Myungseo Song, Jinyoung Choi, Bohyung Han

Abstract: We propose a versatile deep image compression network based on Spatial Feature Transform (SFT), which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e.g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network. This is even possible with a pretrained network without learning separate models for individual tasks. Our algorithm achieves outstanding rate-distortion trade-off compared to the approaches based on multiple models that are optimized separately for several different target rates. At the same level of compression, the proposed approach successfully improves performance on image classification and text region quality preservation via task-aware quality map estimation without additional model training. The code is available at the project website https://github.com/micmic123/QmapCompression.

@InProceedings{Song_2021_ICCV,
    author    = {Song, Myungseo and Choi, Jinyoung and Han, Bohyung},
    title     = {Variable-Rate Deep Image Compression Through Spatially-Adaptive Feature Transform},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2380-2389}
}


Hugo Bertiche, Meysam Madadi, Emilio Tylson, Sergio Escalera

Abstract: We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality.

@InProceedings{Bertiche_2021_ICCV,
    author    = {Bertiche, Hugo and Madadi, Meysam and Tylson, Emilio and Escalera, Sergio},
    title     = {DeePSD: Automatic Deep Skinning and Pose Space Deformation for 3D Garment Animation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5471-5480}
}


Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, Sung-Jea Ko

Abstract: Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks. Conventional methods typically stack sub-networks with multi-scale input images and gradually improve sharpness of images from the bottom sub-network to the top sub-network, yielding inevitably high computational costs. Toward a fast and accurate deblurring network design, we revisit the coarse-to-fine strategy and present a multi-input multi-output U-net (MIMO-UNet). The MIMO-UNet has three distinct features. First, the single encoder of the MIMO-UNet takes multi-scale input images to ease the difficulty of training. Second, the single decoder of the MIMO-UNet outputs multiple deblurred images with different scales to mimic multi-cascaded U-nets using a single U-shaped network. Last, asymmetric feature fusion is introduced to merge multi-scale features in an efficient manner. Extensive experiments on the GoPro and RealBlur datasets demonstrate that the proposed network outperforms the state-of-the-art methods in terms of both accuracy and computational complexity. Source code is available for research purposes at https://github.com/chosj95/MIMO-UNet.

@InProceedings{Cho_2021_ICCV,
    author    = {Cho, Sung-Jin and Ji, Seo-Won and Hong, Jun-Pyo and Jung, Seung-Won and Ko, Sung-Jea},
    title     = {Rethinking Coarse-To-Fine Approach in Single Image Deblurring},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4641-4650}
}


Rawal Khirodkar, Visesh Chari, Amit Agrawal, Ambrish Tyagi

Abstract: A key assumption of top-down human pose estimation approaches is their expectation of having a single person/instance present in the input bounding box. This often leads to failures in crowded scenes with occlusions. We propose a novel solution to overcome the limitations of this fundamental assumption. Our Multi-Instance Pose Network (MIPNet) allows for predicting multiple 2D pose instances within a given bounding box. We introduce a Multi-Instance Modulation Block (MIMB) that can adaptively modulate channel-wise feature responses for each instance and is parameter efficient. We demonstrate the efficacy of our approach by evaluating on COCO, CrowdPose, and OCHuman datasets. Specifically, we achieve 70.0 AP on CrowdPose and 42.5 AP on OCHuman test sets, a significant improvement of 2.4 AP and 6.5 AP over the prior art, respectively. When using ground truth bounding boxes for inference, MIPNet achieves an improvement of 0.7 AP on COCO, 0.9 AP on CrowdPose, and 9.1 AP on OCHuman validation sets compared to HRNet. Interestingly, when fewer, high confidence bounding boxes are used, HRNet's performance degrades (by 5 AP) on OCHuman, whereas MIPNet maintains a relatively stable performance (drop of 1 AP) for the same inputs.

@InProceedings{Khirodkar_2021_ICCV,
    author    = {Khirodkar, Rawal and Chari, Visesh and Agrawal, Amit and Tyagi, Ambrish},
    title     = {Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3122-3131}
}


Abstract: Satellite multi-view stereo (MVS) imagery is particularly suited for large-scale Earth surface reconstruction. Differing from the perspective camera model (pin-hole model) that is commonly used for close-range and aerial cameras, the cubic rational polynomial camera (RPC) model is the mainstream model for push-broom linear-array satellite cameras. However, the homography warping used in the prevailing learning based MVS methods is only applicable to pin-hole cameras. In order to apply the SOTA learning based MVS technology to the satellite MVS taskfor large-scale Earth surface reconstruction, RPC warping should be considered. In this work, we propose, for the first time, a rigorous RPC warping module. The rational polynomial coefficients are recorded as a tensor, and the RPC warping is formulated as a series of tensor transformations. Based on the RPC warping, we propose the deep learning based satellite MVS (SatMVS) framework for large-scale and wide depth range Earth surface reconstruction. We also introduce a large-scale satellite image dataset consisting of 519 5120x5120 images, which we call the TLC SatMVS dataset. The satellite images were acquired from a three-line camera (TLC) that catches triple-view images simultaneously, forming a valuable supplement to the existing open-source WorldView-3 datasets with single-scanline images. Experiments show that the proposed RPC warping module and the SatMVS framework can achieve a superior reconstruction accuracy compared to the pin-hole fitting method and conventional MVS methods. Code and data are available at https://github.com/WHU-GPCV/SatMVS.

@InProceedings{Gao_2021_ICCV,
    author    = {Gao, Jian and Liu, Jin and Ji, Shunping},
    title     = {Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6148-6157}
}


A New Journey From SDRTV to HDRTV

Xiangyu Chen, Zhengwen Zhang, Jimmy S. Ren, Lynhoo Tian, Yu Qiao, Chao Dong

Abstract: Nowadays modern displays are capable to render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most available resources are still in standard dynamic range (SDR). Therefore, there is an urgent demand to transform existing SDR-TV contents into their HDR-TV versions. In this paper, we conduct an analysis of SDRTV-to-HDRTV task by modeling the formation of SDRTV/HDRTV content. Base on the analysis, we propose a three-step solution pipeline including adaptive global color mapping, local enhancement and highlight generation. Moreover, the above analysis inspires us to present a lightweight network that utilizes global statistics as guidance to conduct image-adaptive color mapping. In addition, we construct a dataset using HDR videos in HDR10 standard, named HDRTV1K, and select five metrics to evaluate the results of SDRTV-to-HDRTV algorithms. Furthermore, our final results achieve state-of-the-art performance in quantitative comparisons and visual quality. The code and dataset are available at https://github.com/chxy95/HDRTVNet.

@InProceedings{Chen_2021_ICCV,
    author    = {Chen, Xiangyu and Zhang, Zhengwen and Ren, Jimmy S. and Tian, Lynhoo and Qiao, Yu and Dong, Chao},
    title     = {A New Journey From SDRTV to HDRTV},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4500-4509}
}


Mo Shan, Qiaojun Feng, You-Yi Jau, Nikolay Atanasov

Abstract: Autonomous systems need to understand the semantics and geometry of their surroundings in order to comprehend and safely execute object-level task specifications. This paper proposes an expressive yet compact model for joint object pose and shape optimization, and an associated optimization algorithm to infer an object-level map from multi-view RGB-D camera observations. The model is expressive because it captures the identities, positions, orientations, and shapes of objects in the environment. It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps. Different from other works that rely on a single object representation format, our approach has a bi-level object model that captures both the coarse level scale as well as the fine level shape details. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods.

@InProceedings{Shan_2021_ICCV,
    author    = {Shan, Mo and Feng, Qiaojun and Jau, You-Yi and Atanasov, Nikolay},
    title     = {ELLIPSDF: Joint Object Pose and Shape Optimization With a Bi-Level Ellipsoid and Signed Distance Function Description},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5946-5955}
}


Siyuan Huang, Yichen Xie, Song-Chun Zhu, Yixin Zhu

Abstract: To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immerse variations due to camera views, lighting, occlusions, etc. In this paper, we tackle this immanent challenge by introducing a spatio-temporal representation learning (STRL) framework, capable of learning from unlabeled 3D point clouds in a self-supervised fashion. Inspired by how infants learn from visual data in-the-wild, we explore the rich spatio-temporal cues derived from the 3D data. Specifically, STRL takes two temporal-correlated frames from a 3D point cloud sequence as the input, transforms it with spatial data augmentation, and learns the invariant representation self-supervisedly. To corroborate the efficacy of STRL, we conduct extensive experiments on synthetic, indoor, and outdoor datasets. Experimental results demonstrate that, compared with supervised learning methods, the learned self-supervised representation facilitates various models to attain comparable or even better performances while capable of generalizing pre-trained models to downstream tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. Moreover, spatio-temporal contextual cues embedded in 3D point clouds significantly improve the learned representations.

