Kun Yuan, Quanquan Li, Shaopeng Guo, Dapeng Chen, Aojun Zhou, Fengwei Yu, Ziwei Liu

Abstract: A standard practice of deploying deep neural networks is to apply the same architecture to all the input instances. However, a fixed architecture may not be suitable for different data with high diversity. To boost the model capacity, existing methods usually employ larger convolutional kernels or deeper network layers, which incurs prohibitive computational costs. In this paper, we address this issue by proposing Differentiable Dynamic Wirings (DDW), which learns the instance-aware connectivity that creates different wiring patterns for different instances. 1) Specifically, the network is initialized as a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent the connection paths. 2) We generate edge weights by a learnable module, Router, and select the edges whose weights are larger than a threshold, to adjust the connectivity of the neural network structure. 3) Instead of using the same path of the network, DDW aggregates features dynamically in each node, which allows the network to have more representation power. To facilitate effective training, we further represent the network connectivity of each sample as an adjacency matrix. The matrix is updated to aggregate features in the forward pass, cached in the memory, and used for gradient computing in the backward pass. We validate the effectiveness of our approach with several mainstream architectures, including MobileNetV2, ResNet, ResNeXt, and RegNet. Extensive experiments are performed on ImageNet classification and COCO object detection, which demonstrates the effectiveness and generalization ability of our approach.

@InProceedings{Yuan_2021_ICCV,
    author    = {Yuan, Kun and Li, Quanquan and Guo, Shaopeng and Chen, Dapeng and Zhou, Aojun and Yu, Fengwei and Liu, Ziwei},
    title     = {Differentiable Dynamic Wirings for Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {327-336}
}


Abstract: Joint Energy-based Model (JEM) is a recently proposed hybrid model that retains strong discriminative power of modern CNN classifiers, while generating samples rivaling the quality of GAN-based approaches. In this paper, we propose a variety of new training procedures and architecture features to improve JEM's accuracy, training stability, and speed altogether. 1) We propose a proximal SGLD to generate samples in the proximity of samples from previous step, which improves the stability. 2) We further treat the approximate maximum likelihood learning of EBM as a multi-step differential game, and extend the YOPO framework to cut out redundant calculations during backpropagation, which accelerates the training substantially. 3) Rather than initializing SGLD chain from random noise, we introduce a new informative initialization that samples from a distribution estimated from training data. 4) This informative initialization allows us to enable batch normalization in JEM, which further releases the power of modern CNN architectures for hybrid modeling.

@InProceedings{Yang_2021_ICCV,
    author    = {Yang, Xiulong and Ji, Shihao},
    title     = {JEM++: Improved Techniques for Training JEM},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6494-6503}
}


Yukun Su, Ruizhou Sun, Guosheng Lin, Qingyao Wu

Abstract: Data augmentation is vital for deep learning neural networks. By providing massive training samples, it helps to improve the generalization ability of the model. Weakly supervised semantic segmentation (WSSS) is a challenging problem that has been deeply studied in recent years, conventional data augmentation approaches for WSSS usually employ geometrical transformations, random cropping, and color jittering. However, merely increasing the same contextual semantic data does not bring much gain to the networks to distinguish the objects, e.g., the correct image-level classification of "aeroplane" may be not only due to the recognition of the object itself but also its co-occurrence context like "sky", which will cause the model to focus less on the object features. To this end, we present a Context Decoupling Augmentation (CDA) method, to change the inherent context in which the objects appear and thus drive the network to remove the dependence between object instances and contextual information. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.

@InProceedings{Su_2021_ICCV,
    author    = {Su, Yukun and Sun, Ruizhou and Lin, Guosheng and Wu, Qingyao},
    title     = {Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7004-7014}
}


Chaofei Wang, Jiayu Xiao, Yizeng Han, Qisen Yang, Shiji Song, Gao Huang

Abstract: The backbone of traditional CNN classifier is generally considered as a feature extractor, followed by a linear layer which performs the classification. We propose a novel loss function, termed as CAM-loss, to constrain the embedded feature maps with the class activation maps (CAMs) which indicate the spatially discriminative regions of an image for particular categories. CAM-loss drives the backbone to express the features of target category and suppress the features of non-target categories or background, so as to obtain more discriminative feature representations. It can be simply applied in any CNN architecture with neglectable additional parameters and calculations. Experimental results show that CAM-loss is applicable to a variety of network structures and can be combined with mainstream regularization methods to improve the performance of image classification. The strong generalization ability of CAM-loss is validated in the transfer learning and few shot learning tasks. Based on CAM-loss, we also propose a novel CAAM-CAM matching knowledge distillation method. This method directly uses the CAM generated by the teacher network to supervise the CAAM generated by the student network, which effectively improves the accuracy and convergence rate of the student network.

@InProceedings{Wang_2021_ICCV,
    author    = {Wang, Chaofei and Xiao, Jiayu and Han, Yizeng and Yang, Qisen and Song, Shiji and Huang, Gao},
    title     = {Towards Learning Spatially Discriminative Feature Representations},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1326-1335}
}


Abstract: Scene understanding under low-light conditions is a challenging problem. This is due to the small number of photons captured by the camera and the resulting low signal-to-noise ratio (SNR). Single-photon cameras (SPCs) are an emerging sensing modality that are capable of capturing images with high sensitivity. Despite having minimal read-noise, images captured by SPCs in photon-starved conditions still suffer from strong shot noise, preventing reliable scene inference. We propose photon scale-space -- a collection of high-SNR images spanning a wide range of photons-per-pixel (PPP) levels (but same scene content) as guides to train inference model on low photon flux images. We develop training techniques that push images with different illumination levels closer to each other in feature representation space. The key idea is that having a spectrum of different brightness levels during training enables effective guidance, and increases robustness to shot noise even in extreme noise cases. Based on the proposed approach, we demonstrate, via simulations and real experiments with a SPAD camera, high-performance on various inference tasks such as image classification and monocular depth estimation under ultra low-light, down to <1 PPP.

@InProceedings{Goyal_2021_ICCV,
    author    = {Goyal, Bhavya and Gupta, Mohit},
    title     = {Photon-Starved Scene Inference Using Single Photon Cameras},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2512-2521}
}


Baifeng Shi, Qi Dai, Judy Hoffman, Kate Saenko, Trevor Darrell, Huijuan Xu

Abstract: Training temporal action detection in videos requires large amounts of labeled data, yet such annotation is expensive to collect. Incorporating unlabeled or weakly-labeled data to train action detection model could help reduce annotation cost. In this work, we first introduce the Semi-supervised Action Detection (SSAD) task with a mixture of labeled and unlabeled data and analyze different types of errors in the proposed SSAD baselines which are directly adapted from the semi-supervised classification literature. Identifying that the main source of error is action incompleteness (i.e., missing parts of actions), we alleviate it by designing an unsupervised foreground attention (UFA) module utilizing the conditional independence between foreground and background motion. Then we incorporate weakly-labeled data into SSAD and propose Omni-supervised Action Detection (OSAD) with three levels of supervision. To overcome the accompanying action-context confusion problem in OSAD baselines, an information bottleneck (IB) is designed to suppress the scene information in non-action frames while preserving the action information. We extensively benchmark against the baselines for SSAD and OSAD on our created data splits in THUMOS14 and ActivityNet1.2, and demonstrate the effectiveness of the proposed UFA and IB methods. Lastly, the benefit of our full OSAD-IB model under limited annotation budgets is shown by exploring the optimal annotation strategy for labeled, unlabeled and weakly-labeled data.

@InProceedings{Shi_2021_ICCV,
    author    = {Shi, Baifeng and Dai, Qi and Hoffman, Judy and Saenko, Kate and Darrell, Trevor and Xu, Huijuan},
    title     = {Temporal Action Detection With Multi-Level Supervision},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {8022-8032}
}


Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal

Abstract: Recent Visual Question Answering (VQA) models have shown impressive performance on the VQA benchmark but remain sensitive to small linguistic variations in input questions. Existing approaches address this by augmenting the dataset with question paraphrases from visual question generation models or adversarial perturbations. These approaches use the combined data to learn an answer classifier by minimizing the standard cross-entropy loss. To more effectively leverage augmented data, we build on the recent success in contrastive learning. We propose a novel training paradigm (ConClaT) that optimizes both cross-entropy and contrastive losses. The contrastive loss encourages representations to be robust to linguistic variations in questions while the cross-entropy loss preserves the discriminative power of representations for answer prediction. We find that optimizing both losses -- either alternately or jointly -- is key to effective training. On the VQA-Rephrasings benchmark, which measures the VQA model's answer consistency across human paraphrases of a question, ConClaT improves Consensus Score by 1.63% over an improved baseline. In addition, on the standard VQA 2.0 benchmark, we improve the VQA accuracy by 0.78% overall. We also show that ConClaT is agnostic to the type of data-augmentation strategy used.

