pwc-archive/papers-with-abstracts huggingface dataset:
RFNet: Region-Aware Fusion Network for Incomplete Multi-Modal Brain Tumor Segmentation
Authors: Yuhang Ding, Xin Yu, Yi Yang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Brain Tumor Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

    Most existing brain tumor segmentation methods usually exploit multi-modal magnetic resonance imaging (MRI) images to achieve high segmentation performance. However, the problem of missing certain modality images often happens in clinical practice, thus leading to severe segmentation performance degradation. In this work, we propose a Region-aware Fusion Network (RFNet) that is able to exploit different combinations of multi-modal data adaptively and effectively for tumor segmentation. Considering different modalities are sensitive to different brain tumor regions, we design a Region-aware Fusion Module (RFM) in RFNet to conduct modal feature fusion from available image modalities according to disparate regions. Benefiting from RFM, RFNet can adaptively segment tumor regions from an incomplete set of multi-modal images by effectively aggregating modal features. Furthermore, we also develop a segmentation-based regularizer to prevent RFNet from the insufficient and unbalanced training caused by the incomplete multi-modal data. Specifically, apart from obtaining segmentation results from fused modal features, we also segment each image modality individually from the corresponding encoded features. In this manner, each modal encoder is forced to learn discriminative features, thus improving the representation ability of the fused features. Remarkably, extensive experiments on BRATS2020, BRATS2018 and BRATS2015 datasets demonstrate that our RFNet outperforms the state-of-the-art significantly.
Event-Based Video Reconstruction Using Transformer
Authors: Wenming Weng, Yueyi Zhang, Zhiwei Xiong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Event-based Object Segmentation, Event-Based Video Reconstruction, Video Reconstruction

    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
Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting
Authors: Shijie Li, Yanying Zhou, Jinhui Yi, Juergen Gall
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Autonomous Vehicles, Navigate, Trajectory Forecasting

    Trajectory forecasting is a crucial step for autonomous vehicles and mobile robots in order to navigate and interact safely. In order to handle the spatial interactions between objects, graph-based approaches have been proposed. These methods, however, model motion on a frame-to-frame basis and do not provide a strong temporal model. To overcome this limitation, we propose a compact model called Spatial-Temporal Consistency Network (STC-Net). In STC-Net, dilated temporal convolutions are introduced to model long-range dependencies along each trajectory for better temporal modeling while graph convolutions are employed to model the spatial interaction among different trajectories. Furthermore, we propose a feature-wise convolution to generate the predicted trajectories in one pass and refine the forecast trajectories together with the reconstructed observed trajectories. We demonstrate that STC-Net generates spatially and temporally consistent trajectories and outperforms other graph-based methods. Since STC-Net requires only 0.7k parameters and forecasts the future with a latency of only 1.3ms, it advances the state-of-the-art and satisfies the requirements for realistic applications.
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity
Authors: Yukun Su, Guosheng Lin, Qingyao Wu
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Recognition, Representation Learning, Self-Supervised Learning, Skeleton Based Action Recognition

    Recently, self-supervised learning (SSL) has been proved very effective and it can help boost the performance in learning representations from unlabeled data in the image domain. Yet, very little is explored about its usefulness in 3D skeleton-based action recognition understanding. Directly applying existing SSL techniques for 3D skeleton learning, however, suffers from trivial solutions and imprecise representations. To tackle these drawbacks, we consider perceiving the consistency and continuity of motion at different playback speeds are two critical issues. To this end, we propose a novel SSL method to learn the 3D skeleton representation in an efficacious way. Specifically, by constructing a positive clip (speed-changed) and a negative clip (motion-broken) of the sampled action sequence, we encourage the positive pairs closer while pushing the negative pairs to force the network to learn the intrinsic dynamic motion consistency information. Moreover, to enhance the learning features, skeleton interpolation is further exploited to model the continuity of human skeleton data. To validate the effectiveness of the proposed method, extensive experiments are conducted on Kinetics, NTU60, NTU120, and PKUMMD datasets with several alternative network architectures. Experimental evaluations demonstrate the superiority of our approach and through which, we can gain significant performance improvement without using extra labeled data.
Face Image Retrieval With Attribute Manipulation
Authors: Alireza Zaeemzadeh, Shabnam Ghadar, Baldo Faieta, Zhe Lin, Nazanin Rahnavard, Mubarak Shah, Ratheesh Kalarot
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Attribute, Face Image Retrieval, Image Retrieval, Retrieval