@InProceedings{Huang_2021_ICCV,
    author    = {Huang, Siyuan and Xie, Yichen and Zhu, Song-Chun and Zhu, Yixin},
    title     = {Spatio-Temporal Self-Supervised Representation Learning for 3D Point Clouds},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6535-6545}
}


Liangzhi Li, Bowen Wang, Manisha Verma, Yuta Nakashima, Ryo Kawasaki, Hajime Nagahara

Abstract: Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not directly involved in the decision-making process of the classifier. In this paper, we propose a slot attention-based classifier called SCOUTER for transparent yet accurate classification. Two major differences from other attention-based methods include: (a) SCOUTER's explanation is involved in the final confidence for each category, offering more intuitive interpretation, and (b) all the categories have their corresponding positive or negative explanation, which tells "why the image is of a certain category" or "why the image is not of a certain category." We design a new loss tailored for SCOUTER that controls the model's behavior to switch between positive and negative explanations, as well as the size of explanatory regions. Experimental results show that SCOUTER can give better visual explanations in terms of various metrics while keeping good accuracy on small and medium-sized datasets.

@InProceedings{Li_2021_ICCV,
    author    = {Li, Liangzhi and Wang, Bowen and Verma, Manisha and Nakashima, Yuta and Kawasaki, Ryo and Nagahara, Hajime},
    title     = {SCOUTER: Slot Attention-Based Classifier for Explainable Image Recognition},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1046-1055}
}


Chong Liu, Fengda Zhu, Xiaojun Chang, Xiaodan Liang, Zongyuan Ge, Yi-Dong Shen

Abstract: Vision-language Navigation (VLN) task requires an agent to perceive both the visual scene and natural language and navigate step-by-step. Large data bias makes the VLN task challenging, which is caused by the disparity ratio between small data scale and large navigation space. Previous works have proposed many data augmentation methods to reduce data bias. However, these works do not explicitly reduce the data bias across different house scenes. Therefore, the agent would be overfitting to the seen scenes and perform navigation poorly in the unseen scenes. To tackle this problem, we propose the random environmental mixup (REM) method, which generates augmentation data in cross-connected house scenes. This method consists of three steps: 1) we select the key viewpoints according to the room connection graph for each scene in the training split; 2) we cross-connect the key views of different scenes to construct augmented scenes; 3) we generate augmentation data triplets (environment, path, instruction) in the cross-connected scenes. Our experiments prove that the augmentation data helps the agent reduce its performance gap between the seen and unseen environment and improve its performance, making our model be the best existing approach on the standard benchmark.

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Chong and Zhu, Fengda and Chang, Xiaojun and Liang, Xiaodan and Ge, Zongyuan and Shen, Yi-Dong},
    title     = {Vision-Language Navigation With Random Environmental Mixup},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1644-1654}
}


Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, Yin Li

Abstract: Learning from image-text data has demonstrated recent success for many recognition tasks, yet is currently limited to visual features or individual visual concepts such as objects. In this paper, we propose one of the first methods that learn from image-sentence pairs to extract a graphical representation of localized objects and their relationships within an image, known as scene graph. To bridge the gap between images and texts, we leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph. Further, we design a Transformer-based model to predict these "pseudo" labels via a masked token prediction task. Learning from only image-sentence pairs, our model achieves 30% relative gain over a latest method trained with human-annotated unlocalized scene graphs. Our model also shows strong results for weakly and fully supervised scene graph generation. In addition, we explore an open-vocabulary setting for detecting scene graphs, and present the first result for open-set scene graph generation.

@InProceedings{Zhong_2021_ICCV,
    author    = {Zhong, Yiwu and Shi, Jing and Yang, Jianwei and Xu, Chenliang and Li, Yin},
    title     = {Learning To Generate Scene Graph From Natural Language Supervision},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1823-1834}
}


Editing Conditional Radiance Fields

Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, Bryan Russell

Abstract: A neural radiance field (NeRF) is a scene model supporting high-quality view synthesis, optimized per scene. In this paper, we explore enabling user editing of a category-level NeRF trained on a shape category. Specifically, we propose a method for propagating coarse 2D user scribbles to the 3D space, to modify the color or shape of a local region. First, we propose a conditional radiance field that incorporates new modular network components, including a branch that is shared across object instances in the category. Observing multiple instances of the same category, our model learns underlying part semantics without any supervision, thereby allowing the propagation of coarse 2D user scribbles to the entire 3D region (e.g., chair seat) in a consistent fashion. Next, we investigate for the editing tasks which components of our network require updating. We propose a hybrid network update strategy that targets the later network components, which balances efficiency and accuracy. During user interaction, we formulate an optimization problem that both satisfies the user's constraints and preserves the original object structure. We demonstrate our approach on a variety of editing tasks over three shape datasets and show that it outperforms prior neural editing approaches. Finally, we edit the appearance and shape of a real photograph and show that the edit propagates to extrapolated novel views.

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Steven and Zhang, Xiuming and Zhang, Zhoutong and Zhang, Richard and Zhu, Jun-Yan and Russell, Bryan},
    title     = {Editing Conditional Radiance Fields},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5773-5783}
}


Md Amirul Islam, Matthew Kowal, Sen Jia, Konstantinos G. Derpanis, Neil D. B. Bruce

Abstract: In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information. Specifically, we demonstrate that positional information is encoded based on the ordering of the channel dimensions, while semantic information is largely not. Following this demonstration, we show the real world impact of these findings by applying them to two applications. First, we propose a simple yet effective data augmentation strategy and loss function which improves the translation invariance of a CNN's output. Second, we propose a method to efficiently determine which channels in the latent representation are responsible for (i) encoding overall position information or (ii) region-specific positions. We first show that semantic segmentation has a significant reliance on the overall position channels to make predictions. We then show for the first time that it is possible to perform a `region-specific' attack, and degrade a network's performance in a particular part of the input. We believe our findings and demonstrated applications will benefit research areas concerned with understanding the characteristics of CNNs.

@InProceedings{Islam_2021_ICCV,
    author    = {Islam, Md Amirul and Kowal, Matthew and Jia, Sen and Derpanis, Konstantinos G. and Bruce, Neil D. B.},
    title     = {Global Pooling, More Than Meets the Eye: Position Information Is Encoded Channel-Wise in CNNs},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {793-801}
}


Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang

Abstract: We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Trasnsformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) to the ViT architecture (i.e. shift, scale, and distortion invariance) while maintaining the merits of Transformers (i.e. dynamic attention, global context, and better generalization). We validate CvT by conducting extensive experiments, showing that this approach achieves state-of-the-art performance over other Vision Transformers and ResNets on ImageNet-1k, with less parameters and lower FLOPs. In addition, performance gains are maintained when pretrained on larger datasets (e.g. ImageNet-22k) and fine-tuned to downstream tasks. Finally, our results show that the positional encoding, a crucial component in existing Vision Transformers, can be safely removed in our model, simplifying the design for higher resolution vision tasks. Code will be released at https://github.com/microsoft/CvT.

@InProceedings{Wu_2021_ICCV,
    author    = {Wu, Haiping and Xiao, Bin and Codella, Noel and Liu, Mengchen and Dai, Xiyang and Yuan, Lu and Zhang, Lei},
    title     = {CvT: Introducing Convolutions to Vision Transformers},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {22-31}
}


Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang

Abstract: Most weakly supervised semantic segmentation (WSSS) methods follow the pipeline that generates pseudo-masks initially and trains the segmentation model with the pseudo-masks in fully supervised manner after. However, we find some matters related to the pseudo-masks, including high quality pseudo-masks generation from class activation maps (CAMs), and training with noisy pseudo-mask supervision. For these matters, we propose the following designs to push the performance to new state-of-art: (i) Coefficient of Variation Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask Generation to project the expanded CAMs to pseudo-mask based on a new metric indicating the importance of each class on each location, instead of the scores trained from binary classifiers. (iii) Pretended Under-Fitting strategy to suppress the influence of noise in pseudo-mask; (iv) Cyclic Pseudo-mask to boost the pseudo-masks during training of fully supervised semantic segmentation (FSSS). Experiments based on our methods achieve new state-of-art results on two changeling weakly supervised semantic segmentation datasets, pushing the mIoU to 70.0% and 40.2% on PAS-CAL VOC 2012 and MS COCO 2014 respectively. Codes including segmentation framework are released at https://github.com/Eli-YiLi/PMM

@InProceedings{Li_2021_ICCV,
    author    = {Li, Yi and Kuang, Zhanghui and Liu, Liyang and Chen, Yimin and Zhang, Wayne},
    title     = {Pseudo-Mask Matters in Weakly-Supervised Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6964-6973}
}


Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, Kwang Moo Yi

Abstract: We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other. By doing so, one has the option to query only the points of interest and retrieve sparse correspondences, or to query all points in an image and obtain dense mappings. Importantly, in order to capture both local and global priors, and to let our model relate between image regions using the most relevant among said priors, we realize our network using a transformer. At inference time, we apply our correspondence network by recursively zooming in around the estimates, yielding a multi-scale pipeline able to provide highly-accurate correspondences. Our method significantly outperforms the state-of-the-art on both sparse and dense correspondence problems on multiple datasets and tasks, ranging from wide-baseline stereo to optical flow, without any retraining for a specific dataset.