@InProceedings{Kant_2021_ICCV,
    author    = {Kant, Yash and Moudgil, Abhinav and Batra, Dhruv and Parikh, Devi and Agrawal, Harsh},
    title     = {Contrast and Classify: Training Robust VQA Models},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1604-1613}
}


Chenxu Zhang, Yifan Zhao, Yifei Huang, Ming Zeng, Saifeng Ni, Madhukar Budagavi, Xiaohu Guo

Abstract: In this paper, we propose a talking face generation method that takes an audio signal as input and a short target video clip as reference, and synthesizes a photo-realistic video of the target face with natural lip motions, head poses, and eye blinks that are in-sync with the input audio signal. We note that the synthetic face attributes include not only explicit ones such as lip motions that have high correlations with speech, but also implicit ones such as head poses and eye blinks that have only weak correlation with the input audio. To model such complicated relationships among different face attributes with input audio, we propose a FACe Implicit Attribute Learning Generative Adversarial Network (FACIAL-GAN), which integrates the phonetics-aware, context-aware, and identity-aware information to synthesize the 3D face animation with realistic motions of lips, head poses, and eye blinks. Then, our Rendering-to-Video network takes the rendered face images and the attention map of eye blinks as input to generate the photo-realistic output video frames. Experimental results and user studies show our method can generate realistic talking face videos with not only synchronized lip motions, but also natural head movements and eye blinks, with better qualities than the results of state-of-the-art methods.

@InProceedings{Zhang_2021_ICCV,
    author    = {Zhang, Chenxu and Zhao, Yifan and Huang, Yifei and Zeng, Ming and Ni, Saifeng and Budagavi, Madhukar and Guo, Xiaohu},
    title     = {FACIAL: Synthesizing Dynamic Talking Face With Implicit Attribute Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3867-3876}
}


Andrea Hornakova, Timo Kaiser, Paul Swoboda, Michal Rolinek, Bodo Rosenhahn, Roberto Henschel

Abstract: We present an efficient approximate message passing solver for the lifted disjoint paths problem (LDP), a natural but NP-hard model for multiple object tracking (MOT). Our tracker scales to very large instances that come from long and crowded MOT sequences. Our approximate solver enables us to process the MOT15/16/17 benchmarks without sacrificing solution quality and allows for solving MOT20, which has been out of reach up to now for LDP solvers due to its size and complexity. On all these four standard MOT benchmarks we achieve performance comparable or better than current state-of-the-art methods including a tracker based on an optimal LDP solver.

@InProceedings{Hornakova_2021_ICCV,
    author    = {Hornakova, Andrea and Kaiser, Timo and Swoboda, Paul and Rolinek, Michal and Rosenhahn, Bodo and Henschel, Roberto},
    title     = {Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6330-6340}
}


Yiming Cui, Liqi Yan, Zhiwen Cao, Dongfang Liu

Abstract: Video objection detection is a challenging task because isolated video frames may encounter appearance deterioration, which introduces great confusion for detection. One of the popular solutions is to exploit the temporal information and enhance per-frame representation through aggregating features from neighboring frames. Despite achieving improvements in detection, existing methods focus on the selection of higher-level video frames for aggregation rather than modeling lower-level temporal relations to increase the feature representation. To address this limitation, we propose a novel solution named TF-Blender, which includes three modules: 1) Temporal relation models the relations between the current frame and its neighboring frames to preserve spatial information. 2). Feature adjustment enriches the representation of every neighboring feature map; 3) Feature blender combines outputs from the first two modules and produces stronger features for the later detection tasks. For its simplicity, TF-Blender can be effortlessly plugged into any detection network to improve detection behavior. Extensive evaluations on ImageNet VID and YouTube-VIS benchmarks indicate the performance guarantees of using TF-Blender on recent state-of-the-art methods.

@InProceedings{Cui_2021_ICCV,
    author    = {Cui, Yiming and Yan, Liqi and Cao, Zhiwen and Liu, Dongfang},
    title     = {TF-Blender: Temporal Feature Blender for Video Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {8138-8147}
}


Soumi Das, Harikrishna Patibandla, Suparna Bhattacharya, Kshounis Bera, Niloy Ganguly, Sourangshu Bhattacharya

Abstract: Training vision-based Autonomous driving models is a challenging problem with enormous practical implications. One of the main challenges is the requirement of storage and processing of vast volumes of (possibly redundant) driving video data. In this paper, we study the problem of data-efficient training of autonomous driving systems. We argue that in the context of an edge-device deployment, multi-criteria online video frame subset selection is an appropriate technique for developing such frameworks. We study existing convex optimization based solutions and show that they are unable to provide solution with high weightage to loss of selected video frames. We design a novel multi-criteria online subset selection algorithm, TMCOSS, which uses a thresholded concave function of selection variables. Extensive experiments using driving simulator CARLA show that we are able to drop 80% of the frames, while succeeding to complete 100% of the episodes. We also show that TMCOSS improves performance on the crucial affordance 'Relative Angle' during turns, on inclusion of bucket-specific relative angle loss (BL), leading to selection of more frames in those parts. TMCOSS also achieves an 80% reduction in number of training video frames, on real-world videos from the standard BDD and Cityscapes datasets, for the tasks of drivable area segmentation, and semantic segmentation.

@InProceedings{Das_2021_ICCV,
    author    = {Das, Soumi and Patibandla, Harikrishna and Bhattacharya, Suparna and Bera, Kshounis and Ganguly, Niloy and Bhattacharya, Sourangshu},
    title     = {TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6341-6350}
}


Unsupervised Deep Video Denoising

Dev Yashpal Sheth, Sreyas Mohan, Joshua L. Vincent, Ramon Manzorro, Peter A. Crozier, Mitesh M. Khapra, Eero P. Simoncelli, Carlos Fernandez-Granda

Abstract: Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, we propose an Unsupervised Deep Video Denoiser (UDVD), a CNN architecture designed to be trained exclusively with noisy data. The performance of UDVD is comparable to the supervised state-of-the-art, even when trained only on a single short noisy video. We demonstrate the promise of our approach in real-world imaging applications by denoising raw video, fluorescence-microscopy and electron-microscopy data. In contrast to many current approaches to video denoising, UDVD does not require explicit motion compensation. This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy. A gradient-based analysis reveals that UDVD automatically tracks the motion of objects in the input noisy videos. Thus, the network learns to perform implicit motion compensation, even though it is only trained for denoising.

@InProceedings{Sheth_2021_ICCV,
    author    = {Sheth, Dev Yashpal and Mohan, Sreyas and Vincent, Joshua L. and Manzorro, Ramon and Crozier, Peter A. and Khapra, Mitesh M. and Simoncelli, Eero P. and Fernandez-Granda, Carlos},
    title     = {Unsupervised Deep Video Denoising},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1759-1768}
}


Xingxu Yao, Sicheng Zhao, Pengfei Xu, Jufeng Yang

Abstract: To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unlabeled target domain. However, existing methods assume that the labeled data are sampled from a single source domain, which ignores a more generalized scenario, where labeled data are from multiple source domains. For the more challenging task, we propose a unified Faster RCNN based framework, termed Divide-and-Merge Spindle Network (DMSN), which can simultaneously enhance domain invariance and preserve discriminative power. Specifically, the framework contains multiple source subnets and a pseudo target subnet. First, we propose a hierarchical feature alignment strategy to conduct strong and weak alignments for low- and high-level features, respectively, considering their different effects for object detection. Second, we develop a novel pseudo subnet learning algorithm to approximate optimal parameters of pseudo target subset by weighted combination of parameters in different source subnets. Finally, a consistency regularization for region proposal network is proposed to facilitate each subnet to learn more abstract invariances. Extensive experiments on different adaptation scenarios demonstrate the effectiveness of the proposed model.

@InProceedings{Yao_2021_ICCV,
    author    = {Yao, Xingxu and Zhao, Sicheng and Xu, Pengfei and Yang, Jufeng},
    title     = {Multi-Source Domain Adaptation for Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3273-3282}
}


Jinming Cao, Hanchao Leng, Dani Lischinski, Daniel Cohen-Or, Changhe Tu, Yangyan Li

Abstract: RGB-D semantic segmentation has attracted increasing attention over the past few years. Existing methods mostly employ homogeneous convolution operators to consume the RGB and depth features, ignoring their intrinsic differences. In fact, the RGB values capture the photometric appearance properties in the projected image space, while the depth feature encodes both the shape of a local geometry as well as the base (whereabout) of it in a larger context. Compared with the base, the shape probably is more inherent and has a stronger connection to the semantics, and thus is more critical for segmentation accuracy. Inspired by this observation, we introduce Shape-aware Convolutional layer (ShapeConv) for processing the depth feature, where the depth feature is firstly decomposed into a shape-component and a base-component, next two learnable weights are introduced to cooperate with them independently, and finally a convolution is applied on the re-weighted combination of these two components. ShapeConv is model-agnostic and can be easily integrated into most CNNs to replace vanilla convolutional layers for semantic segmentation. Extensive experiments on three challenging indoor RGB-D semantic segmentation benchmarks, i.e., NYU-Dv2(-13,-40), SUN RGB-D, and SID, demonstrate the effectiveness of our ShapeConv when employing it over five popular architectures. Moreover, the performance of CNNs with ShapeConv is boosted without introducing any computation and memory increase in the inference phase. The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.

