mtybilly/Instruct2DS huggingface dataset:
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
Authors: Luisa Zintgraf, John Bronskill, Lida Theodorou, Matthew Tobias Harris, Edward Cutrell, Cecily Morrison, Katja Hofmann, Simone Stumpf Daniela Massiceti
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Object recognition has made great advances in the last decade, but predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset and benchmark, grounded in the real-world application of teachable object recognizers for people who are blind/low-vision. The dataset contains 3,822 videos of 486 objects recorded by people who are blind/low-vision on their mobile phones. The benchmark reflects a realistic, highly challenging recognition problem, providing a rich playground to drive research in robustness to few-shot, high-variation conditions. We set the benchmark's first state-of-the-art and show there is massive scope for further innovation, holding the potential to impact a broad range of real-world vision applications including tools for the blind/low-vision community. We release the dataset at https://doi.org/10.25383/city.14294597 and benchmark code at https://github.com/microsoft/ORBIT-Dataset.
Domain Generalization via Gradient Surgery
Authors: Rodrigo Echeveste, Diego H. Milone, Enzo Ferrante Lucas Mansilla
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains. When the aim is to make predictions on distributions different from those seen at training, we incur in a domain generalization problem. Methods to address this issue learn a model using data from multiple source domains, and then apply this model to the unseen target domain. Our hypothesis is that when training with multiple domains, conflicting gradients within each mini-batch contain information specific to the individual domains which is irrelevant to the others, including the test domain. If left untouched, such disagreement may degrade generalization performance. In this work, we characterize the conflicting gradients emerging in domain shift scenarios and devise novel gradient agreement strategies based on gradient surgery to alleviate their effect. We validate our approach in image classification tasks with three multi-domain datasets, showing the value of the proposed agreement strategy in enhancing the generalization capability of deep learning models in domain shift scenarios.
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation
Authors: Yi Liu, Yunfei Zhang, Si Wu, Yong Xu, Feng Liangbing, Hau San Wong Tianyi Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Previous state-of-the-art deep generative models improve fine-grained image generation quality by designing hierarchical model structures and synthesizing images across multiple stages. The learning process is typically performed without any supervision in object categories. To address this issue, while at the same time to alleviate the level of complexity of both model design and training, we propose a Single-Stage Controllable GAN (SSC-GAN) for conditional fine-grained image synthesis in a semi-supervised setting. Considering the fact that fine-grained object categories may have subtle distinctions and shared attributes, we take into account three factors of variation for generative modeling: class-independent content, cross-class attributes and class semantics, and associate them with different variables. To ensure disentanglement among the variables, we maximize mutual information between the class-independent variable and synthesized images, map real images to the latent space of a generator to perform consistency regularization of cross-class attributes, and incorporate class semantic-based regularization into a discriminator's feature space. We show that the proposed approach delivers a single-stage controllable generator and high-fidelity synthesized images of fine-grained categories. The proposed approach establishes state-of-the-art semi-supervised image synthesis results across multiple fine-grained datasets.
Partner-Assisted Learning for Few-Shot Image Classification
Authors: Hanchen Xie, Guangxing Han, Shih-Fu Chang, Aram Galstyan, Wael Abd-Almageed Jiawei Ma
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learning for adaptation has dominated the few-shot learning methods, how to train a feature extractor is still a challenge. In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples. We propose a two-stage training scheme, Partner-Assisted Learning (PAL), which first trains a partner encoder to model pair-wise similarities and extract features serving as soft-anchors, and then trains a main encoder by aligning its outputs with soft-anchors while attempting to maximize classification performance. Two alignment constraints from logit-level and feature-level are designed individually. For each few-shot task, we perform prototype classification. Our method consistently outperforms the state-of-the-art method on four benchmarks. Detailed ablation studies of PAL are provided to justify the selection of each component involved in training.