@InProceedings{Jiang_2021_ICCV,
    author    = {Jiang, Wei and Trulls, Eduard and Hosang, Jan and Tagliasacchi, Andrea and Yi, Kwang Moo},
    title     = {COTR: Correspondence Transformer for Matching Across Images},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6207-6217}
}


Mengde Xu, Zheng Zhang, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, Zicheng Liu

Abstract: Previous pseudo-label approaches for semi-supervised object detection typically follow a multi-stage schema, with the first stage to train an initial detector on a few labeled data, followed by the pseudo labeling and re-training stage on unlabeled data. These multi-stage methods complicate the training, and also hinder the use of improved detectors for more accurate pseudo-labeling. In this paper, we propose an end-to-end approach to simultaneously improve the detector and pseudo labels gradually for semi-supervised object detection. The pseudo labels are generated on the fly by a teacher model which is an aggregated version of the student detector at different steps. As the detector becomes stronger during the training, the teacher detector's performance improves and the pseudo labels tend to be more accurate, which further benefits the detector training. Within the end-to-end training, we present two simple yet effective techniques: weigh the classification loss of unlabeled images through soft teacher and select reliable pseudo boxes for regression through box jittering. Experimentally, the proposed approach outperforms the state-of-the-art methods by a large margin on MS-COCO benchmark by using Faster R-CNN with ResNet-50 and FPN, reaching 20.5 mAP, 30.7 mAP and 34.0 mAP with 1%, 5%, 10% labeled data, respectively. Moreover, the proposed approach also proves to improve this detector trained on the COCO full set by +1.8 mAP by leveraging additional unlabelled data of COCO, achieving 42.7 mAP.

@InProceedings{Xu_2021_ICCV,
    author    = {Xu, Mengde and Zhang, Zheng and Hu, Han and Wang, Jianfeng and Wang, Lijuan and Wei, Fangyun and Bai, Xiang and Liu, Zicheng},
    title     = {End-to-End Semi-Supervised Object Detection With Soft Teacher},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3060-3069}
}


Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

Abstract: Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation. To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures. The Zen-Score represents the network expressivity and positively correlates with the model accuracy. The calculation of Zen-Score only takes a few forward inferences through a randomly initialized network, without training network parameters. Built upon the Zen-Score, we further propose a new NAS algorithm, termed as Zen-NAS, by maximizing the Zen-Score of the target network under given inference budgets. Within less than half GPU day, Zen-NAS is able to directly search high performance architectures in a data-free style. Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet. Searching and training code as well as pre-trained models are available from https://github.com/idstcv/ZenNAS.

@InProceedings{Lin_2021_ICCV,
    author    = {Lin, Ming and Wang, Pichao and Sun, Zhenhong and Chen, Hesen and Sun, Xiuyu and Qian, Qi and Li, Hao and Jin, Rong},
    title     = {Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {347-356}
}


Kun Yuan, Yiming Chen, Xinmeng Huang, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin

Abstract: The scale of deep learning nowadays calls for efficient distributed training algorithms. Decentralized momentum SGD (DmSGD), in which each node averages only with its neighbors, is more communication efficient than vanilla Parallel momentum SGD that incurs global average across all computing nodes. On the other hand, the large-batch training has been demonstrated critical to achieve runtime speedup. This motivates us to investigate how DmSGD performs in the large-batch scenario.  In this work, we find the momentum term can amplify the inconsistency bias in DmSGD. Such bias becomes more evident as batch-size grows large and hence results in severe performance degradation. We next propose DecentLaM, a novel decentralized large-batch momentum SGD to remove the momentum-incurred bias. The convergence rate for both strongly convex and non-convex scenarios is established. Our theoretical results justify the superiority of DecentLaM to DmSGD especially in the large-batch scenario. Experimental results on a a variety of computer vision tasks and models show that DecentLaM promises both efficient and high-quality training.

@InProceedings{Yuan_2021_ICCV,
    author    = {Yuan, Kun and Chen, Yiming and Huang, Xinmeng and Zhang, Yingya and Pan, Pan and Xu, Yinghui and Yin, Wotao},
    title     = {DecentLaM: Decentralized Momentum SGD for Large-Batch Deep Training},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3029-3039}
}


Julio Marco, Adrian Jarabo, Ji Hyun Nam, Xiaochun Liu, Miguel Ángel Cosculluela, Andreas Velten, Diego Gutierrez

Abstract: The light transport matrix (LTM) is an instrumental tool in line-of-sight (LOS) imaging, describing how light interacts with the scene and enabling applications such as relighting or separation of illumination components. We introduce a framework to estimate the LTM of non-line-of-sight (NLOS) scenarios, coupling recent virtual forward light propagation models for NLOS imaging with the LOS light transport equation. We design computational projector-camera setups, and use these virtual imaging systems to estimate the transport matrix of hidden scenes. We introduce the specific illumination functions to compute the different elements of the matrix, overcoming the challenging wide-aperture conditions of NLOS setups. Our NLOS light transport matrix allows us to (re)illuminate specific locations of a hidden scene, and separate direct, first-order indirect, and higher-order indirect illumination of complex cluttered hidden scenes, similar to existing LOS techniques.

@InProceedings{Marco_2021_ICCV,
    author    = {Marco, Julio and Jarabo, Adrian and Nam, Ji Hyun and Liu, Xiaochun and Cosculluela, Miguel \'Angel and Velten, Andreas and Gutierrez, Diego},
    title     = {Virtual Light Transport Matrices for Non-Line-of-Sight Imaging},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2440-2449}
}


Shuxian Liang, Xu Shen, Jianqiang Huang, Xian-Sheng Hua

Abstract: In this paper, we propose a novel solution for object-matching based semi-supervised video object segmentation, where the target object masks in the first frame are provided. Existing object-matching based methods focus on the matching between the raw object features of the current frame and the first/previous frames. However, two issues are still not solved by these object-matching based methods. As the appearance of the video object changes drastically over time, 1) unseen parts/details of the object present in the current frame, resulting in incomplete annotation in the first annotated frame (e.g., view/scale changes). 2) even for the seen parts/details of the object in the current frame, their positions change relatively (e.g., pose changes/camera motion), leading to a misalignment for the object matching. To obtain the complete information of the target object, we propose a novel object-based dynamic memory network that exploits visual contents of all the past frames. To solve the misalignment problem caused by position changes of visual contents, we propose an adaptive object alignment module by incorporating a region translation function that aligns object proposals towards templates in the feature space. Our method achieves state-of-the-art results on latest benchmark datasets DAVIS 2017 (J of 81.4% and F of 87.5% on the validation set) and YouTube-VOS (the overall score of 82.7% on the validation set) with a very efficient inference time (0.16 second/frame on DAVIS 2017 validation set). Code is available at: https://github.com/liang4sx/DMN-AOA.

@InProceedings{Liang_2021_ICCV,
    author    = {Liang, Shuxian and Shen, Xu and Huang, Jianqiang and Hua, Xian-Sheng},
    title     = {Video Object Segmentation With Dynamic Memory Networks and Adaptive Object Alignment},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {8065-8074}
}


Jérôme Rony, Eric Granger, Marco Pedersoli, Ismail Ben Ayed

Abstract: Adversarial attack algorithms are dominated by penalty methods, which are slow in practice, or more efficient distance-customized methods, which are heavily tailored to the properties of the considered distance. We propose a white-box attack algorithm to generate minimally perturbed adversarial examples based on Augmented Lagrangian principles. We bring several algorithmic modifications, which have a crucial effect on performance. Our attack enjoys the generality of penalty methods and the computational efficiency of distance-customized algorithms, and can be readily used for a wide set of distances. We compare our attack to state-of-the-art methods on three datasets and several models, and consistently obtain competitive performances with similar or lower computational complexity.