@InProceedings{Cao_2021_ICCV,
    author    = {Cao, Jinming and Leng, Hanchao and Lischinski, Dani and Cohen-Or, Daniel and Tu, Changhe and Li, Yangyan},
    title     = {ShapeConv: Shape-Aware Convolutional Layer for Indoor RGB-D Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7088-7097}
}


Ni Zhang, Junwei Han, Nian Liu, Ling Shao

Abstract: Humans perform co-saliency detection by first summarizing the consensus knowledge in the whole group and then searching corresponding objects in each image. Previous methods usually lack robustness, scalability, or stability for the first process and simply fuse consensus features with image features for the second process. In this paper, we propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process. To summarize consensus image features, we first summarize robust features for every single image using an effective pooling method and then aggregate cross-image consensus cues via the self-attention mechanism. By doing this, our model meets the scalability and stability requirements. Next, we generate dynamic kernels from consensus features to encode the summarized consensus knowledge. Two kinds of kernels are generated in a supplementary way to summarize fine-grained image-specific consensus object cues and the coarse group-wise common knowledge, respectively. Then, we can effectively perform object searching by employing dynamic convolution at multiple scales. Besides, a novel and effective data synthesis method is also proposed to train our network. Experimental results on four benchmark datasets verify the effectiveness of our proposed method. Our code and saliency maps are available at https://github.com/nnizhang/CADC.

@InProceedings{Zhang_2021_ICCV,
    author    = {Zhang, Ni and Han, Junwei and Liu, Nian and Shao, Ling},
    title     = {Summarize and Search: Learning Consensus-Aware Dynamic Convolution for Co-Saliency Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4167-4176}
}


Shih-Cheng Huang, Liyue Shen, Matthew P. Lungren, Serena Yeung

Abstract: In recent years, the growing number of medical imaging studies is placing an ever-increasing burden on radiologists. Deep learning provides a promising solution for automatic medical image analysis and clinical decision support. However, large-scale manually labeled datasets required for training deep neural networks are difficult and expensive to obtain for medical images. The purpose of this work is to develop label-efficient multimodal medical imaging representations by leveraging radiology reports. Specifically, we propose an attention-based framework (GLoRIA) for learning global and local representations by contrasting image sub-regions and words in the paired report. In addition, we propose methods to leverage the learned representations for various downstream medical image recognition tasks with limited labels. Our results demonstrate high-performance and label-efficiency for image-text retrieval, classification (finetuning and zeros-shot settings), and segmentation on different datasets.

@InProceedings{Huang_2021_ICCV,
    author    = {Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P. and Yeung, Serena},
    title     = {GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3942-3951}
}


Juan Léon Alcázar, Fabian Caba, Ali K. Thabet, Bernard Ghanem

Abstract: Active speaker detection requires a solid integration of multi-modal cues. While individual modalities can approximate a solution, accurate predictions can only be achieved by explicitly fusing the audio and visual features and modeling their temporal progression. Despite its inherent muti-modal nature, current methods still focus on modeling and fusing short-term audiovisual features for individual speakers, often at frame level. In this paper we present a novel approach to active speaker detection that directly addresses the multi-modal nature of the problem, and provides a straightforward strategy where independent visual features from potential speakers in the scene are assigned to a previously detected speech event. Our experiments show that, an small graph data structure built from local information, allows to approximate an instantaneous audio-visual assignment problem. Moreover, the temporal extension of this initial graph achieves a new state-of-the-art performance on the AVA-ActiveSpeaker dataset with a mAP of 88.8%.

@InProceedings{Alcazar_2021_ICCV,
    author    = {Alc\'azar, Juan L\'eon and Caba, Fabian and Thabet, Ali K. and Ghanem, Bernard},
    title     = {MAAS: Multi-Modal Assignation for Active Speaker Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {265-274}
}


Hoeseong Kim, Jongseok Kim, Hyungseok Lee, Hyunsung Park, Gunhee Kim

Abstract: Change captioning is the task of identifying the change and describing it with a concise caption. Despite recent advancements, filtering out insignificant changes still remains as a challenge. Namely, images from different camera perspectives can cause issues; a mere change in viewpoint should be disregarded while still capturing the actual changes. In order to tackle this problem, we present a new Viewpoint-Agnostic change captioning network with Cycle Consistency (VACC) that requires only one image each for the before and after scene, without depending on any other information. We achieve this by devising a new difference encoder module which can encode viewpoint information and model the difference more effectively. In addition, we propose a cycle consistency module that can potentially improve the performance of any change captioning networks in general by matching the composite feature of the generated caption and before image with the after image feature. We evaluate the performance of our proposed model across three datasets for change captioning, including a novel dataset we introduce here that contains images with changes under extreme viewpoint shifts. Through our experiments, we show the excellence of our method with respect to the CIDEr, BLEU-4, METEOR and SPICE scores. Moreover, we demonstrate that attaching our proposed cycle consistency module yields a performance boost for existing change captioning networks, even with varying image encoding mechanisms.

@InProceedings{Kim_2021_ICCV,
    author    = {Kim, Hoeseong and Kim, Jongseok and Lee, Hyungseok and Park, Hyunsung and Kim, Gunhee},
    title     = {Viewpoint-Agnostic Change Captioning With Cycle Consistency},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2095-2104}
}


Ruohao Guo, Dantong Niu, Liao Qu, Zhenbo Li

Abstract: Most recent transformer-based models show impressive performance on vision tasks, even better than Convolution Neural Networks (CNN). In this work, we present a novel, flexible, and effective transformer-based model for high-quality instance segmentation. The proposed method, Segmenting Objects with TRansformers (SOTR), simplifies the segmentation pipeline, building on an alternative CNN backbone appended with two parallel subtasks: (1) predicting per-instance category via transformer and (2) dynamically generating segmentation mask with the multi-level upsampling module. SOTR can effectively extract lower-level feature representations and capture long-range context dependencies by Feature Pyramid Network (FPN) and twin transformer, respectively. Meanwhile, compared with the original transformer, the proposed twin transformer is timeand resource-efficient since only a row and a column attention are involved to encode pixels. Moreover, SOTR is easy to be incorporated with various CNN backbones and transformer model variants to make considerable improvements for the segmentation accuracy and training convergence. Extensive experiments show that our SOTR performs well on the MS COCO dataset and surpasses state-of-the-art instance segmentation approaches. We hope our simple but strong framework could serve as a preferment baseline for instance-level recognition. Our code is available at https://github.com/easton-cau/SOTR.

@InProceedings{Guo_2021_ICCV,
    author    = {Guo, Ruohao and Niu, Dantong and Qu, Liao and Li, Zhenbo},
    title     = {SOTR: Segmenting Objects With Transformers},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7157-7166}
}


Longwen Zhang, Qixuan Zhang, Minye Wu, Jingyi Yu, Lan Xu

Abstract: Video portraits relighting is critical in user-facing human photography, especially for immersive VR/AR experience. Recent advances still fail to recover consistent relit result under dynamic illuminations from monocular RGB stream, suffering from the lack of video consistency supervision. In this paper, we propose a neural approach for real-time, high-quality and coherent video portrait relighting, which jointly models the semantic, temporal and lighting consistency using a new dynamic OLAT dataset. We propose a hybrid structure and lighting disentanglement in an encoder-decoder architecture, which combines a multi-task and adversarial training strategy for semantic-aware consistency modeling. We adopt a temporal modeling scheme via flow-based supervision to encode the conjugated temporal consistency in a cross manner. We also propose a lighting sampling strategy to model the illumination consistency and mutation for natural portrait light manipulation in real-world. Extensive experiments demonstrate the effectiveness of our approach for consistent video portrait light-editing and relighting, even using mobile computing.

@InProceedings{Zhang_2021_ICCV,
    author    = {Zhang, Longwen and Zhang, Qixuan and Wu, Minye and Yu, Jingyi and Xu, Lan},
    title     = {Neural Video Portrait Relighting in Real-Time via Consistency Modeling},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {802-812}
}


Guo-Sen Xie, Huan Xiong, Jie Liu, Yazhou Yao, Ling Shao

Abstract: Few-shot semantic segmentation (FSS) is an important task for novel (unseen) object segmentation under the data-scarcity scenario. However, most FSS methods rely on unidirectional feature aggregation, e.g., from support prototypes to get the query prediction, and from high-resolution features to guide the low-resolution ones. This usually fails to fully capture the cross-resolution feature relationships and thus leads to inaccurate estimates of the query objects. To resolve the above dilemma, we propose a cyclic memory network (CMN) to directly learn to read abundant support information from all resolution features in a cyclic manner. Specifically, we first generate N pairs (key and value) of multi-resolution query features guided by the support feature and its mask. Next, we circularly take one pair of these features as the query to be segmented, and the rest N-1 pairs are written into an external memory accordingly, i.e., this leave-one-out process is conducted for N times. In each cycle, the query feature is updated by collaboratively matching its key and value with the memory, which can elegantly cover all the spatial locations from different resolutions. Furthermore, we incorporate the query feature re-adding and the query feature recursive updating mechanisms into the memory reading operation. CMN, equipped with these merits, can thus capture cross-resolution relationships and better handle the object appearance and scale variations in FSS. Experiments on PASCAL-5i and COCO-20i well validate the effectiveness of our model for FSS.