Contrastive Coding for Active Learning Under Class Distribution Mismatch
Authors: Suyun Zhao, Hui Chen, Shuwen Chai, Hong Chen, Cuiping Li Pan Du
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Active learning (AL) is successful based on the assumption that labeled and unlabeled data are obtained from the same class distribution. However, its performance deteriorates under class distribution mismatch, wherein the unlabeled data contain many samples out of the class distribution of labeled data. To effectively handle the problems under class distribution mismatch, we propose a contrastive coding based AL framework named CCAL. Unlike the existing AL methods that focus on selecting the most informative samples for annotating, CCAL extracts both semantic and distinctive features by contrastive learning and combines them in a query strategy to choose the most informative unlabeled samples with matched categories. Theoretically, we prove that the AL error of CCAL has a tight upper bound. Experimentally, we evaluate its performance on CIFAR10, CIFAR100, and an artificial cross-dataset that consists of five datasets; consequently, CCAL achieves state-of-the-art performance by a large margin with remarkably lower annotation cost. To the best of our knowledge, CCAL is the first work related to AL for class distribution mismatch.
Partial Video Domain Adaptation With Partial Adversarial Temporal Attentive Network
Authors: Jianfei Yang, Haozhi Cao, Zhenghua Chen, Qi Li, Kezhi Mao Yuecong Xu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source label space subsumes the target one. The key challenge of PDA is the issue of negative transfer caused by source-only classes. For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem. In this paper, we propose a novel Partial Adversarial Temporal Attentive Network (PATAN) to address the PVDA problem by utilizing both spatial and temporal features for filtering source-only classes. Besides, PATAN constructs effective overall temporal features by attending to local temporal features that contribute more toward the class filtration process. We further introduce new benchmarks to facilitate research on PVDA problems, covering a wide range of PVDA scenarios. Empirical results demonstrate the state-of-the-art performance of our proposed PATAN across the multiple PVDA benchmarks.
Personalized and Invertible Face De-Identification by Disentangled Identity Information Manipulation
Authors: Bo Liu, Yunqian Wen, Rong Xie, Li Song Jingyi Cao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The popularization of intelligent devices including smartphones and surveillance cameras results in more serious privacy issues. De-identification is regarded as an effective tool for visual privacy protection with the process of concealing or replacing identity information. Most of the existing de-identification methods suffer from some limitations since they mainly focus on the protection process and are usually non-reversible. In this paper, we propose a personalized and invertible de-identification method based on the deep generative model, where the main idea is introducing a user-specific password and an adjustable parameter to control the direction and degree of identity variation. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both face de-identification and recovery.
Learning Compatible Embeddings
Authors: Chixiang Zhang, Xiaoqiang Xu, Feng Zhou Qiang Meng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Achieving backward compatibility when rolling out new models can highly reduce costs or even bypass feature re-encoding of existing gallery images for in-production visual retrieval systems. Previous related works usually leverage losses used in knowledge distillation which can cause performance degradations or not guarantee compatibility. To address these issues, we propose a general framework called Learning Compatible Embeddings (LCE) which is applicable for both cross model compatibility and compatible training in direct/forward/backward manners. Our compatibility is achieved by aligning class centers between models directly or via a transformation, and restricting more compact intra-class distributions for the new model. Experiments are conducted in extensive scenarios such as changes of training dataset, loss functions, network architectures as well as feature dimensions, and demonstrate that LCE efficiently enables model compatibility with marginal sacrifices of accuracies.
Seasonal Contrast: Unsupervised Pre-Training From Uncurated Remote Sensing Data
Authors: Alexandre Lacoste, Xavier Giró-i-Nieto, David Vazquez, Pau Rodríguez Oscar Mañas
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use monitoring, or tackling climate change. Although there exist vast amounts of remote sensing data, most of it remains unlabeled and thus inaccessible for supervised learning algorithms. Transfer learning approaches can reduce the data requirements of deep learning algorithms. However, most of these methods are pre-trained on ImageNet and their generalization to remote sensing imagery is not guaranteed due to the domain gap. In this work, we propose Seasonal Contrast (SeCo), an effective pipeline to leverage unlabeled data for in-domain pre-training of remote sensing representations. The SeCo pipeline is composed of two parts. First, a principled procedure to gather large-scale, unlabeled and uncurated remote sensing datasets containing images from multiple Earth locations at different timestamps. Second, a self-supervised algorithm that takes advantage of time and position invariance to learn transferable representations for remote sensing applications. We empirically show that models trained with SeCo achieve better performance than their ImageNet pre-trained counterparts and state-of-the-art self-supervised learning methods on multiple downstream tasks. The datasets and models in SeCo will be made public to facilitate transfer learning and enable rapid progress in remote sensing applications.
Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation
Authors: Yuta Nakashima, Noa Garcia Zechen Bai
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Have you ever looked at a painting and wondered what is the story behind it? This work presents a framework to bring art closer to people by generating comprehensive descriptions of fine-art paintings. Generating informative descriptions for artworks, however, is extremely challenging, as it requires to 1) describe multiple aspects of the image such as its style, content, or composition, and 2) provide background and contextual knowledge about the artist, their influences, or the historical period. To address these challenges, we introduce a multi-topic and knowledgeable art description framework, which modules the generated sentences according to three artistic topics and, additionally, enhances each description with external knowledge. The framework is validated through an exhaustive analysis, both quantitative and qualitative, as well as a comparative human evaluation, demonstrating outstanding results in terms of both topic diversity and information veracity.
Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment
Authors: Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi Pengfei Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: During the last years, convolutional neural networks (CNNs) have triumphed over video quality assessment (VQA) tasks. However, CNN-based approaches heavily rely on annotated data which are typically not available in VQA, leading to the difficulty of model generalization. Recent advances in domain adaptation technique makes it possible to adapt models trained on source data to unlabeled target data. However, due to the distortion diversity and content variation of the collected videos, the intrinsic subjectivity of VQA tasks hampers the adaptation performance. In this work, we propose a curriculum-style unsupervised domain adaptation to handle the cross-domain no-reference VQA problem. The proposed approach could be divided into two stages. In the first stage, we conduct an adaptation between source and target domains to predict the rating distribution for target samples, which can better reveal the subjective nature of VQA. From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences. In the second stage, by regarding samples in confident subdomain as the easy tasks in the curriculum, a fine-level adaptation is conducted between two subdomains to fine-tune the prediction model. Extensive experimental results on benchmark datasets highlight the superiority of the proposed method over the competing methods in both accuracy and speed. The source code is released at https://github.com/cpf0079/UCDA.
GTT-Net: Learned Generalized Trajectory Triangulation
Authors: Enrique Dunn Xiangyu Xu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry. We build on a graph-theoretic formulation of the generalized trajectory triangulation problem, where non-concurrent multi-view imaging geometry is known but global image sequencing is not provided. GTT-Net learns pairwise affinities modeling the spatio-temporal relationships among our input observations and leverages them to determine 3D geometry estimates. Experiments reconstructing 3D motion-capture sequences show GTT-Net outperforms the state of the art in terms of accuracy and robustness. Within the context of articulated motion reconstruction, our proposed architecture is 1) able to learn and enforce semantic 3D motion priors for shared training and test domains, while being 2) able to generalize its performance across different training and test domains. Moreover, GTT-Net provides a computationally streamlined framework for trajectory triangulation with applications to multi-instance reconstruction and event segmentation.
Contrastive Learning of Image Representations With Cross-Video Cycle-Consistency
Authors: Xiaolong Wang Haiping Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Recent works have advanced the performance of self-supervised representation learning by a large margin. The core among these methods is intra-image invariance learning. Two different transformations of one image instance are considered as a positive sample pair, where various tasks are designed to learn invariant representations by comparing the pair. Analogically, for video data, representations of frames from the same video are trained to be closer than frames from other videos, i.e. intra-video invariance. However, cross-video relation has barely been explored for visual representation learning. Unlike intra-video invariance, ground-truth labels of cross-video relation is usually unavailable without human labors. In this paper, we propose a novel contrastive learning method which explores the cross-video relation by using cycle-consistency for general image representation learning. This allows to collect positive sample pairs across different video instances, which we hypothesize will lead to higher-level semantics. We validate our method by transferring our image representation to multiple downstream tasks including visual object tracking, image classification, and action recognition. We show significant improvement over state-of-the-art contrastive learning methods. Project page is available at https://happywu.github.io/cycle_contrast_video.