@InProceedings{Rony_2021_ICCV,
    author    = {Rony, J\'er\^ome and Granger, Eric and Pedersoli, Marco and Ben Ayed, Ismail},
    title     = {Augmented Lagrangian Adversarial Attacks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7738-7747}
}


Yunze Liu, Qingnan Fan, Shanghang Zhang, Hao Dong, Thomas Funkhouser, Li Yi

Abstract: This paper proposes a method for representation learning of multimodal data using contrastive losses. A traditional approach is to contrast different modalities to learn the information shared between them. However, that approach could fail to learn the complementary synergies between modalities that might be useful for downstream tasks. Another approach is to concatenate all the modalities into a tuple and then contrast positive and negative tuple correspondences. However, that approach could consider only the stronger modalities while ignoring the weaker ones. To address these issues, we propose a novel contrastive learning objective, TupleInfoNCE. It contrasts tuples based not only on positive and negative correspondences, but also by composing new negative tuples using modalities describing different scenes. Training with these additional negatives encourages the learning model to examine the correspondences among modalities in the same tuple, ensuring that weak modalities are not ignored. We provide a theoretical justification based on mutual-information for why this approach works, and we propose a sample optimization algorithm to generate positive and negative samples to maximize training efficacy. We find that TupleInfoNCE significantly outperforms previous state of the arts on three different downstream tasks.

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Yunze and Fan, Qingnan and Zhang, Shanghang and Dong, Hao and Funkhouser, Thomas and Yi, Li},
    title     = {Contrastive Multimodal Fusion With TupleInfoNCE},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {754-763}
}


Goutam Bhat, Martin Danelljan, Fisher Yu, Luc Van Gool, Radu Timofte

Abstract: We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction. Our approach thereby leverages the advantages of deep learning, while also benefiting from the principled multi-frame fusion provided by the classical MAP formulation. We validate our approach through comprehensive experiments on burst denoising and burst super-resolution datasets. Our approach sets a new state-of-the-art for both tasks, demonstrating the generality and effectiveness of the proposed formulation.

@InProceedings{Bhat_2021_ICCV,
    author    = {Bhat, Goutam and Danelljan, Martin and Yu, Fisher and Van Gool, Luc and Timofte, Radu},
    title     = {Deep Reparametrization of Multi-Frame Super-Resolution and Denoising},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2460-2470}
}


ViViT: A Video Vision Transformer

Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid

Abstract: We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks. To facilitate further research, we will release code and models.

@InProceedings{Arnab_2021_ICCV,
    author    = {Arnab, Anurag and Dehghani, Mostafa and Heigold, Georg and Sun, Chen and Lu\v{c}i\'c, Mario and Schmid, Cordelia},
    title     = {ViViT: A Video Vision Transformer},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6836-6846}
}


Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, Dani Lischinski

Abstract: Inspired by the ability of StyleGAN to generate highly re-alistic images in a variety of domains, much recent work hasfocused on understanding how to use the latent spaces ofStyleGAN to manipulate generated and real images. How-ever, discovering semantically meaningful latent manipula-tions typically involves painstaking human examination ofthe many degrees of freedom, or an annotated collectionof images for each desired manipulation. In this work, weexplore leveraging the power of recently introduced Con-trastive Language-Image Pre-training (CLIP) models in or-der to develop a text-based interface for StyleGAN imagemanipulation that does not require such manual effort. Wefirst introduce an optimization scheme that utilizes a CLIP-based loss to modify an input latent vector in response to auser-provided text prompt. Next, we describe a latent map-per that infers a text-guided latent manipulation step fora given input image, allowing faster and more stable text-based manipulation. Finally, we present a method for map-ping a text prompts to input-agnostic directions in Style-GAN's style space, enabling interactive text-driven imagemanipulation. Extensive results and comparisons demon-strate the effectiveness of our approaches.

@InProceedings{Patashnik_2021_ICCV,
    author    = {Patashnik, Or and Wu, Zongze and Shechtman, Eli and Cohen-Or, Daniel and Lischinski, Dani},
    title     = {StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2085-2094}
}


Weiwei Feng, Baoyuan Wu, Tianzhu Zhang, Yong Zhang, Yongdong Zhang

Abstract: Modern deep neural networks are often vulnerable to adversarial examples. Most exist attack methods focus on crafting adversarial examples in the digital domain, while only limited works study physical adversarial attack. However, it is more challenging to generate effective adversarial examples in the physical world due to many uncontrollable physical dynamics. Most current physical attack methods aim to generate robust physical adversarial examples by simulating all possible physical dynamics. When attacking new images or new DNN models, they require expensive manually efforts for simulating physical dynamics and considerable time for iteratively optimizing for each image. To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images. To the best of our knowledge, this is the first work to formulate the physical attack as a few-shot learning problem. Here, the training task is redefined as the composition of a support set, a query set, and a target DNN model. Under the few- shot setting, we design a novel class-agnostic and model-agnostic meta-learning algorithm to enhance the generalization ability of our method. Extensive experimental results on two benchmark datasets with four challenging experimental settings verify the superior robustness and generalization of our method by comparing to state-of-the-art physical attack methods.

@InProceedings{Feng_2021_ICCV,
    author    = {Feng, Weiwei and Wu, Baoyuan and Zhang, Tianzhu and Zhang, Yong and Zhang, Yongdong},
    title     = {Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7787-7796}
}


Bruno Lecouat, Jean Ponce, Julien Mairal

Abstract: This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time. Key challenges for solving this   super-resolution  problem include (i) aligning the input pictures with sub-pixel accuracy, (ii) handling raw (noisy) images for maximal faithfulness to native camera data, and (iii) designing/learning an image prior (regularizer) well suited to the task. We address these three challenges with a hybrid algorithm building on the insight from Wronski et al. that aliasing is an ally in this setting, with parameters that can be learned end to end, while retaining the interpretability of classical approaches to inverse problems. The effectiveness of our approach is demonstrated on synthetic and real image bursts, setting a new state of the art on several benchmarks and delivering excellent qualitative results on real raw bursts captured by smartphones and prosumer cameras.

@InProceedings{Lecouat_2021_ICCV,
    author    = {Lecouat, Bruno and Ponce, Jean and Mairal, Julien},
    title     = {Lucas-Kanade Reloaded: End-to-End Super-Resolution From Raw Image Bursts},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2370-2379}
}


Mengyang Pu, Yaping Huang, Qingji Guan, Haibin Ling

Abstract: As a fundamental building block in computer vision, edges can be categorised into four types according to the discontinuity in surface-Reflectance, Illumination, surface-Normal or Depth. While great progress has been made in detecting generic or individual types of edges, it remains under-explored to comprehensively study all four edge types together. In this paper, we propose a novel neural network solution, RINDNet, to jointly detect all four types of edges. Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages. In stage I, RINDNet uses a common backbone to extract features shared by all edges. Then in stage II it branches to prepare discriminative features for each edge type by the corresponding decoder. In stage III, an independent decision head for each type aggregates the features from previous stages to predict the initial results. Additionally, an attention module learns attention maps for all types to capture the underlying relations between them, and these maps are combined with initial results to generate the final edge detection results. For training and evaluation, we construct the first public benchmark, BSDS-RIND, with all four types of edges carefully annotated. In our experiments, RINDNet yields promising results in comparison with state-of-the-art methods. Additional analysis is presented in supplementary material.

@InProceedings{Pu_2021_ICCV,
    author    = {Pu, Mengyang and Huang, Yaping and Guan, Qingji and Ling, Haibin},
    title     = {RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6879-6888}
}


Guangyuan Zhou, Huiqun Wang, Jiaxin Chen, Di Huang

Abstract: RGB-D based 6D pose estimation has recently achieved remarkable progress, but still suffers from two major limitations: (1) ineffective representation of depth data and (2) insufficient integration of different modalities. This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN), to simultaneously address the issues above in a unified way. It first introduces the Point Refinement Network (PRN) to polish 3D point clouds, recovering missing parts with noise removed. Subsequently, the Multi-Modal Fusion Graph Convolutional Network (MMF-GCN) is presented to strengthen RGB-D combination, which captures geometry-aware inter-modality correlation through local information propagation in the graph convolutional network. Extensive experiments are conducted on three widely used benchmarks, and state-of-the-art performance is reached. Besides, it is also shown that the proposed PRN and MMF-GCN modules are well generalized to other frameworks.