@InProceedings{Xie_2021_ICCV,
    author    = {Xie, Guo-Sen and Xiong, Huan and Liu, Jie and Yao, Yazhou and Shao, Ling},
    title     = {Few-Shot Semantic Segmentation With Cyclic Memory Network},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7293-7302}
}


Abstract: We address the problem of learning to segment actions from weakly-annotated videos, i.e., videos accompanied by transcripts (ordered list of actions). We propose a framework in which we model actions with a union of low-dimensional subspaces, learn the subspaces using transcripts and refine video features that lend themselves to action subspaces. To do so, we design an architecture consisting of a Union-of-Subspace Network, which is an ensemble of autoencoders, each modeling a low-dimensional action subspace and can capture variations of an action within and across videos. For learning, at each iteration, we generate positive and negative soft alignment matrices using the segmentations from the previous iteration, which we use for discriminative training of our model. To regularize the learning, we introduce a constraint loss that prevents imbalanced segmentations and enforces relatively similar duration of each action across videos. To have a real-time inference, we develop a hierarchical segmentation framework that uses subset selection to find representative transcripts and hierarchically align a test video with increasingly refined representative transcripts. Our experiments on three datasets show that our method improves the state-of-the-art action segmentation and alignment, while speeding up the inference time by a factor of 4 to 13.

@InProceedings{Lu_2021_ICCV,
    author    = {Lu, Zijia and Elhamifar, Ehsan},
    title     = {Weakly-Supervised Action Segmentation and Alignment via Transcript-Aware Union-of-Subspaces Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {8085-8095}
}


Bo Xu, Han Huang, Cheng Lu, Ziwen Li, Yandong Guo

Abstract: Most existing human matting algorithms tried to separate pure human-only foreground from the background. In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image. The VMFM method requires no additional inputs, e.g. trimap or known background. We reformulate foreground matting as a self-supervised multi-modality problem: factor each input image into estimated depth map, segmentation mask, and interaction heatmap using three auto-encoders. In order to fully utilize the characteristics of each modality, we first train a dual encoder-to-decoder network to estimate the same alpha matte. Then we introduce a self-supervised method: Complementary Learning(CL) to predict deviation probability map and exchange reliable gradients across modalities without label. We conducted extensive experiments to analyze the effectiveness of each modality and the significance of different components in complementary learning. We demonstrate that our model outperforms the state-of-the-art methods.

@InProceedings{Xu_2021_ICCV,
    author    = {Xu, Bo and Huang, Han and Lu, Cheng and Li, Ziwen and Guo, Yandong},
    title     = {Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {438-447}
}


Duo Chen, Zixin Tang, Zhenyu Xu, Yunan Zheng, Yiguang Liu

Abstract: Reconstructing delicate geometric details with consumer RGB-D sensors is challenging due to sensor depth and poses uncertainties. To tackle this problem, we propose a unique geometry-guided fusion framework: 1) First, we characterize fusion correspondences with the geodesic curves derived from the mass transport problem, also known as the Monge-Kantorovich problem. Compared with the depth map back-projection methods, the geodesic curves reveal the geometric structures of the local surface. 2) Moving the points along the geodesic curves is the core of our fusion approach, guided by local geometric properties, i.e., Gaussian curvature and mean curvature. Compared with the state-of-the-art methods, our novel geometry-guided displacement interpolation fully utilizes the meaningful geometric features of the local surface. It makes the reconstruction accuracy and completeness improved. Finally, a significant number of experimental results on real object data verify the superior performance of the proposed method. Our technique achieves the most delicate geometric details on thin objects for which the original depth map back-projection fusion scheme suffers from severe artifacts (See Fig.1).

@InProceedings{Chen_2021_ICCV,
    author    = {Chen, Duo and Tang, Zixin and Xu, Zhenyu and Zheng, Yunan and Liu, Yiguang},
    title     = {Gaussian Fusion: Accurate 3D Reconstruction via Geometry-Guided Displacement Interpolation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5916-5925}
}


Abstract: Hypergraph matching is a useful tool to find feature correspondence by considering higher-order structural information. Recently, the employment of deep learning has made great progress in the matching of graphs, suggesting its potential for hypergraphs. Hence, in this paper, we present the first, to our best knowledge, unified hypergraph neural network (HNN) solution for hypergraph matching. Specifically, given two hypergraphs to be matched, we first construct an association hypergraph over them and convert the hypergraph matching problem into a node classification problem on the association hypergraph. Then, we design a novel hypergraph neural network to effectively solve the node classification problem. Being end-to-end trainable, our proposed method, named HNN-HM, jointly learns all its components with improved optimization. For evaluation, HNN-HM is tested on various benchmarks and shows a clear advantage over state-of-the-arts.

@InProceedings{Liao_2021_ICCV,
    author    = {Liao, Xiaowei and Xu, Yong and Ling, Haibin},
    title     = {Hypergraph Neural Networks for Hypergraph Matching},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1266-1275}
}


Bingyao Yu, Wanhua Li, Xiu Li, Jiwen Lu, Jie Zhou

Abstract: In this paper, we propose a frequency-aware spatiotemporal transformers for deep In this paper, we propose a Frequency-Aware Spatiotemporal Transformer (FAST) for video inpainting detection, which aims to simultaneously mine the traces of video inpainting from spatial, temporal, and frequency domains. Unlike existing deep video inpainting detection methods that usually rely on hand-designed attention modules and memory mechanism, the proposed FAST have innate global self-attention mechanisms to capture the long-range relations. While existing video inpainting methods usually explore the spatial and temporal connections in a video, our method employs a spatiotemporal transformer framework to detect the spatial connections between patches and temporal dependency between frames. As the inpainted videos usually lack high frequency details, the proposed FAST simultaneously exploits the frequency domain information with a specifically designed decoder. Extensive experimental results demonstrate that our approach achieves very competitive performance and generalizes well.

@InProceedings{Yu_2021_ICCV,
    author    = {Yu, Bingyao and Li, Wanhua and Li, Xiu and Lu, Jiwen and Zhou, Jie},
    title     = {Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {8188-8197}
}


Abstract: The classification and regression head are both indispensable components to build up a dense object detector, which are usually supervised by the same training samples and thus expected to have consistency with each other for detecting objects accurately in final detection pipelines. In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency. MuSu defines training samples for the regression head mainly based on classification predicting scores and in turn, defines samples for the classification head based on localization scores from the regression head. Experimental results show that the convergence of detectors trained by this mutual supervision is guaranteed and the effectiveness of the proposed method is verified on the challenging MS COCO benchmark. We also find that tiling more anchors at the same location benefits detectors and leads to further improvements under this training scheme. We hope this work can inspire further researches on the interaction of the classification and regression task in detection and the supervision paradigm for detectors, especially separately for these two heads.

@InProceedings{Gao_2021_ICCV,
    author    = {Gao, Ziteng and Wang, Limin and Wu, Gangshan},
    title     = {Mutual Supervision for Dense Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3641-3650}
}


Hongjun Chen, Jinbao Wang, Hong Cai Chen, Xiantong Zhen, Feng Zheng, Rongrong Ji, Ling Shao

Abstract: Annotation burden has become one of the biggest barriers to semantic segmentation. Approaches based on click-level annotations have therefore attracted increasing attention due to their superior trade-off between supervision and annotation cost. In this paper, we propose seminar learning, a new learning paradigm for semantic segmentation with click-level supervision. The fundamental rationale of seminar learning is to leverage the knowledge from different networks to compensate for insufficient information provided in click-level annotations. Mimicking a seminar, our seminar learning involves a teacher-student and a student-student module, where a student can learn from both skillful teachers and other students. The teacher-student module uses a teacher network based on the exponential moving average to guide the training of the student network. In the student-student module, heterogeneous pseudo-labels are proposed to bridge the transfer of knowledge among students to enhance each other's performance. Experimental results demonstrate the effectiveness of seminar learning, which achieves the new state-of-the-art performance of 72.51% (mIOU), surpassing previous methods by a large margin of up to 16.88% on the Pascal VOC 2012 dataset.

@InProceedings{Chen_2021_ICCV,
    author    = {Chen, Hongjun and Wang, Jinbao and Chen, Hong Cai and Zhen, Xiantong and Zheng, Feng and Ji, Rongrong and Shao, Ling},
    title     = {Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6920-6929}
}


Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, Dacheng Tao

Abstract: In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both the GNN parameters and the graph features. Despite the lightweight architecture, we observed that this vanilla framework suffered from insufficient discriminative power in distinguishing graph topologies, leading to a dramatic drop in performance. This discovery motivates us to devise meta aggregators to improve the expressive power of vanilla binarized GNNs, of which the aggregation schemes can be adaptively changed in a learnable manner based on the binarized features. Towards this end, we propose two dedicated forms of meta neighborhood aggregators, an exclusive meta aggregator termed as Greedy Gumbel Neighborhood Aggregator (GNA), and a diffused meta aggregator termed as Adaptable Hybrid Neighborhood Aggregator (ANA). GNA learns to exclusively pick one single optimal aggregator from a pool of candidates, while ANA learns a hybrid aggregation behavior to simultaneously retain the benefits of several individual aggregators. Furthermore, the proposed meta aggregators may readily serve as a generic plugin module into existing full-precision GNNs. Experiments across various domains demonstrate that the proposed method yields results superior to the state of the art.