Learning To Remove Refractive Distortions From Underwater Images
Authors: Nianyi Li, Jinwei Ye Simron Thapa
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The fluctuation of the water surface causes refractive distortions that severely downgrade the image of an underwater scene. Here, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. We first use a physically constrained convolutional network to estimate the distortion map from the refracted image. We then use a generative adversarial network guided by the distortion map to restore the sharp distortion-free image. Since the distortion map indicates correspondences between the distorted image and the distortion-free one, it guides the network to make better predictions. We evaluate our network on several real and synthetic underwater image datasets and show that it out-performs the state-of-the-art algorithms, especially in presence of large distortions. We also show results of complex scenarios, including outdoor swimming pool images captured by the drone and indoor aquarium images taken by cellphone camera.
BARF: Bundle-Adjusting Neural Radiance Fields
Authors: Wei-Chiu Ma, Antonio Torralba, Simon Lucey Chen-Hsuan Lin
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Neural Radiance Fields (NeRF) have recently gained a surge of interest within the computer vision community for its power to synthesize photorealistic novel views of real-world scenes. One limitation of NeRF, however, is its requirement of known camera poses to learn the scene representations. In this paper, we propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from imperfect camera poses -- the joint problem of learning neural 3D representations and registering camera frames. We establish a theoretical connection to classical planar image registration and show that coarse-to-fine registration is also applicable to NeRF. Furthermore, we demonstrate mathematically that positional encoding has a direct impact on the basin of attraction for registration with a synthesis-based objective. Experiments on synthetic and real-world data show that BARF can effectively optimize the neural scene representations and resolve large camera pose misalignment at the same time. This enables applications of view synthesis and localization of video sequences from unknown camera poses, opening up new avenues for visual localization systems (e.g. SLAM) towards sequential registration with NeRF.
Seeking Similarities Over Differences: Similarity-Based Domain Alignment for Adaptive Object Detection
Authors: Rakshith Shetty, Rahaf Aljundi, Daniel Olmeda Reino, Shanshan Zhang, Bernt Schiele Farzaneh Rezaeianaran
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data. This has motivated research in Unsupervised Domain Adaptation (UDA) algorithms for detection. UDA methods learn to adapt from labeled source domains to unlabeled target domains, by inducing alignment between detector features from source and target domains. Yet, there is no consensus on what features to align and how to do the alignment. In our work, we propose a framework that generalizes the different components commonly used by UDA methods laying the ground for an in-depth analysis of the UDA design space. Specifically, we propose a novel UDA algorithm, ViSGA, a direct implementation of our framework, that leverages the best design choices and introduces a simple but effective method to aggregate features at the instance-level based on the visual similarity before inducing group alignment via adversarial training. We show that both similarity-based grouping and adversarial training allows our model to focus on coarsely aligning feature groups, without being forced to match all instances across loosely aligned domains. Finally, we examine the applicability of ViSGA to the setting where labeled data are gathered from different sources. Experiments show that not only our method outperforms previous single-source approaches on Sim2Real and Adverse Weather, but also generalizes well to the multi-source setting.
LapsCore: Language-Guided Person Search via Color Reasoning
Authors: Zizheng Yan, Xiaoguang Han, Guanbin Li, Changqing Zou, Shuguang Cui Yushuang Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: The key point of language-guided person search is to construct the cross-modal association between visual and textual input. Existing methods focus on designing multimodal attention mechanisms and novel cross-modal loss functions to learn such association implicitly. We propose a representation learning method for language-guided person search based on color reasoning (LapsCore). It can explicitly build a fine-grained cross-modal association bidirectionally. Specifically, a pair of dual sub-tasks, image colorization and text completion, is designed. In the former task, rich text information is learned to colorize gray images, and the latter one requests the model to understand the image and complete color word vacancies in the captions. The two sub-tasks enable models to learn correct alignments between text phrases and image regions, so that rich multimodal representations can be learned. Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of the proposed method.