@InProceedings{Zhou_2021_ICCV,
    author    = {Zhou, Guangyuan and Wang, Huiqun and Chen, Jiaxin and Huang, Di},
    title     = {PR-GCN: A Deep Graph Convolutional Network With Point Refinement for 6D Pose Estimation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2793-2802}
}


Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Elisa Ricci

Abstract: Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark, in particular on the commonly reported validation split. This generated a distorted impression about the superiority of Pseudo-LiDAR-based (PL-based) approaches over methods working with RGB images only. Our first contribution consists in rectifying this view by pointing out and showing experimentally that the validation results published by PL-based methods are substantially biased. The source of the bias resides in an overlap between the KITTI3D object detection validation set and the training/validation sets used to train depth predictors feeding PL-based methods. Surprisingly, the bias remains also after geographically removing the overlap. This leaves the test set as the only reliable set for comparison, where published PL-based methods do not excel. Our second contribution brings PL-based methods back up in the ranking with the design of a novel deep architecture which introduces a 3D confidence prediction module. We show that 3D confidence estimation techniques derived from RGB-only 3D detection approaches can be successfully integrated into our framework and, more importantly, that improved performance can be obtained with a newly designed 3D confidence measure, leading to state-of-the-art performance on the KITTI3D benchmark.

@InProceedings{Simonelli_2021_ICCV,
    author    = {Simonelli, Andrea and Bul\`o, Samuel Rota and Porzi, Lorenzo and Kontschieder, Peter and Ricci, Elisa},
    title     = {Are We Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3225-3233}
}


Eric Brachmann, Martin Humenberger, Carsten Rother, Torsten Sattler

Abstract: Benchmark datasets that measure camera pose accuracy have driven progress in visual re-localisation research. To obtain poses for thousands of images, it is common to use a reference algorithm to generate pseudo ground truth. Popular choices include Structure-from-Motion (SfM) and Simultaneous-Localisation-and-Mapping (SLAM) using additional sensors like depth cameras if available. Re-localisation benchmarks thus measure how well each method replicates the results of the reference algorithm. This begs the question whether the choice of the reference algorithm favours a certain family of re-localisation methods. This paper analyzes two widely used re-localisation datasets and shows that evaluation outcomes indeed vary with the choice of the reference algorithm. We thus question common beliefs in the re-localisation literature, namely that learning-based scene coordinate regression outperforms classical feature-based methods, and that RGB-D- based methods outperform RGB-based methods. We argue that any claims on ranking re-localisation methods should take the type of the reference algorithm, and the similarity of the methods to the reference algorithm, into account.

@InProceedings{Brachmann_2021_ICCV,
    author    = {Brachmann, Eric and Humenberger, Martin and Rother, Carsten and Sattler, Torsten},
    title     = {On the Limits of Pseudo Ground Truth in Visual Camera Re-Localisation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6218-6228}
}


Da Chen, Laurent D. Cohen, Jean-Marie Mirebeau, Xue-Cheng Tai

Abstract: The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios. Existing geodesic-based segmentation approaches usually exploit the image features in conjunction with geometric regularization terms (such as curve length or elastica length) for computing geodesic paths. In this paper, we consider a more complicated problem: finding simple and closed geodesic curves which are imposed a convexity shape prior. The proposed approach relies on an orientation-lifting strategy, by which a planar curve can be mapped to an high-dimensional orientation space. The convexity shape prior serves as a constraint for the construction of local metrics. The geodesic curves in the lifted space then can be efficiently computed through the fast marching method. In addition, we introduce a way to incorporate region-based homogeneity features into the proposed geodesic model so as to solve the region-based segmentation issues with shape prior constraints.

@InProceedings{Chen_2021_ICCV,
    author    = {Chen, Da and Cohen, Laurent D. and Mirebeau, Jean-Marie and Tai, Xue-Cheng},
    title     = {An Elastica Geodesic Approach With Convexity Shape Prior},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6900-6909}
}


Songhua Liu, Tianwei Lin, Dongliang He, Fu Li, Meiling Wang, Xin Li, Zhengxing Sun, Qian Li, Errui Ding

Abstract: Fast arbitrary neural style transfer has attracted widespread attention from academic, industrial and art communities due to its flexibility in enabling various applications. Existing solutions either attentively fuse deep style feature into deep content feature without considering feature distributions, or adaptively normalize deep content feature according to the style such that their global statistic information is matched. Although effective, leaving shallow feature unexplored or without locally considering feature statistics, they are prone to suffer from unnatural output with unpleasing local distortions. To alleviate this problem, in this paper, we propose a novel Adaptive Attention Normalization (AdaAttN) module to adaptively perform attentive normalization on per-point basis. Specifically, spatial attention score is learnt from both shallow and deep features of content and style images. Then per-point weighted statistics are calculated by regarding a style feature point as a distribution of attention-weighted output of all style feature points. Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics. Besides, a novel local feature loss is derived based on AdaAttN to enhance local visual quality. We also extend AdaAttN to be ready for video style transfer with slight modifications. Extensive experiments demonstrate that our method achieves state-of-the-art arbitrary image/video style transfer. Codes and models will be available.

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Songhua and Lin, Tianwei and He, Dongliang and Li, Fu and Wang, Meiling and Li, Xin and Sun, Zhengxing and Li, Qian and Ding, Errui},
    title     = {AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6649-6658}
}


Shi-Xue Zhang, Xiaobin Zhu, Chun Yang, Hongfa Wang, Xu-Cheng Yin

Abstract: Arbitrary shape text detection is a challenging task due to the high complexity and variety of scene texts. In this work, we propose a novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitrary shape text without any post-processing. Our method mainly consists of a boundary proposal model and an innovative adaptive boundary deformation model. The boundary proposal model constructed by multi-layer dilated convolutions is adopted to produce prior information (including classification map, distance field, and direction field) and coarse boundary proposals. The adaptive boundary deformation model is an encoder-decoder network, in which the encoder mainly consists of a Graph Convolutional Network (GCN) and a Recurrent Neural Network (RNN). It aims to perform boundary deformation in an iterative way for obtaining text instance shape guided by prior information from the boundary proposal model. In this way, our method can directly and efficiently generate accurate text boundaries without complex post-processing. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method.

@InProceedings{Zhang_2021_ICCV,
    author    = {Zhang, Shi-Xue and Zhu, Xiaobin and Yang, Chun and Wang, Hongfa and Yin, Xu-Cheng},
    title     = {Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1305-1314}
}


Tiantian Wang, Sifei Liu, Yapeng Tian, Kai Li, Ming-Hsuan Yang

Abstract: Learning temporally consistent foreground opacity from videos, i.e., video matting, has drawn great attention due to the blossoming of video conferencing. Previous approaches are built on top of image matting models, which fail in maintaining the temporal coherence when being adapted to videos. They either utilize the optical flow to smooth frame-wise prediction, where the performance is dependent on the selected optical flow model; or naively combine feature maps from multiple frames, which does not model well the correspondence of pixels in adjacent frames. In this paper, we propose to enhance the temporal coherence by Consistency-Regularized Graph Neural Networks (CRGNN) with the aid of a synthesized video matting dataset. CRGNN utilizes Graph Neural Networks (GNN) to relate adjacent frames such that pixels or regions that are incorrectly predicted in one frame can be corrected by leveraging information from its neighboring frames. To generalize our model from synthesized videos to real-world videos, we propose a consistency regularization technique to enforce the consistency on the alpha and foreground when blending them with different backgrounds. To evaluate the efficacy of CRGNN, we further collect a real-world dataset with annotated alpha mattes. Compared with state-of-the-art methods that require hand-crafted trimaps or backgrounds for modeling training, CRGNN generates favorably results with the help of unlabeled real training dataset.

@InProceedings{Wang_2021_ICCV,
    author    = {Wang, Tiantian and Liu, Sifei and Tian, Yapeng and Li, Kai and Yang, Ming-Hsuan},
    title     = {Video Matting via Consistency-Regularized Graph Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4902-4911}
}


Isinsu Katircioglu, Helge Rhodin, Jörg Spörri, Mathieu Salzmann, Pascal Fua

Abstract: Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data. However, existing approaches predominantly rely on restrictive assumptions on appearance and motion. For scenes with dynamic activities and camera motion, we propose a multi-camera framework in which geometric constraints are embedded in the form of multi-view consistency during training via coarse 3D localization in a voxel grid and fine-grained offset regression. In this manner, we learn a joint distribution of proposals over multiple views. At inference time, our method operates on single RGB images. We outperform state-of-the-art techniques both on images that visually depart from those of standard benchmarks and on those of the classical Human3.6M dataset.