@InProceedings{Jing_2021_ICCV,
    author    = {Jing, Yongcheng and Yang, Yiding and Wang, Xinchao and Song, Mingli and Tao, Dacheng},
    title     = {Meta-Aggregator: Learning To Aggregate for 1-Bit Graph Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5301-5310}
}


Viktor Larsson, Marc Pollefeys, Magnus Oskarsson

Abstract: In this paper we consider the epipolar geometry between orthographic and perspective cameras. We generalize many of the classical results for the perspective essential matrix to this setting and derive novel minimal solvers, not only for the calibrated case, but also for partially calibrated and non-central camera setups. While orthographic cameras might seem exotic, they occur naturally in many applications. They can e.g. model 2D maps (such as floor plans), aerial/satellite photography and even approximate narrow field-of-view cameras (e.g. from telephoto lenses). In our experiments we highlight various applications of the developed theory and solvers, including Radar-Camera calibration and aligning Structure-from-Motion models to aerial or satellite images.

@InProceedings{Larsson_2021_ICCV,
    author    = {Larsson, Viktor and Pollefeys, Marc and Oskarsson, Magnus},
    title     = {Orthographic-Perspective Epipolar Geometry},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5570-5578}
}


Min Jin Chong, Wen-Sheng Chu, Abhishek Kumar, David Forsyth

Abstract: We present Retrieve in Style (RIS), an unsupervised framework for facial feature transfer and retrieval on real images. Recent work shows capabilities of transferring local facial features by capitalizing on the disentanglement property of the StyleGAN latent space. RIS improves existing art on the following: 1) Introducing more effective feature disentanglement to allow for challenging transfers (i.e., hair, pose) that were not shown possible in SoTA methods. 2) Eliminating the need for per-image hyperparameter tuning, and for computing a catalog over a large batch of images. 3) Enabling fine-grained face retrieval using disentangled facial features (e.g., eyes). To our best knowledge, this is the first work to retrieve face images at this fine level. 4) Demonstrating robust, natural editing on real images. Our qualitative and quantitative analyses show RIS achieves both high-fidelity feature transfers and accurate fine-grained retrievals on real images. We also discuss the responsible applications of RIS. Our code is available at https://github.com/mchong6/RetrieveInStyle.

@InProceedings{Chong_2021_ICCV,
    author    = {Chong, Min Jin and Chu, Wen-Sheng and Kumar, Abhishek and Forsyth, David},
    title     = {Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3887-3896}
}


Lv Tang, Bo Li, Yijie Zhong, Shouhong Ding, Mofei Song

Abstract: Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to the era of high resolution, SOD methods are facing new challenges. The major limitation of previous methods is that they try to identify the salient regions and estimate the accurate objects boundaries simultaneously with a single regression task at low-resolution. This practice ignores the inherent difference between the two difficult problems, resulting in poor detection quality. In this paper, we propose a novel deep learning framework for high-resolution SOD task, which disentangles the task into a low-resolution saliency classification network (LRSCN) and a high-resolution refinement network (HRRN). As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions. HRRN is a regression task, which aims at accurately refining the saliency value of pixels in the uncertain region to preserve a clear object boundary at high-resolution with limited GPU memory. It is worth noting that by introducing uncertainty into the training process, our HRRN can well address the high-resolution refinement task without using any high-resolution training data. Extensive experiments on high-resolution saliency datasets as well as some widely used saliency benchmarks show that the proposed method achieves superior performance compared to the state-of-the-art methods.

@InProceedings{Tang_2021_ICCV,
    author    = {Tang, Lv and Li, Bo and Zhong, Yijie and Ding, Shouhong and Song, Mofei},
    title     = {Disentangled High Quality Salient Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3580-3590}
}


Abstract: We propose a novel pointwise descriptor, called DWKS, aimed at finding correspondences across two deformable shape collections. Unlike the majority of existing descriptors, rather than capturing local geometry, DWKS captures the deformation around a point within a collection in a multi-scale and informative manner. This, in turn, allows to compute inter-collection correspondences without using landmarks. To this end, we build upon the successful spectral WKS descriptors, but rather than using the Laplace-Beltrami operator, show that a similar construction can be performed on shape difference operators, that capture differences or distortion within a collection. By leveraging the collection information our descriptor facilitates difficult non-rigid shape matching tasks, even in the presence of strong partiality and significant deformations. We demonstrate the utility of our approach across a range of challenging matching problems on both meshes and point clouds. The code for this paper can be found at https://github.com/RobinMagnet/DWKS.

@InProceedings{Magnet_2021_ICCV,
    author    = {Magnet, Robin and Ovsjanikov, Maks},
    title     = {DWKS: A Local Descriptor of Deformations Between Meshes and Point Clouds},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3793-3802}
}


Shiming Chen, Wenjie Wang, Beihao Xia, Qinmu Peng, Xinge You, Feng Zheng, Ling Shao

Abstract: Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gaps and seen-unseen bias. However, most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. Such a bias inevitably results in poor-quality visual features for GZSL tasks, which potentially limits the recognition performance on both seen and unseen classes. In this paper, we propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE), to tackle the above problem. FREE employs a feature refinement (FR) module that incorporates semantic-visual mapping into a unified generative model to refine the visual features of seen and unseen class samples. Furthermore, we propose a self-adaptive margin center loss (SAMC-loss) that cooperates with a semantic cycle-consistency loss to guide FR to learn class- and semantically-relevant representations, and concatenate the features in FR to extract the fully refined features. Extensive experiments on five benchmark datasets demonstrate the significant performance gain of FREE over current state-of-the-art methods and its baseline. The code is available at https://github.com/shiming-chen/FREE.

@InProceedings{Chen_2021_ICCV,
    author    = {Chen, Shiming and Wang, Wenjie and Xia, Beihao and Peng, Qinmu and You, Xinge and Zheng, Feng and Shao, Ling},
    title     = {FREE: Feature Refinement for Generalized Zero-Shot Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {122-131}
}


Abstract: Global Covariance Pooling (GCP) aims at exploiting the second-order statistics of the convolutional feature. Its effectiveness has been demonstrated in boosting the classification performance of Convolutional Neural Networks (CNNs). Singular Value Decomposition (SVD) is used in GCP to compute the matrix square root. However, the approximate matrix square root calculated using Newton-Schulz iteration [??] outperforms the accurate one computed via SVD [??]. We empirically analyze the reason behind the performance gap from the perspectives of data precision and gradient smoothness. Various remedies for computing smooth SVD gradients are investigated. Based on our observation and analyses, a hybrid training protocol is proposed for SVD-based GCP meta-layers such that competitive performances can be achieved against Newton-Schulz iteration. Moreover, we propose a new GCP meta-layer that uses SVD in the forward pass, and Pade approximants in the backward propagation to compute the gradients. The proposed meta-layer has been integrated into different CNN models and achieves state-of-the-art performances on both large-scale and fine-grained datasets.

@InProceedings{Song_2021_ICCV,
    author    = {Song, Yue and Sebe, Nicu and Wang, Wei},
    title     = {Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1115-1123}
}


Abstract: The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism, leading to image distortions if the camera moves during image acquisition. In this paper, we present a novel deep network to solve the generic rolling shutter correction problem with two consecutive frames. Our pipeline is symmetrically designed to predict the global shutter image corresponding to the intermediate time of these two frames, which is difficult for existing methods because it corresponds to a camera pose that differs most from the two frames. First, two time-symmetric dense undistortion flows are estimated by using well-established principles: pyramidal construction, warping, and cost volume processing. Then, both rolling shutter images are warped into a common global shutter one in the feature space, respectively. Finally, a symmetric consistency constraint is constructed in the image decoder to effectively aggregate the contextual cues of two rolling shutter images, thereby recovering the high-quality global shutter image. Extensive experiments with both synthetic and real data from public benchmarks demonstrate the superiority of our proposed approach over the state-of-the-art methods.

@InProceedings{Fan_2021_ICCV,
    author    = {Fan, Bin and Dai, Yuchao and He, Mingyi},
    title     = {SUNet: Symmetric Undistortion Network for Rolling Shutter Correction},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4541-4550}
}


Abstract: Autoregressive models are a class of exact inference approaches with highly flexible functional forms, yielding state-of-the-art density estimates for natural images. Yet, the sequential ordering on the dimensions makes these models computationally expensive and limits their applicability to low-resolution imagery. In this work, we propose Pixel-Pyramids, a block-autoregressive approach employing a lossless pyramid decomposition with scale-specific representations to encode the joint distribution of image pixels. Crucially, it affords a sparser dependency structure compared to fully autoregressive approaches. Our PixelPyramids yield state-of-the-art results for density estimation on various image datasets, especially for high-resolution data. For CelebA-HQ 1024 x 1024, we observe that the density estimates (in terms of bits/dim) are improved to  44% of the baseline despite sampling speeds superior even to easily parallelizable flow-based models.