Deep Permutation Equivariant Structure From Motion
Authors: Hodaya Koslowsky, Yoni Kasten, Haggai Maron, Meirav Galun, Ronen Basri Dror Moran
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Existing deep methods produce highly accurate 3D reconstructions in stereo and multiview stereo settings, i.e., when cameras are both internally and externally calibrated. Nevertheless, the challenge of simultaneous recovery of camera poses and 3D scene structure in multiview settings with deep networks is still outstanding. Inspired by projective factorization for Structure from Motion (SFM) and by deep matrix completion techniques, we propose a neural network architecture that, given a set of point tracks in multiple images of a static scene, recovers both the camera parameters and a (sparse) scene structure by minimizing an unsupervised reprojection loss. Our network architecture is designed to respect the structure of the problem: the sought output is equivariant to permutations of both cameras and scene points. Notably, our method does not require initialization of camera parameters or 3D point locations. We test our architecture in two setups: (1) single scene reconstruction and (2) learning from multiple scenes. Our experiments, conducted on a variety of datasets in both internally calibrated and uncalibrated settings, indicate that our method accurately recovers pose and structure, on par with classical state of the art methods. Additionally, we show that a pre-trained network can be used to reconstruct novel scenes using inexpensive fine-tuning with no loss of accuracy.
Collaborative Unsupervised Visual Representation Learning From Decentralized Data
Authors: Xin Gan, Yonggang Wen, Shuai Zhang, Shuai Yi Weiming Zhuang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Unsupervised representation learning has achieved outstanding performances using centralized data available on the Internet. However, the increasing awareness of privacy protection limits sharing of decentralized unlabeled image data that grows explosively in multiple parties (e.g. mobile phones and cameras). As such, a natural problem is how to leverage these data to learn visual representations for downstream tasks while preserving data privacy. To address this problem, we propose a novel federated unsupervised learning framework, FedU. In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network. Then, a central server aggregates trained models and updates clients' models with the aggregated global model. It preserves data privacy as each party only has access to its raw data. Decentralized data among multiple parties is normally non-independent and identically distributed (non-IID), which leads to performance degradation. To tackle this challenge, we propose two simple but effective methods: (1) We design the communication protocol to upload only the encoders of online networks for server aggregation and update them with the aggregated encoder. (2) We introduce a new module to dynamically decide how to update the predictors based on the degree of divergence caused by non-IID. The predictor is the other component of the online network. Extensive experiments and ablations demonstrate the effectiveness and significance of FedU. It outperforms training with only one party by over 5% and other methods by over 14% in linear and semi-supervised evaluation on non-IID data.
DeepPRO: Deep Partial Point Cloud Registration of Objects
Authors: Onur C. Hamsici, Steven Feng, Prachee Sharma, Thorsten Gernoth Donghoon Lee
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We consider the problem of online and real-time registration of partial point clouds obtained from an unseen real-world rigid object without knowing its 3D model. The point cloud is partial as it is obtained by a depth sensor capturing only the visible part of the object from a certain viewpoint. It introduces two main challenges: 1) two partial point clouds do not fully overlap and 2) keypoints tend to be less reliable when the visible part of the object does not have salient local structures. To address these issues, we propose DeepPRO, a keypoint-free and an end-to-end trainable deep neural network. Its core idea is inspired by how humans align two point clouds: we can imagine how two point clouds will look like after the registration based on their shape. To realize the idea, DeepPRO has inputs of two partial point clouds and directly predicts the point-wise location of the aligned point cloud. By preserving the ordering of points during the prediction, we enjoy dense correspondences between input and predicted point clouds when inferring rigid transform parameters. We conduct extensive experiments on the real-world Linemod and synthetic ModelNet40 datasets. In addition, we collect and evaluate on the PRO1k dataset, a large-scale version of Linemod meant to test generalization to real-world scans. Results show that DeepPRO achieves the best accuracy against thirteen strong baseline methods, e.g., 2.2mm ADD on the Linemod dataset, while running 50 fps on mobile devices.
RECALL: Replay-Based Continual Learning in Semantic Segmentation
Authors: Umberto Michieli, Marco Toldo, Pietro Zanuttigh Andrea Maracani
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Deep networks allow to obtain outstanding results in semantic segmentation, however they need to be trained in a single shot with a large amount of data. Continual learning settings where new classes are learned in incremental steps and previous training data is no longer available are challenging due to the catastrophic forgetting phenomenon. Existing approaches typically fail when several incremental steps are performed or in presence of a distribution shift of the background class. We tackle these issues by recreating no longer available data for the old classes and outlining a content inpainting scheme on the background class. We propose two sources for replay data. The first resorts to a generative adversarial network to sample from the class space of past learning steps. The second relies on web-crawled data to retrieve images containing examples of old classes from online databases. In both scenarios no samples of past steps are stored, thus avoiding privacy concerns. Replay data are then blended with new samples during the incremental steps. Our approach, RECALL, outperforms state-of-the-art methods.