@InProceedings{Katircioglu_2021_ICCV,
    author    = {Katircioglu, Isinsu and Rhodin, Helge and Sp\"orri, J\"org and Salzmann, Mathieu and Fua, Pascal},
    title     = {Human Detection and Segmentation via Multi-View Consensus},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2855-2864}
}


Abstract: Rolling shutter (RS) images can be viewed as the result of the row-wise combination of global shutter (GS) images captured by a virtual moving GS camera over the period of camera readout time. The RS effect brings tremendous difficulties for the downstream applications. In this paper, we propose to invert the above RS imaging mechanism, i.e., recovering a high framerate GS video from consecutive RS images to achieve RS temporal super-resolution (RSSR). This extremely challenging problem, e.g., recovering 1440 GS images from two 720-height RS images, is far from being solved end-to-end. To address this challenge, we exploit the geometric constraint in the RS camera model, thus achieving geometry-aware inversion. Specifically, we make three contributions in resolving the above difficulties: (i) formulating the bidirectional RS undistortion flows under the constant velocity motion model, (ii) building the connection between the RS undistortion flow and optical flow via a scaling operation, and (iii) developing a mutual conversion scheme between varying RS undistortion flows that correspond to different scanlines. Building upon these formulations, we propose the first RS temporal super-resolution network in a cascaded structure to extract high framerate global shutter video. Our method explores the underlying spatio-temporal geometric relationships within a deep learning framework, where no extra supervision besides the middle-scanline ground truth GS image is needed. Essentially, our method can be very efficient for explicit propagation to generate GS images under any scanline. Experimental results on both synthetic and real data show that our method can produce high-quality GS image sequences with rich details, outperforming state-of-the-art methods.

@InProceedings{Fan_2021_ICCV,
    author    = {Fan, Bin and Dai, Yuchao},
    title     = {Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4228-4237}
}


Yi Guo, Huan Yuan, Jianchao Tan, Zhangyang Wang, Sen Yang, Ji Liu

Abstract: Model compression techniques are recently gaining explosive attention for obtaining efficient AI models for various real time applications. Channel pruning is one important compression strategy, and widely used in slimming various DNNs. Previous gate-based or importance-based pruning methods aim to remove channels whose "importance" are smallest. However, it remains unclear what criteria the channel importance should be measured on, leading to various channel selection heuristics. Some other sampling-based pruning methods deploy sampling strategy to train sub-nets, which often causes the training instability and the compressed model's degraded performance. In view of the research gaps, we present a new module named Gates with Differentiable Polarization (GDP), inspired by principled optimization ideas. GDP can be plugged before convolutional layers without bells and whistles, to control the on-and-off of each channel or whole layer block. During the training process, the polarization effect will drive a subset of gates to smoothly decrease to exactly zero, while other gates gradually stay away from zero by a large margin. When training terminates, those zero-gated channels can be painlessly removed, while other non-zero gates can be absorbed into the succeeding convolution kernel, causing completely no interruption to training nor damage to the trained model. Experiments conducted over CIFAR-10 and ImageNet datasets show that the proposed GDP algorithm achieves the state-of-the-art performance on various benchmark DNNs at a broad range of pruning ratios. We also apply GDP to DeepLabV3Plus-ResNet50 on the challenging Pascal VOC segmentation task, whose test performance sees no drop (even slightly improved) with over 60% FLOPs saving.

@InProceedings{Guo_2021_ICCV,
    author    = {Guo, Yi and Yuan, Huan and Tan, Jianchao and Wang, Zhangyang and Yang, Sen and Liu, Ji},
    title     = {GDP: Stabilized Neural Network Pruning via Gates With Differentiable Polarization},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5239-5250}
}


Multimodal Knowledge Expansion

Zihui Xue, Sucheng Ren, Zhengqi Gao, Hang Zhao

Abstract: The popularity of multimodal sensors and the accessibility of the Internet have brought us a massive amount of unlabeled multimodal data. Since existing datasets and well-trained models are primarily unimodal, the modality gap between a unimodal network and unlabeled multimodal data poses an interesting problem: how to transfer a pre-trained unimodal network to perform the same task on unlabeled multimodal data? In this work, we propose multimodal knowledge expansion (MKE), a knowledge distillation-based framework to effectively utilize multimodal data without requiring labels. Opposite to traditional knowledge distillation, where the student is designed to be lightweight and inferior to the teacher, we observe that the multimodal student model consistently rectifies pseudo labels and generalizes better than its teacher. Extensive experiments on four tasks and different modalities verify this finding. Furthermore, we connect the mechanism of MKE to semi-supervised learning and offer both empirical and theoretical explanations to understand the expansion capability of a multimodal student.

@InProceedings{Xue_2021_ICCV,
    author    = {Xue, Zihui and Ren, Sucheng and Gao, Zhengqi and Zhao, Hang},
    title     = {Multimodal Knowledge Expansion},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {854-863}
}


Tetsuya Tanaka, Yukihiro Sasagawa, Takayuki Okatani

Abstract: Bundle adjustment (BA) occupies a large portion of SfM and visual SLAM's total execution time. Local BA over the latest several keyframes plays a crucial role in visual SLAM. Its execution time should be sufficiently short for robust tracking; this is especially critical for embedded systems with a limited computational resource. This study proposes a learning-based method using a graph network that can replace conventional optimization-based BA and works faster. The graph network operates on a graph consisting of the nodes of keyframes and landmarks and the edges of the latter's visibility from the former. The graph network receives the parameters' initial values as inputs and predicts the updates to their optimal values. We design an intermediate representation of inputs inspired by the normal equation of the Levenberg-Marquardt method. We use the sum of reprojection errors as a loss function to train the graph network. The experiments show that the proposed method outputs parameter estimates with slightly inferior accuracy in 1/60-1/10 of time compared with the conventional BA.

@InProceedings{Tanaka_2021_ICCV,
    author    = {Tanaka, Tetsuya and Sasagawa, Yukihiro and Okatani, Takayuki},
    title     = {Learning To Bundle-Adjust: A Graph Network Approach to Faster Optimization of Bundle Adjustment for Vehicular SLAM},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6250-6259}
}


Cheng Zhang, Tai-Yu Pan, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

Abstract: Many objects do not appear frequently enough in complex scenes (e.g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e.g., in product images). Yet, these object-centric images are not effectively leveraged for improving object detection in scene-centric images. In this paper, we propose Mosaic of Object-centric images as Scene-centric images (MosaicOS), a simple and novel framework that is surprisingly effective at tackling the challenges of long-tailed object detection. Keys to our approach are three-fold: (i) pseudo scene-centric image construction from object-centric images for mitigating domain differences, (ii) high-quality bounding box imputation using the object-centric images' class labels, and (iii) a multi-stage training procedure. On LVIS object detection (and instance segmentation), MosaicOS leads to a massive 60% (and 23%) relative improvement in average precision for rare object categories. We also show that our framework can be compatibly used with other existing approaches to achieve even further gains. Our pre-trained models are publicly available at https://github.com/czhang0528/MosaicOS/.

@InProceedings{Zhang_2021_ICCV,
    author    = {Zhang, Cheng and Pan, Tai-Yu and Li, Yandong and Hu, Hexiang and Xuan, Dong and Changpinyo, Soravit and Gong, Boqing and Chao, Wei-Lun},
    title     = {MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {417-427}
}


Wei Shang, Dongwei Ren, Dongqing Zou, Jimmy S. Ren, Ping Luo, Wangmeng Zuo

Abstract: Recently, video deblurring has attracted considerable research attention, and several works suggest that events at high time rate can benefit deblurring. In this paper, we develop a principled framework D2Nets for video deblurring to exploit non-consecutively blurry frames, and propose a flexible event fusion module (EFM) to bridge the gap between event-driven and video deblurring. In D2Nets, we propose to first detect nearest sharp frames (NSFs) using a bidirectional LSTM detector, and then perform deblurring guided by NSFs. Furthermore, the proposed EFM is flexible to be incorporated into D2Nets, in which events can be leveraged to notably boost the deblurring performance. EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods. On synthetic and real-world blurry datasets, our methods achieve better results than competing methods, and EFM not only benefits D2Nets but also significantly improves the competing deblurring networks.

@InProceedings{Shang_2021_ICCV,
    author    = {Shang, Wei and Ren, Dongwei and Zou, Dongqing and Ren, Jimmy S. and Luo, Ping and Zuo, Wangmeng},
    title     = {Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4531-4540}
}


Shiqi Chen, Huajun Feng, Keming Gao, Zhihai Xu, Yueting Chen

Abstract: To meet the space limitation of optical elements, free-form surfaces or high-order aspherical lenses are adopted in mobile cameras to compress volume. However, the application of free-form surfaces also introduces the problem of image quality mutation. Existing model-based deconvolution methods are inefficient in dealing with the degradation that shows a wide range of spatial variants over regions. And the deep learning techniques in low-level and physics-based vision suffer from a lack of accurate data. To address this issue, we develop a degradation framework to estimate the spatially variant point spread functions (PSFs) of mobile cameras. When input extreme-quality digital images, the proposed framework generates degraded images sharing a common domain with real-world photographs. Supplied with the synthetic image pairs, we design a Field-Of-View shared kernel prediction network (FOV-KPN) to perform spatial-adaptive reconstruction on real degraded photos. Extensive experiments demonstrate that the proposed approach achieves extreme-quality computational imaging and outperforms the state-of-the-art methods. Furthermore, we illustrate that our technique can be integrated into existing postprocessing systems, resulting in significantly improved visual quality.