@InProceedings{Mahajan_2021_ICCV,
    author    = {Mahajan, Shweta and Roth, Stefan},
    title     = {PixelPyramids: Exact Inference Models From Lossless Image Pyramids},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6639-6648}
}


Abstract: Adversarial robustness of deep models is pivotal in ensuring safe deployment in real world settings, but most modern defenses have narrow scope and expensive costs. In this paper, we propose a self-supervised method to detect adversarial attacks and classify them to their respective threat models, based on a linear model operating on the embeddings from a pre-trained self-supervised encoder. We use a SimCLR encoder in our experiments, since we show the SimCLR embedding distance is a good proxy for human perceptibility, enabling it to encapsulate many threat models at once. We call our method SimCat since it uses SimCLR encoder to catch and categorize various types of adversarial attacks, including L_p and non-L_p evasion attacks, as well as data poisonings. The simple nature of a linear classifier makes our method efficient in both time and sample complexity. For example, on SVHN, using only five pairs of clean and adversarial examples computed with a PGD-L_inf attack, SimCat's detection accuracy is over 85%. Moreover, on ImageNet, using only 25 examples from each threat model, SimCat can classify eight different attack types such as PGD-L_2, PGD-L_inf, CW-L_2, PPGD, LPA, StAdv, ReColor, and JPEG-L_inf, with over 40% accuracy. On STL10 data, we apply SimCat as a defense against poisoning attacks, such as BP, CP, FC, CLBD, HTBD, halving the success rate while using only twenty total poisons for training. We find that the detectors generalize well to unseen threat models. Lastly, we investigate the performance of our detection method under adaptive attacks and further boost its robustness against such attacks via adversarial training.

@InProceedings{Moayeri_2021_ICCV,
    author    = {Moayeri, Mazda and Feizi, Soheil},
    title     = {Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7677-7686}
}


Zhengyuan Yang, Songyang Zhang, Liwei Wang, Jiebo Luo

Abstract: 3D visual grounding aims at grounding a natural language description about a 3D scene, usually represented in the form of 3D point clouds, to the targeted object region. Point clouds are sparse, noisy, and contain limited semantic information compared with 2D images. These inherent limitations make the 3D visual grounding problem more challenging. In this study, we propose 2D Semantics Assisted Training (SAT) that utilizes 2D image semantics in the training stage to ease point-cloud-language joint representation learning and assist 3D visual grounding. The main idea is to learn auxiliary alignments between rich, clean 2D object representations and the corresponding objects or mentioned entities in 3D scenes. SAT takes 2D object semantics, i.e., object label, image feature, and 2D geometric feature, as the extra input in training but does not require such inputs during inference. By effectively utilizing 2D semantics in training, our approach boosts the accuracy on the Nr3D dataset from 37.7% to 49.2%, which significantly surpasses the non-SAT baseline with the identical network architecture and inference input. Our approach outperforms the state of the art by large margins on multiple 3D visual grounding datasets, i.e., +10.4% absolute accuracy on Nr3D, +9.9% on Sr3D, and +5.6% on ScanRef.

@InProceedings{Yang_2021_ICCV,
    author    = {Yang, Zhengyuan and Zhang, Songyang and Wang, Liwei and Luo, Jiebo},
    title     = {SAT: 2D Semantics Assisted Training for 3D Visual Grounding},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1856-1866}
}


Sarah Schwettmann, Evan Hernandez, David Bau, Samuel Klein, Jacob Andreas, Antonio Torralba

Abstract: A large body of recent work has identified transformations in the latent spaces of generative adversarial networks (GANs) that consistently and interpretably transform generated images. But existing techniques for identifying these transformations rely on either a fixed vocabulary of pre-specified visual concepts, or on unsupervised disentanglement techniques whose alignment with human judgments about perceptual salience is unknown. This paper introduces a new method for building open-ended vocabularies of primitive visual concepts represented in a GAN's latent space. Our approach is built from three components: (1) automatic identification of perceptually salient directions based on their layer selectivity; (2) human annotation of these directions with free-form, compositional natural language descriptions; and (3) decomposition of these annotations into a visual concept vocabulary, consisting of distilled directions labeled with single words. Experiments show that concepts learned with our approach are reliable and composable--generalizing across classes, contexts, and observers, and enabling fine-grained manipulation of image style and content.

@InProceedings{Schwettmann_2021_ICCV,
    author    = {Schwettmann, Sarah and Hernandez, Evan and Bau, David and Klein, Samuel and Andreas, Jacob and Torralba, Antonio},
    title     = {Toward a Visual Concept Vocabulary for GAN Latent Space},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6804-6812}
}


Deep Blind Video Super-Resolution

Jinshan Pan, Haoran Bai, Jiangxin Dong, Jiawei Zhang, Jinhui Tang

Abstract: Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for blind video SR and usually leads to over-smoothed super-resolved frames. In this paper, we propose an effective blind video SR algorithm based on deep convolutional neural networks (CNNs). Our algorithm first estimates blur kernels from low-resolution (LR) input videos. Then, with the estimated blur kernels, we develop an effective image deconvolution method based on the image formation model of blind video SR to generate intermediate latent frames so that sharp image contents can be restored well. To effectively explore the information from adjacent frames, we estimate the motion fields from LR input videos, extract features from LR videos by a feature extraction network, and warp the extracted features from LR inputs based on the motion fields. Moreover, we develop an effective sharp feature exploration method which first extracts sharp features from restored intermediate latent frames and then uses a transformation operation based on the extracted sharp features and warped features from LR inputs to generate better features for HR video restoration. We formulate the proposed algorithm into an end-to-end trainable framework and show that it performs favorably against state-of-the-art methods.

@InProceedings{Pan_2021_ICCV,
    author    = {Pan, Jinshan and Bai, Haoran and Dong, Jiangxin and Zhang, Jiawei and Tang, Jinhui},
    title     = {Deep Blind Video Super-Resolution},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4811-4820}
}


Xiaoyu Zhu, Jeffrey Chen, Xiangrui Zeng, Junwei Liang, Chengqi Li, Sinuo Liu, Sima Behpour, Min Xu

Abstract: We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images. Unlike most existing methods that require voxel-wise densely labeled training data, our weakly-supervised CIVA-Net is the first model that only needs image-level class labels as guidance to learn accurate volumetric segmentation. Our model learns from cross-image co-occurrence for integral region generation, and explores inter-voxel affinity relations to predict segmentation with accurate boundaries. We empirically validate our model on both simulated and real cryo-ET datasets. Our experiments show that CIVA-Net achieves comparable performance to the state-of-the-art models trained with stronger supervision.

@InProceedings{Zhu_2021_ICCV,
    author    = {Zhu, Xiaoyu and Chen, Jeffrey and Zeng, Xiangrui and Liang, Junwei and Li, Chengqi and Liu, Sinuo and Behpour, Sima and Xu, Min},
    title     = {Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2834-2844}
}


Bootstrap Your Own Correspondences

Abstract: Geometric feature extraction is a crucial component of point cloud registration pipelines. Recent work has demonstrated how supervised learning can be leveraged to learn better and more compact 3D features. However, those approaches' reliance on ground-truth annotation limits their scalability. We propose BYOC: a self-supervised approach that learns visual and geometric features from RGB-D video without relying on ground-truth pose or correspondence. Our key observation is that randomly-initialized CNNs readily provide us with good correspondences; allowing us to bootstrap the learning of both visual and geometric features. Our approach combines classic ideas from point cloud registration with more recent representation learning approaches. We evaluate our approach on indoor scene datasets and find that our method outperforms traditional and learned descriptors, while being competitive with current state-of-the-art supervised approaches.

@InProceedings{El_Banani_2021_ICCV,
    author    = {El Banani, Mohamed and Johnson, Justin},
    title     = {Bootstrap Your Own Correspondences},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6433-6442}
}


Jingwei Ji, Rishi Desai, Juan Carlos Niebles

Abstract: We study a crucial problem in video analysis: human-object relationship detection. The majority of previous approaches are developed only for the static image scenario, without incorporating the temporal dynamics so vital to contextualizing human-object relationships. We propose a model with Intra- and Inter-Transformers, enabling joint spatial and temporal reasoning on multiple visual concepts of objects, relationships, and human poses. We find that applying attention mechanisms among features distributed spatio-temporally greatly improves our understanding of human-object relationships. Our method is validated on two datasets, Action Genome and CAD-120-EVAR, and achieves state-of-the-art performance on both of them.