Extending Neural P-Frame Codecs for B-Frame Coding
Authors: Taco Cohen Reza Pourreza
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: While most neural video codecs address P-frame coding (predicting each frame from past ones), in this paper we address B-frame compression (predicting frames using both past and future reference frames). Our B-frame solution is based on the existing P-frame methods. As a result, B-frame coding capability can easily be added to an existing neural codec. The basic idea of our B-frame coding method is to interpolate the two reference frames to generate a single reference frame and then use it together with an existing P-frame codec to encode the input B-frame. Our studies show that the interpolated frame is a much better reference for the P-frame codec compared to using the previous frame as is usually done. Our results show that using the proposed method with an existing P-frame codec can lead to 28.5% saving in bit-rate on the UVG dataset compared to the P-frame codec while generating the same video quality.
HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering
Authors: Jing Liu, Weining Wang, Hanqing Lu Fei Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Relational reasoning is at the heart of video question answering. However, existing approaches suffer from several common limitations: (1) they only focus on either object-level or frame-level relational reasoning, and fail to integrate the both; and (2) they neglect to leverage semantic knowledge for relational reasoning. In this work, we propose a Hierarchical VisuAl-Semantic RelatIonal Reasoning (HAIR) framework to address these limitations. Specifically, we present a novel graph memory mechanism to perform relational reasoning, and further develop two types of graph memory: a) visual graph memory that leverages visual information of video for relational reasoning; b) semantic graph memory that is specifically designed to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform relational reasoning in the semantic space. Taking advantage of both graph memory mechanisms, we build a hierarchical framework to enable visual-semantic relational reasoning from object level to frame level. Experiments on four challenging benchmark datasets show that the proposed framework leads to state-of-the-art performance, with fewer parameters and faster inference speed. Besides, our approach also shows superior performance on other video+language task.
Ensemble Attention Distillation for Privacy-Preserving Federated Learning
Authors: Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje Xuan Gong
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We consider the problem of Federated Learning (FL) where numerous decentralized computational nodes collaborate with each other to train a centralized machine learning model without explicitly sharing their local data samples. Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model. Existing FL methods deal with these issues by either sharing local parameters or fusing models via online distillation. However, such a design leads to multiple rounds of inter-node communication resulting in substantial bandwidth consumption, while also increasing the risk of data leakage and consequent privacy issues. To address these problems, we propose a new distillation-based FL framework that can preserve privacy by design, while also consuming substantially less network communication resources when compared to the current state-of-the-art. Our framework engages in inter-node communication using only publicly available and approved datasets, thereby giving explicit privacy control to the user. To distill knowledge among the various local models, our framework involves a novel ensemble distillation algorithm that uses both final prediction as well as model attention. This algorithm explicitly considers the diversity among various local nodes while also seeking consensus among them. This results in a comprehensive technique to distill knowledge from various decentralized nodes. We demonstrate the various aspects and the associated benefits of our FL framework through extensive experiments that produce state-of-the-art results on both classification and segmentation tasks on natural and medical images.
Voxel Transformer for 3D Object Detection
Authors: Yujing Xue, Minzhe Niu, Haoyue Bai, Jiashi Feng, Xiaodan Liang, Hang Xu, Chunjing Xu Jiageng Mao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: We present Voxel Transformer (VoTr), a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds. Conventional 3D convolutional backbones in voxel-based 3D detectors cannot efficiently capture large context information, which is crucial for object recognition and localization, owing to the limited receptive fields. In this paper, we resolve the problem by introducing a Transformer-based architecture that enables long-range relationships between voxels by self-attention. Given the fact that non-empty voxels are naturally sparse but numerous, directly applying standard Transformer on voxels is non-trivial. To this end, we propose the sparse voxel module and the submanifold voxel module, which can operate on the empty and non-empty voxel positions effectively. To further enlarge the attention range while maintaining comparable computational overhead to the convolutional counterparts, we propose two attention mechanisms for multi-head attention in those two modules: Local Attention and Dilated Attention, and we further propose Fast Voxel Query to accelerate the querying process in multi-head attention. VoTr contains a series of sparse and submanifold voxel modules and can be applied in most voxel-based detectors. Our proposed VoTr shows consistent improvement over the convolutional baselines while maintaining computational efficiency on the KITTI dataset and the Waymo Open dataset.