@InProceedings{Chen_2021_ICCV,
    author    = {Chen, Shiqi and Feng, Huajun and Gao, Keming and Xu, Zhihai and Chen, Yueting},
    title     = {Extreme-Quality Computational Imaging via Degradation Framework},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2632-2641}
}


Abstract: In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e.g., cross-resolution and insufficient lighting. Due to the limited variation range of training data, existing models are difficult to generalize to scenes with unknown perceptual interference types. To address the above problem, in this paper, we propose two disjoint data-generation ways to complement existing training samples to improve the robustness of Re-ID models. Firstly, considering the sparsity and imbalance of samples in the perceptual space, a dense resampling method from the estimated perceptual distribution is performed. Secondly, to dig more representative generated samples for identity representation learning, we introduce a graph-based white-box attacker to guide the data generation process with intra-batch ranking and discriminate attention. In addition, two synthetic-to-real feature constraints are introduced into the Re-ID training to prevent the generated data from bringing domain bias. Our method is effective, easy-to-implement, and independent of the specific network architecture. Applying our approach to a ResNet-50 baseline can already achieve competitive results, surpassing state-of-the-art methods by +1.2% at Rank-1 on the MLR-CUHK03 dataset.

@InProceedings{Huang_2021_ICCV,
    author    = {Huang, Yukun and Fu, Xueyang and Zha, Zheng-Jun},
    title     = {Attack-Guided Perceptual Data Generation for Real-World Re-Identification},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {215-224}
}


Mingfei Cheng, Kaili Zhao, Xuhong Guo, Yajing Xu, Jun Guo

Abstract: Curvilinear structure segmentation (CSS) is under semantic segmentation, whose applications include crack detection, aerial road extraction, and biomedical image segmentation. In general, geometric topology and pixel-wise features are two critical aspects of CSS. However, most semantic segmentation methods only focus on enhancing feature representations while existing CSS techniques emphasize preserving topology alone. In this paper, we present a Joint Topology-preserving and Feature-refinement Network (JTFN) that jointly models global topology and refined features based on an iterative feedback learning strategy. Specifically, we explore the structure of objects to help preserve corresponding topologies of predicted masks, thus design a reciprocative two-stream module for CSS and boundary detection. In addition, we introduce such topology-aware predictions as feedback guidance that refines attentive features by supplementing and enhancing saliencies. To the best of our knowledge, this is the first work that jointly addresses topology preserving and feature refinement for CSS. We evaluate JTFN on four datasets of diverse applications: Crack500, CrackTree200, Roads, and DRIVE. Results show that JTFN performs best in comparison with alternative methods. Code is available.

@InProceedings{Cheng_2021_ICCV,
    author    = {Cheng, Mingfei and Zhao, Kaili and Guo, Xuhong and Xu, Yajing and Guo, Jun},
    title     = {Joint Topology-Preserving and Feature-Refinement Network for Curvilinear Structure Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7147-7156}
}


Shuang Li, Yilun Du, Antonio Torralba, Josef Sivic, Bryan Russell

Abstract: We introduce the task of weakly supervised learning for detecting human and object interactions in videos. Our task poses unique challenges as a system does not know what types of human-object interactions are present in a video or the actual spatiotemporal location of the human and object. To address these challenges, we introduce a contrastive weakly supervised training loss that aims to jointly associate spatiotemporal regions in a video with an action and object vocabulary and encourage temporal continuity of the visual appearance of moving objects as a form of self-supervision. To train our model, we introduce a dataset comprising over 6.5k videos with human-object interaction annotations that have been semi-automatically curated from sentence captions associated with the videos. We demonstrate improved performance over weakly supervised baselines adapted to our task on our video dataset.

@InProceedings{Li_2021_ICCV,
    author    = {Li, Shuang and Du, Yilun and Torralba, Antonio and Sivic, Josef and Russell, Bryan},
    title     = {Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1845-1855}
}


Wamiq Para, Paul Guerrero, Tom Kelly, Leonidas J. Guibas, Peter Wonka

Abstract: We propose a new generative model for layout generation. We generate layouts in three steps. First, we generate the layout elements as nodes in a layout graph. Second, we compute constraints between layout elements as edges in the layout graph. Third, we solve for the final layout using constrained optimization. For the first two steps, we build on recent transformer architectures. The layout optimization implements the constraints efficiently. We show three practical contributions compared to the state of the art: our work requires no user input, produces higher quality layouts, and enables many novel capabilities for conditional layout generation.

@InProceedings{Para_2021_ICCV,
    author    = {Para, Wamiq and Guerrero, Paul and Kelly, Tom and Guibas, Leonidas J. and Wonka, Peter},
    title     = {Generative Layout Modeling Using Constraint Graphs},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6690-6700}
}


Ayush Chopra, Rishabh Jain, Mayur Hemani, Balaji Krishnamurthy

Abstract: Image-based virtual try-on involves synthesizing perceptually convincing images of a model wearing a particular garment and has garnered significant research interest due to its immense practical applicability. Recent methods involve a two-stage process: i) warping of the garment to align with the model ii) texture fusion of the warped garment and target model to generate the try-on output. Issues arise due to the non-rigid nature of garments and the lack of geometric information about the model or the garment. It often results in improper rendering of granular details. We propose ZFlow, an end-to-end framework, which seeks to alleviate these concerns regarding geometric and textural integrity (such as pose, depth-ordering, skin and neckline reproduction) through a combination of gated aggregation of hierarchical flow estimates termed Gated Appearance Flow, and dense structural priors at various stage of the network. ZFlow achieves state-of-the-art results as observed qualitatively, and on benchmark image quality measures (PSNR, SSIM, and FID scores). The paper also presents extensive comparisons with existing state-of-the-art including a detailed user study and ablation studies to gauge the effectiveness of each of our contributions on multiple datasets

@InProceedings{Chopra_2021_ICCV,
    author    = {Chopra, Ayush and Jain, Rishabh and Hemani, Mayur and Krishnamurthy, Balaji},
    title     = {ZFlow: Gated Appearance Flow-Based Virtual Try-On With 3D Priors},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5433-5442}
}


Jiaming Liu, Ming Lu, Kaixin Chen, Xiaoqi Li, Shizun Wang, Zhaoqing Wang, Enhua Wu, Yurong Chen, Chuang Zhang, Ming Wu

Abstract: Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwidth. Deep Neural Networks (DNNs) are utilized to improve the quality of video delivery recently. These methods divide a video into chunks, and stream LR video chunks and corresponding content-aware models to the client. The client runs the inference of models to super-resolve the LR chunks. Consequently, a large number of models are streamed in order to deliver a video. In this paper, we first carefully study the relation between models of different chunks, then we tactfully design a joint training framework along with the Content-aware Feature Modulation (CaFM) layer to compress these models for neural video delivery. With our method, each video chunk only requires less than 1% of original parameters to be streamed, achieving even better SR performance. We conduct extensive experiments across various SR backbones, video time length, and scaling factors to demonstrate the advantages of our method. Besides, our method can be also viewed as a new approach of video coding. Our primary experiments achieve better video quality compared with the commercial H.264 and H.265 standard under the same storage cost, showing the great potential of the proposed method. Code is available at: https://github.com/Neural-video-delivery/CaFM-Pytorch-ICCV2021

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Jiaming and Lu, Ming and Chen, Kaixin and Li, Xiaoqi and Wang, Shizun and Wang, Zhaoqing and Wu, Enhua and Chen, Yurong and Zhang, Chuang and Wu, Ming},
    title     = {Overfitting the Data: Compact Neural Video Delivery via Content-Aware Feature Modulation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4631-4640}
}


Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, Chitta Baral

Abstract: Vision-and-language (V&L) reasoning necessitates perception of visual concepts such as objects and actions, understanding semantics and language grounding, and reasoning about the interplay between the two modalities. One crucial aspect of visual reasoning is spatial understanding, which involves understanding relative locations of objects, i.e. implicitly learning the geometry of the scene. In this work, we evaluate the faithfulness of V&L models to such geometric understanding, by formulating the prediction of pair-wise relative locations of objects as a classification as well as a regression task. Our findings suggest that state-of-the-art transformer-based V&L models lack sufficient abilities to excel at this task. Motivated by this, we design two objectives as proxies for 3D spatial reasoning (SR) -- object centroid estimation, and relative position estimation, and train V&L with weak supervision from off-the-shelf depth estimators. This leads to considerable improvements in accuracy for the "GQA" visual question answering challenge (in fully supervised, few-shot, and O.O.D settings) as well as improvements in relative spatial reasoning. Code and data will be released here.