@InProceedings{Ji_2021_ICCV,
    author    = {Ji, Jingwei and Desai, Rishi and Niebles, Juan Carlos},
    title     = {Detecting Human-Object Relationships in Videos},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {8106-8116}
}


Adaptive Curriculum Learning

Yajing Kong, Liu Liu, Jun Wang, Dacheng Tao

Abstract: Inspired by the human learning principle that learning easier concepts first and then gradually paying more attention to harder ones, curriculum learning uses the non-uniform sampling of mini-batches according to the order of examples' difficulty. Just as a teacher adjusts the curriculum according to the learning progress of each student, a proper curriculum should be adapted to the current state of the model. Therefore, in contrast to recent works using a fixed curriculum, we devise a new curriculum learning method, Adaptive Curriculum Learning (Adaptive CL), adapting the difficulty of examples to the current state of the model. Specifically, we make use of the loss of the current model to adjust the difficulty score while retaining previous useful learned knowledge by KL divergence. Moreover, under a non-linear model and binary classification, we theoretically prove that the expected convergence rate of curriculum learning monotonically decreases with respect to the loss of a point regarding the optimal hypothesis, and monotonically increases with respect to the loss of a point regarding the current hypothesis. The analyses indicate that Adaptive CL could improve the convergence properties during the early stages of learning. Extensive experimental results demonstrate the superiority of the proposed approach over existing competitive curriculum learning methods.

@InProceedings{Kong_2021_ICCV,
    author    = {Kong, Yajing and Liu, Liu and Wang, Jun and Tao, Dacheng},
    title     = {Adaptive Curriculum Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {5067-5076}
}


Abstract: Top-k multi-label learning, which returns the top-k predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine. However, the vulnerabilities of such algorithms with regards to dedicated adversarial perturbation attacks have not been extensively studied previously. In this work, we develop methods to create adversarial perturbations that can be used to attack top-k multi-label learning-based image annotation systems (T_kML-AP). Our methods explicitly consider the top-k ranking relation and are based on novel loss functions. Experimental evaluations on large-scale benchmark datasets including PASCAL VOC and MS COCO demonstrate the effectiveness of our methods in reducing the performance of state-of-the-art top-k multi-label learning methods, under both untargeted and targeted attacks.

@InProceedings{Hu_2021_ICCV,
    author    = {Hu, Shu and Ke, Lipeng and Wang, Xin and Lyu, Siwei},
    title     = {TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7649-7657}
}


Andreas Panteli, Jonas Teuwen, Hugo Horlings, Efstratios Gavves

Abstract: Object localisation, in the context of regular images, often depicts objects like people or cars. In these images, there is typically a relatively small number of objects per class, which usually is manageable to annotate. However, outside the setting of regular images, we are often confronted with a different situation. In computational pathology, digitised tissue sections are extremely large images, whose dimensions quickly exceed 250'000x250'000 pixels, where relevant objects, such as tumour cells or lymphocytes can quickly number in the millions. Annotating them all is practically impossible and annotating sparsely a few, out of many more, is the only possibility. Unfortunately, learning from sparse annotations, or sparse-shot learning, clashes with standard supervised learning because what is not annotated is treated as a negative. However, assigning negative labels to what are true positives leads to confusion in the gradients and biased learning. To this end, we present exclusive cross-entropy, which slows down the biased learning by examining the second-order loss derivatives in order to drop the loss terms corresponding to likely biased terms. Experiments on nine datasets and two different localisation tasks, detection with YOLLO and segmentation with Unet, show that we obtain considerable improvements compared to cross-entropy or focal loss, while often reaching the best possible performance for the model with only 10-40% of annotations.

@InProceedings{Panteli_2021_ICCV,
    author    = {Panteli, Andreas and Teuwen, Jonas and Horlings, Hugo and Gavves, Efstratios},
    title     = {Sparse-Shot Learning With Exclusive Cross-Entropy for Extremely Many Localisations},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2813-2823}
}


Visual Saliency Transformer

Nian Liu, Ni Zhang, Kaiyuan Wan, Ling Shao, Junwei Han

Abstract: Existing state-of-the-art saliency detection methods heavily rely on CNN-based architectures. Alternatively, we rethink this task from a convolution-free sequence-to-sequence perspective and predict saliency by modeling long-range dependencies, which can not be achieved by convolution. Specifically, we develop a novel unified model based on a pure transformer, namely, Visual Saliency Transformer (VST), for both RGB and RGB-D salient object detection (SOD). It takes image patches as inputs and leverages the transformer to propagate global contexts among image patches. Unlike conventional architectures used in Vision Transformer (ViT), we leverage multi-level token fusion and propose a new token upsampling method under the transformer framework to get high-resolution detection results. We also develop a token-based multi-task decoder to simultaneously perform saliency and boundary detection by introducing task-related tokens and a novel patch-task-attention mechanism. Experimental results show that our model outperforms existing methods on both RGB and RGB-D SOD benchmark datasets. Most importantly, our whole framework not only provides a new perspective for the SOD field but also shows a new paradigm for transformer-based dense prediction models. Code is available at https://github.com/nnizhang/VST.

@InProceedings{Liu_2021_ICCV,
    author    = {Liu, Nian and Zhang, Ni and Wan, Kaiyuan and Shao, Ling and Han, Junwei},
    title     = {Visual Saliency Transformer},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4722-4732}
}


Abstract: Event cameras, which output events by detecting spatio-temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency. Previous works have achieved impressive performances on event-based video reconstruction by introducing convolutional neural networks (CNNs). However, intrinsic locality of convolutional operations is not capable of modeling long-range dependency, which is crucial to many vision tasks. In this paper, we present a hybrid CNN-Transformer network for event-based video reconstruction (ET-Net), which merits the fine local information from CNN and global contexts from Transformer. In addition, we further propose a Token Pyramid Aggregation strategy to implement multi-scale token integration for relating internal and intersected semantic concepts in the token-space. Experimental results demonstrate that our proposed method achieves superior performance over state-of-the-art methods on multiple real-world event datasets. The code is available at https://github.com/WarranWeng/ET-Net

@InProceedings{Weng_2021_ICCV,
    author    = {Weng, Wenming and Zhang, Yueyi and Xiong, Zhiwei},
    title     = {Event-Based Video Reconstruction Using Transformer},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2563-2572}
}


Motion Deblurring With Real Events

Fang Xu, Lei Yu, Bishan Wang, Wen Yang, Gui-Song Xia, Xu Jia, Zhendong Qiao, Jianzhuang Liu

Abstract: In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency. To achieve this end, optical flows are predicted from events, with which the blurry consistency and photometric consistency are exploited to enable self-supervision on the deblurring network with real-world data. Furthermore, a piece-wise linear motion model is proposed to take into account motion non-linearities and thus leads to an accurate model for the physical formation of motion blurs in the real-world scenario. Extensive evaluation on both synthetic and real motion blur datasets demonstrates that the proposed algorithm bridges the gap between simulated and real-world motion blurs and shows remarkable performance for event-based motion deblurring in real-world scenarios.

@InProceedings{Xu_2021_ICCV,
    author    = {Xu, Fang and Yu, Lei and Wang, Bishan and Yang, Wen and Xia, Gui-Song and Jia, Xu and Qiao, Zhendong and Liu, Jianzhuang},
    title     = {Motion Deblurring With Real Events},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2583-2592}
}


Yu-Chih-Tuan Hu, Bo-Han Kung, Daniel Stanley Tan, Jun-Cheng Chen, Kai-Lung Hua, Wen-Huang Cheng

Abstract: Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches. This leads to conspicuous and attention-grabbing patterns for the generated patches which can be easily identified by humans. To address this issue, we propose a method to craft physical adversarial patches for object detectors by leveraging the learned image manifold of a pretrained generative adversarial network (GAN) (e.g., BigGAN and StyleGAN) upon real-world images. Through sampling the optimal image from the GAN, our method can generate natural looking adversarial patches while maintaining high attack performance. With extensive experiments on both digital and physical domains and several independent subjective surveys, the results show that our proposed method produces significantly more realistic and natural looking patches than several state-of-the-art baselines while achieving competitive attack performance.

@InProceedings{Hu_2021_ICCV,
    author    = {Hu, Yu-Chih-Tuan and Kung, Bo-Han and Tan, Daniel Stanley and Chen, Jun-Cheng and Hua, Kai-Lung and Cheng, Wen-Huang},
    title     = {Naturalistic Physical Adversarial Patch for Object Detectors},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {7848-7857}
}


Seokju Lee, Francois Rameau, Fei Pan, In So Kweon

Abstract: Estimating the motion of the camera together with the 3D structure of the scene from a monocular vision system is a complex task that often relies on the so-called scene rigidity assumption. When observing a dynamic environment, this assumption is violated which leads to an ambiguity between the ego-motion of the camera and the motion of the objects. To solve this problem, we present a self-supervised learning framework for 3D object motion field estimation from monocular videos. Our contributions are two-fold. First, we propose a two-stage projection pipeline to explicitly disentangle the camera ego-motion and the object motions with dynamics attention module, called DAM. Specifically, we design an integrated motion model that estimates the motion of the camera and object in the first and second warping stages, respectively, controlled by the attention module through a shared motion encoder. Second, we propose an object motion field estimation through contrastive sample consensus, called CSAC, taking advantage of weak semantic prior (bounding box from an object detector) and geometric constraints (each object respects the rigid body motion model). Experiments on KITTI, Cityscapes, and Waymo Open Dataset demonstrate the relevance of our approach and show that our method outperforms state-of-the-art algorithms for the tasks of self-supervised monocular depth estimation, object motion segmentation, monocular scene flow estimation, and visual odometry.