Out-of-Boundary View Synthesis Towards Full-Frame Video Stabilization
Authors: Jing Zhang, Dacheng Tao Yufei Xu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Warping-based video stabilizers smooth camera trajectory by constraining each pixel's displacement and warp stabilized frames from unstable ones accordingly. However, since the view outside the boundary is not available during warping, the resulting holes around the boundary of the stabilized frame must be discarded (i.e., cropping) to maintain visual consistency, and thus does leads to a tradeoff between stability and cropping ratio. In this paper, we make a first attempt to address this issue by proposing a new Out-of-boundary View Synthesis (OVS) method. By the nature of spatial coherence between adjacent frames and within each frame, OVS extrapolates the out-of-boundary view by aligning adjacent frames to each reference one. Technically, it first calculates the optical flow and propagates it to the outer boundary region according to the affinity, and then warps pixels accordingly. OVS can be integrated into existing warping-based stabilizers as a plug-and-play pre-processing module to significantly improve the cropping ratio of the stabilized results. In addition, stability is improved because the jitter amplification effect caused by cropping and resizing is reduced. Experimental results on the NUS benchmark show that OVS can improve the performance of five representative state-of-the-art methods in terms of objective metrics and subjective visual quality.
Multimodal Clustering Networks for Self-Supervised Learning From Unlabeled Videos
Authors: Andrew Rouditchenko, Kevin Duarte, Hilde Kuehne, Samuel Thomas, Angie Boggust, Rameswar Panda, Brian Kingsbury, Rogerio Feris, David Harwath, James Glass, Michael Picheny, Shih-Fu Chang Brian Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data across various modalities. In this context, this paper proposes a framework that, starting from a pre-trained backbone, learns a common multimodal embedding space that, in addition to sharing representations across different modalities, enforces a grouping of semantically similar instances. To this end, we extend the concept of instance-level contrastive learning with a multimodal clustering step in the training pipeline to capture semantic similarities across modalities. The resulting embedding space enables retrieval of samples across all modalities, even from unseen datasets and different domains. To evaluate our approach, we train our model on the HowTo100M dataset and evaluate its zero-shot retrieval capabilities in two challenging domains, namely text-to-video retrieval, and temporal action localization, showing state-of-the-art results on four different datasets.
Towards a Universal Model for Cross-Dataset Crowd Counting
Authors: Xiaopeng Hong, Xing Wei, Yunfeng Qiu, Yihong Gong Zhiheng Ma
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: This paper proposes to handle the practical problem of learning a universal model for crowd counting across scenes and datasets. We dissect that the crux of this problem is the catastrophic sensitivity of crowd counters to scale shift, which is very common in the real world and caused by factors such as different scene layouts and image resolutions. Therefore it is difficult to train a universal model that can be applied to various scenes. To address this problem, we propose scale alignment as a prime module for establishing a novel crowd counting framework. We derive a closed-form solution to get the optimal image rescaling factors for alignment by minimizing the distances between their scale distributions. A novel neural network together with a loss function based on an efficient sliced Wasserstein distance is also proposed for scale distribution estimation. Benefiting from the proposed method, we have learned a universal model that generally works well on several datasets where can even outperform state-of-the-art models that are particularly fine-tuned for each dataset significantly. Experiments also demonstrate the much better generalizability of our model to unseen scenes.
GAN Inversion for Out-of-Range Images With Geometric Transformations
Authors: Seongtae Kim, Sunghyun Cho Kyoungkook Kang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: For successful semantic editing of real images, it is critical for a GAN inversion method to find an in-domain latent code that aligns with the domain of a pre-trained GAN model. Unfortunately, such in-domain latent codes can be found only for in-range images that align with the training images of a GAN model. In this paper, we propose BDInvert, a novel GAN inversion approach to semantic editing of out-of-range images that are geometrically unaligned with the training images of a GAN model. To find a latent code that is semantically editable, BDInvert inverts an input out-of-range image into an alternative latent space than the original latent space. We also propose a regularized inversion method to find a solution that supports semantic editing in the alternative space. Our experiments show that BDInvert effectively supports semantic editing of out-of-range images with geometric transformations.