@InProceedings{Banerjee_2021_ICCV,
    author    = {Banerjee, Pratyay and Gokhale, Tejas and Yang, Yezhou and Baral, Chitta},
    title     = {Weakly Supervised Relative Spatial Reasoning for Visual Question Answering},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1908-1918}
}


Hong-Yu Zhou, Chixiang Lu, Sibei Yang, Xiaoguang Han, Yizhou Yu

Abstract: Preserving maximal information is the basic principle of designing self-supervised learning methodologies. To reach this goal, contrastive learning adopts an implicit way which is contrasting image pairs. However, we believe it is not fully optimal to simply use the contrastive estimation for preservation. Moreover, it is necessary and complemental to introduce an explicit solution to preserve more information. From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations. Together with the contrastive loss, we present Preservational Contrastive Representation Learning (PCRL) for learning self-supervised medical representations. PCRL provides very competitive results under the pretraining-finetuning protocol, outperforming both self-supervised and supervised counterparts in 5 classification/segmentation tasks substantially.

@InProceedings{Zhou_2021_ICCV,
    author    = {Zhou, Hong-Yu and Lu, Chixiang and Yang, Sibei and Han, Xiaoguang and Yu, Yizhou},
    title     = {Preservational Learning Improves Self-Supervised Medical Image Models by Reconstructing Diverse Contexts},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3499-3509}
}


Zijian Yu, Xuhui Li, Huijuan Huang, Wen Zheng, Li Chen

Abstract: Image matting refers to the estimation of the opacity of foreground objects. It requires correct contours and fine details of foreground objects for the matting results. To better accomplish human image matting tasks, we propose the Cascade Image Matting Network with Deformable Graph Refinement(CasDGR), which can automatically predict precise alpha mattes from single human images without any additional inputs. We adopt a network cascade architecture to perform matting from low-to-high resolution, which corresponds to coarse-to-fine optimization. We also introduce the Deformable Graph Refinement (DGR) module based on graph neural networks (GNNs) to overcome the limitations of convolutional neural networks (CNNs). The DGR module can effectively capture long-range relations and obtain more global and local information to help produce finer alpha mattes. We also reduce the computation complexity of the DGR module by dynamically predicting the neighbors and apply DGR module to higher-resolution features. Experimental results demonstrate the ability of our CasDGR to achieve state-of-the-art performance on synthetic datasets and produce good results on real human images.

@InProceedings{Yu_2021_ICCV,
    author    = {Yu, Zijian and Li, Xuhui and Huang, Huijuan and Zheng, Wen and Chen, Li},
    title     = {Cascade Image Matting With Deformable Graph Refinement},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7167-7176}
}


Qingyu Song, Changan Wang, Zhengkai Jiang, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Yang Wu

Abstract: Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization based methods relying on intermediate representations (i.e., density maps or pseudo boxes) serving as learning targets are counter-intuitive and error-prone. In this paper, we propose a purely point-based framework for joint crowd counting and individual localization. For this framework, instead of merely reporting the absolute counting error at image level, we propose a new metric, called density Normalized Average Precision (nAP), to provide more comprehensive and more precise performance evaluation. Moreover, we design an intuitive solution under this framework, which is called Point to Point Network (P2PNet). P2PNet discards superfluous steps and directly predicts a set of point proposals to represent heads in an image, being consistent with the human annotation results. By thorough analysis, we reveal the key step towards implementing such a novel idea is to assign optimal learning targets for these proposals. Therefore, we propose to conduct this crucial association in an one-to-one matching manner using the Hungarian algorithm. The P2PNet not only significantly surpasses state-of-the-art methods on popular counting benchmarks, but also achieves promising localization accuracy. The codes will be available at: https://github.com/TencentYoutuResearch/CrowdCounting-P2PNet.

@InProceedings{Song_2021_ICCV,
    author    = {Song, Qingyu and Wang, Changan and Jiang, Zhengkai and Wang, Yabiao and Tai, Ying and Wang, Chengjie and Li, Jilin and Huang, Feiyue and Wu, Yang},
    title     = {Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3365-3374}
}


Thanh-Dat Truong, Chi Nhan Duong, The De Vu, Hoang Anh Pham, Bhiksha Raj, Ngan Le, Khoa Luu

Abstract: Turn-taking has played an essential role in structuring the regulation of a conversation. The task of identifying the main speaker (who is properly taking his/her turn of speaking) and the interrupters (who are interrupting or reacting to the main speaker's utterances) remains a challenging task. Although some prior methods have partially addressed this task, there still remain some limitations. Firstly, a direct association of Audio and Visual features may limit the correlations to be extracted due to different modalities. Secondly, the relationship across temporal segments helping to maintain the consistency of localization, separation and conversation contexts is not effectively exploited. Finally, the interactions between speakers that usually contain the tracking and anticipatory decisions about transition to a new speaker is usually ignored. Therefore, this work introduces a new Audio-Visual Transformer approach to the problem of localization and highlighting the main speaker in both audio and visual channels of a multi-speaker conversation video in the wild. The proposed method exploits different types of correlations presented in both visual and audio signals. The temporal audio-visual relationships across spatial-temporal space are anticipated and optimized via the self-attention mechanism in a Transformer structure. Moreover, a newly collected dataset is introduced for the main speaker detection. To the best of our knowledge, it is one of the first studies that is able to automatically localize and highlight the main speaker in both visual and audio channels in multi-speaker conversation videos.

@InProceedings{Truong_2021_ICCV,
    author    = {Truong, Thanh-Dat and Duong, Chi Nhan and De Vu, The and Pham, Hoang Anh and Raj, Bhiksha and Le, Ngan and Luu, Khoa},
    title     = {The Right To Talk: An Audio-Visual Transformer Approach},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1105-1114}
}


Wei Wang, Haochen Zhang, Zehuan Yuan, Changhu Wang

Abstract: Most existing convolution neural network (CNN) based super-resolution (SR) methods generate their paired training dataset by artificially synthesizing low-resolution (LR) images from the high-resolution (HR) ones. However, this dataset preparation strategy harms the application of these CNNs in real-world scenarios due to the inherent domain gap between the training and testing data. A popular attempts towards the challenge is unpaired generative adversarial networks, which generate "real" LR counterparts from real HR images using image-to-image translation and then perform super-resolution from "real" LR->SR. Despite great progress, it is still difficult to synthesize perfect "real" LR images for super-resolution. In this paper, we firstly consider the real-world SR problem from the traditional domain adaptation perspective. We propose a novel unpaired SR training framework based on feature distribution alignment, with which we can obtain degradation-indistinguishable feature maps and then map them to HR images. In order to generate better SR images for target LR domain, we introduce several regularization losses to force the aligned feature to locate around the target domain. Our experiments indicate that our SR network obtains the state-of-the-art performance over both blind and unpaired SR methods on diverse datasets.

@InProceedings{Wang_2021_ICCV,
    author    = {Wang, Wei and Zhang, Haochen and Yuan, Zehuan and Wang, Changhu},
    title     = {Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4318-4327}
}


Abstract: Black-box attacks aim to generate adversarial noise tofail the victim deep neural network in the black box. Thecentral task in black-box attack method design is to estimateand characterize the victim model in the high-dimensionalmodel space based on feedback results of queries submittedto the victim network. The central performance goal is tominimize the number of queries needed for successful at-tack. Existing attack methods directly search and refine theadversarial noise in an extremely high-dimensional space,requiring hundreds or even thousands queries to the victimnetwork. To address this challenge, we propose to explore aconsistency and sensitivity guided ensemble attack (CSEA)method in a low-dimensional space. Specifically, we esti-mate the victim model in the black box using a learned lin-ear composition of an ensemble of surrogate models withdiversified network structures. Using random block maskson the input image, these surrogate models jointly constructand submit randomized and sparsified queries to the victimmodel. Based on these query results and guided by a con-sistency constraint, the surrogate models can be trained us-ing a very small number of queries such that their learnedcomposition is able to accurately approximate the victimmodel in the high-dimensional space. The randomized andsparsified queries also provide important information for usto construct an attack sensitivity map for the input image,with which the adversarial attack can be locally refined tofurther increase its success rate. Our extensive experimen-tal results demonstrate that our proposed approach signifi-cantly reduces the number of queries to the victim networkwhile maintaining very high success rates, outperformingexisting black-box attack methods by large margins.

@InProceedings{Yuan_2021_ICCV,
    author    = {Yuan, Jianhe and He, Zhihai},
    title     = {Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7778-7786}
}