@InProceedings{Lee_2021_ICCV,
    author    = {Lee, Seokju and Rameau, Francois and Pan, Fei and Kweon, In So},
    title     = {Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {4862-4871}
}


Mingtao Feng, Zhen Li, Qi Li, Liang Zhang, XiangDong Zhang, Guangming Zhu, Hui Zhang, Yaonan Wang, Ajmal Mian

Abstract: 3D object grounding aims to locate the most relevant target object in a raw point cloud scene based on a free-form language description. Understanding complex and diverse descriptions, and lifting them directly to a point cloud is a new and challenging topic due to the irregular and sparse nature of point clouds. There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object. In this paper, we address all three challenges. Firstly, we propose a language scene graph module to capture the rich structure and long-distance phrase correlations. Secondly, we introduce a multi-level 3D proposal relation graph module to extract the object-object and object-scene co-occurrence relationships, and strengthen the visual features of the initial proposals. Lastly, we develop a description guided 3D visual graph module to encode global contexts of phrases and proposals by a nodes matching strategy. Extensive experiments on challenging benchmark datasets (ScanRefer and Nr3D) show that our algorithm outperforms existing state-of-the-art. Our code is available at https://github.com/PNXD/FFL-3DOG.

@InProceedings{Feng_2021_ICCV,
    author    = {Feng, Mingtao and Li, Zhen and Li, Qi and Zhang, Liang and Zhang, XiangDong and Zhu, Guangming and Zhang, Hui and Wang, Yaonan and Mian, Ajmal},
    title     = {Free-Form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3722-3731}
}


Rindra Ramamonjison, Amin Banitalebi-Dehkordi, Xinyu Kang, Xiaolong Bai, Yong Zhang

Abstract: This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label noise, our method integrates a novel domain-centric data augmentation, a gradual self-labeling adaptation procedure, and a teacher-guided fine-tuning mechanism. Using our method, target domain samples can be leveraged to adapt object detection models without changing the model architecture or generating synthetic data. When applied to image corruptions and high-level cross-domain adaptation benchmarks, our method outperforms prior baselines on multiple domain adaptation benchmarks. SimROD achieves new state-of-the-art on standard real-to-synthetic and cross-camera setup benchmarks. On the image corruption benchmark, models adapted with our method achieved a relative robustness improvement of 15-25% AP50 on Pascal-C and 5-6% AP on COCO-C and Cityscapes-C. On the cross-domain benchmark, our method outperformed the best baseline performance by up to 8% and 4% AP50 on Comic and Watercolor respectively.

@InProceedings{Ramamonjison_2021_ICCV,
    author    = {Ramamonjison, Rindra and Banitalebi-Dehkordi, Amin and Kang, Xinyu and Bai, Xiaolong and Zhang, Yong},
    title     = {SimROD: A Simple Adaptation Method for Robust Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {3570-3579}
}


Yi-Lun Wu, Hong-Han Shuai, Zhi-Rui Tam, Hong-Yu Chiu

Abstract: In this paper, we propose a novel normalization method called gradient normalization (GN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the sharp gradient space. Unlike existing work such as gradient penalty and spectral normalization, the proposed GN only imposes a hard 1-Lipschitz constraint on the discriminator function, which increases the capacity of the discriminator. Moreover, the proposed gradient normalization can be applied to different GAN architectures with little modification. Extensive experiments on four datasets show that GANs trained with gradient normalization outperform existing methods in terms of both Frechet Inception Distance and Inception Score.

@InProceedings{Wu_2021_ICCV,
    author    = {Wu, Yi-Lun and Shuai, Hong-Han and Tam, Zhi-Rui and Chiu, Hong-Yu},
    title     = {Gradient Normalization for Generative Adversarial Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6373-6382}
}


Zhikang Zou, Xiaoqing Ye, Liang Du, Xianhui Cheng, Xiao Tan, Li Zhang, Jianfeng Feng, Xiangyang Xue, Errui Ding

Abstract: Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. Our objective is to dig into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information underlying the entire task. We introduce a Dynamic Feature Reflecting Network, named DFR-Net, which contains two novel standalone modules: (i) the Appearance-Localization Feature Reflecting module (ALFR) that first separates task-specific features and then self-mutually reflects the reciprocal features; (ii) the Dynamic Intra-Trading module (DIT) that adaptively realigns the training processes of various sub-tasks via a self-learning manner. Extensive experiments on the challenging KITTI dataset demonstrate the effectiveness and generalization of DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set (till March 16th, 2021). The proposed method is also easy to be plug-and-play in many cutting-edge 3D detection frameworks at negligible cost to boost performance. The code will be made publicly available.

@InProceedings{Zou_2021_ICCV,
    author    = {Zou, Zhikang and Ye, Xiaoqing and Du, Liang and Cheng, Xianhui and Tan, Xiao and Zhang, Li and Feng, Jianfeng and Xue, Xiangyang and Ding, Errui},
    title     = {The Devil Is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2713-2722}
}


Frank Julca-Aguilar, Jason Taylor, Mario Bijelic, Fahim Mannan, Ethan Tseng, Felix Heide

Abstract: Today's state-of-the-art methods for 3D object detection are based on lidar, stereo, or monocular cameras. Lidar-based methods achieve the best accuracy, but have a large footprint, high cost, and mechanically-limited angular sampling rates, resulting in low spatial resolution at long ranges. Recent approaches using low-cost monocular or stereo cameras promise to overcome these limitations but struggle in low-light or low-contrast regions as they rely on passive CMOS sensors. We propose a novel 3D object detection modality that exploits temporal illumination cues from a low-cost monocular gated imager. We introduce a novel deep detection architecture, Gated3D, that is tailored to temporal illumination cues in gated images. This modality allows us to exploit mature 2D object feature extractors that guide the 3D predictions through a frustum segment estimation. We assess the proposed method experimentally on a 3D detection dataset that includes gated images captured over 10,000 km of driving data. We validate that our method outperforms state-of-the-art monocular and stereo methods, opening up a new sensor modality as an avenue to replace lidar in autonomous driving. https://light.princeton.edu/gated3d

@InProceedings{Julca-Aguilar_2021_ICCV,
    author    = {Julca-Aguilar, Frank and Taylor, Jason and Bijelic, Mario and Mannan, Fahim and Tseng, Ethan and Heide, Felix},
    title     = {Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2938-2948}
}


Jiannan Guo, Haochen Shi, Yangyang Kang, Kun Kuang, Siliang Tang, Zhuoren Jiang, Changlong Sun, Fei Wu, Yueting Zhuang

Abstract: The performance of computer vision models significantly improves with more labeled data. However, the acquisition of labeled data is limited by the high cost. To mitigate the reliance on large labeled datasets, active learning (AL) and semi-supervised learning (SSL) are frequently adopted. Although current mainstream methods begin to combine SSL and AL (SSL-AL) to excavate the diverse expressions of unlabeled samples, these methods' fully supervised task models are still trained only with labeled data. Besides, these method's SSL-AL frameworks suffer from mismatch problems. Here, we propose a graph-based SSL-AL framework to unleash the SSL task models' power and make an effective SSL-AL interaction. In the framework, SSL leverages graph-based label propagation to deliver virtual labels to unlabeled samples, rendering AL samples' structural distribution and boosting AL. AL finds samples near the clusters' boundary to help SSL perform better label propagation by exploiting adversarial examples. The information exchange in the closed-loop realizes mutual enhancement of SSL and AL. Experimental results show that our method outperforms the state-of-the-art methods against classification and segmentation benchmarks.

@InProceedings{Guo_2021_ICCV,
    author    = {Guo, Jiannan and Shi, Haochen and Kang, Yangyang and Kuang, Kun and Tang, Siliang and Jiang, Zhuoren and Sun, Changlong and Wu, Fei and Zhuang, Yueting},
    title     = {Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2896-2905}
}


Edgar Sucar, Shikun Liu, Joseph Ortiz, Andrew J. Davison

Abstract: We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior data, building a dense, scene-specific implicit 3D model of occupancy and colour which is also immediately used for tracking. Achieving real-time SLAM via continual training of a neural network against a live image stream requires significant innovation. Our iMAP algorithm uses a keyframe structure and multi-processing computation flow, with dynamic information-guided pixel sampling for speed, with tracking at 10 Hz and global map updating at 2 Hz. The advantages of an implicit MLP over standard dense SLAM techniques include efficient geometry representation with automatic detail control and smooth, plausible filling-in of unobserved regions such as the back surfaces of objects.

@InProceedings{Sucar_2021_ICCV,
    author    = {Sucar, Edgar and Liu, Shikun and Ortiz, Joseph and Davison, Andrew J.},
    title     = {iMAP: Implicit Mapping and Positioning in Real-Time},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6229-6238}
}