Scaling Up Instance Annotation via Label Propagation
Authors: Ethan Weber, Antonio Torralba Dim P. Papadopoulos
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Manually annotating object segmentation masks is very time-consuming. While interactive segmentation methods offer a more efficient alternative, they become unaffordable at a large scale because the cost grows linearly with the number of annotated masks. In this paper, we propose a highly efficient annotation scheme for building large datasets with object segmentation masks. At a large scale, images contain many object instances with similar appearance. We exploit these similarities by using hierarchical clustering on mask predictions made by a segmentation model. We propose a scheme that efficiently searches through the hierarchy of clusters and selects which clusters to annotate. Humans manually verify only a few masks per cluster, and the labels are propagated to the whole cluster. Through a large-scale experiment to populate 1M unlabeled images with object segmentation masks for 80 object classes, we show that (1) we obtain 1M object segmentation masks with an total annotation time of only 290 hours; (2) we reduce annotation time by 76x compared to manual annotation; (3) the segmentation quality of our masks is on par with those from manually annotated datasets. Code, data, and models are available online.
Learning RAW-to-sRGB Mappings With Inaccurately Aligned Supervision
Authors: Haolin Wang, Ming Liu, Ruohao Wang, Jiawei Zhang, Wangmeng Zuo Zhilu Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Learning RAW-to-sRGB mapping has drawn increasing attention in recent years, wherein an input raw image is trained to imitate the target sRGB image captured by another camera. However, the severe color inconsistency makes it very challenging to generate well-aligned training pairs of input raw and target sRGB images. While learning with inaccurately aligned supervision is prone to causing pixel shift and producing blurry results. In this paper, we circumvent such issue by presenting a joint learning model for image alignment and RAW-to-sRGB mapping. To diminish the effect of color inconsistency in image alignment, we introduce to use a global color mapping (GCM) module to generate an initial sRGB image given the input raw image, which can keep the spatial location of the pixels unchanged, and the target sRGB image is utilized to guide GCM for converting the color towards it. Then a pre-trained optical flow estimation network (e.g., PWC-Net) is deployed to warp the target sRGB image to align with the GCM output. To alleviate the effect of inaccurately aligned supervision, the warped target sRGB image is leveraged to learn RAW-to-sRGB mapping. When training is done, the GCM module and optical flow network can be detached, thereby bringing no extra computation cost for inference. Experiments show that our method performs favorably against state-of-the-arts on ZRR and SR-RAW datasets. With our joint learning model, a light-weight backbone can achieve better quantitative and qualitative performance on ZRR dataset. Codes are available at https://github.com/cszhilu1998/RAW-to-sRGB.
Context Reasoning Attention Network for Image Super-Resolution
Authors: Donglai Wei, Can Qin, Huan Wang, Hanspeter Pfister, Yun Fu Yulun Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2021 (Main Conference Track), 2021

Abstract: Deep convolutional neural networks (CNNs) are achieving great successes for image super-resolution (SR), where global context is crucial for accurate restoration. However, the basic convolutional layer in CNNs is designed to extract local patterns, lacking the ability to model global context. Many efforts have been devoted to augmenting SR networks with the global context information, especially by global feature interaction methods. These works incorporate the global context into local feature representation. However, recent advances in neuroscience show that it is necessary for the neurons to dynamically modulate their functions according to context, which is neglected in most CNN based SR methods. Motivated by those observations and analyses, we propose context reasoning attention network (CRAN) to adaptively modulate the convolution kernel according to the global context. Specifically, we extract global context descriptors, which are further enhanced with semantic reasoning. Channel and spatial interactions are then proposed to generate context reasoning attention mask, which is applied to modify the convolution kernel adaptively. Such a modulated convolution layer is utilized as basic component to build the network blocks and itself. Extensive experiments on benchmark datasets with multiple degradation models show that our CRAN achieves superior SR results and favourable efficiency trade-off.