train the VSR model and recover more details from the LR inputs, we convert the LR-HR videos into YCbCr space and decompose the luminance channel into a Laplacian pyramid, and then apply different loss functions to different components. Experiments validate that VSR models trained on our RealVSR dataset demonstrate better visual quality than those trained on synthetic datasets under real-world settings. They also exhibit good generalization capability in cross-camera tests. The dataset and code can be found at https://github.com/IanYeung/RealVSR.