    Current face image retrieval solutions are limited, since they treat different facial attributes the same and cannot incorporate user's preference for a subset of attributes in their search criteria. This paper introduces a new face image retrieval framework, where the input face query is augmented by both an adjustment vector that specifies the desired modifications to the facial attributes, and a preference vector that assigns different levels of importance to different attributes. For example, a user can ask for retrieving images similar to a query image, but with a different hair color, and no preference for absence/presence of eyeglasses in the results. To achieve this, we propose to disentangle the semantics, corresponding to various attributes, by learning a set of sparse and orthogonal basis vectors in the latent space of StyleGAN. Such basis vectors are then employed to decompose the dissimilarity between face images in terms of dissimilarity between their attributes, assign preference to the attributes, and adjust the attributes in the query. Enforcing sparsity on the basis vectors helps us to disentangle the latent space and adjust each attribute independently from other attributes, while enforcing orthogonality facilitates preference assignment and the dissimilarity decomposition. The effectiveness of our approach is illustrated by achieving state-of-the-art results for the face image retrieval task.
Adversarial Attack on Deep Cross-Modal Hamming Retrieval
Authors: Chao Li, Shangqian Gao, Cheng Deng, Wei Liu, Heng Huang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Adversarial Attack, Cross-Modal Retrieval, Retrieval, Text Retrieval, Triplet

    Recently, Cross-Modal Hamming space Retrieval (CMHR) regains ever-increasing attention, mainly benefiting from the excellent representation capability of deep neural networks. On the other hand, the vulnerability of deep networks exposes a deep cross-modal retrieval system to various safety risks (e.g., adversarial attack). However, attacking deep cross-modal Hamming retrieval remains underexplored. In this paper, we propose an effective Adversarial Attack on Deep Cross-Modal Hamming Retrieval, dubbed AACH, which fools a target deep CMHR model in a black-box setting. Specifically, given a target model, we first construct its substitute model to exploit cross-modal correlations within hamming space, with which we create adversarial examples by limitedly querying from a target model. Furthermore, to enhance the efficiency of adversarial attacks, we design a triplet construction module to exploit cross-modal positive and negative instances. In this way, perturbations can be learned to fool the target model through pulling perturbed examples far away from the positive instances whereas pushing them close to the negative ones. Extensive experiments on three widely used cross-modal (image and text) retrieval benchmarks demonstrate the superiority of the proposed AACH. We find that AACH can successfully attack a given target deep CMHR model with fewer interactions, and that its performance is on par with previous state-of-the-art attacks.
Curvature Generation in Curved Spaces for Few-Shot Learning
Authors: Zhi Gao, Yuwei Wu, Yunde Jia, Mehrtash Harandi
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Few-Shot Learning

    Few-shot learning describes the challenging problem of recognizing samples from unseen classes given very few labeled examples. In many cases, few-shot learning is cast as learning an embedding space that assigns test samples to their corresponding class prototypes. Previous methods assume that data of all few-shot learning tasks comply with a fixed geometrical structure, mostly a Euclidean structure. Questioning this assumption that is clearly difficult to hold in real-world scenarios and incurs distortions to data, we propose to learn a task-aware curved embedding space by making use of the hyperbolic geometry. As a result, task-specific embedding spaces where suitable curvatures are generated to match the characteristics of data are constructed, leading to more generic embedding spaces. We then leverage on intra-class and inter-class context information in the embedding space to generate class prototypes for discriminative classification. We conduct a comprehensive set of experiments on inductive and transductive few-shot learning, demonstrating the benefits of our proposed method over existing embedding methods.
PoGO-Net: Pose Graph Optimization With Graph Neural Networks
Authors: Xinyi Li, Haibin Ling
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Camera Pose Estimation, Pose Estimation

    Accurate camera pose estimation or global camera re-localization is a core component in Structure-from-Motion (SfM) and SLAM systems. Given pair-wise relative camera poses, pose-graph optimization (PGO) involves solving for an optimized set of globally-consistent absolute camera poses. In this work, we propose a novel PGO scheme fueled by graph neural networks (GNN), namely PoGO-Net, to conduct the absolute camera pose regression leveraging multiple rotation averaging (MRA). Specifically, PoGO-Net takes a noisy view-graph as the input, where the nodes and edges are designed to encode the geometric constraints and local graph consistency. Besides, we address the outlier edge removal by exploiting an implicit edge-dropping scheme where the noisy or corrupted edges are effectively filtered out with parameterized networks. Furthermore, we introduce a joint loss function embedding MRA formulation such that the robust inference is capable of achieving real-time performances even for large-scale scenes. Our proposed network is trained end-to-end on public benchmarks, outperforming state-of-the-art approaches in extensive experiments that demonstrate the efficiency and robustness of our proposed network.
Naturalistic Physical Adversarial Patch for Object Detectors
Authors: Yu-Chih-Tuan Hu, Bo-Han Kung, Daniel Stanley Tan, Jun-Cheng Chen, Kai-Lung Hua, Wen-Huang Cheng
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Generative Adversarial Network, Object

    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.
Learning Attribute-Driven Disentangled Representations for Interactive Fashion Retrieval
Authors: Yuxin Hou, Eleonora Vig, Michael Donoser, Loris Bazzani
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Attribute, Disentanglement, Image Retrieval, Retrieval

    Interactive retrieval for online fashion shopping provides the ability of changing image retrieval results according to the user feedback. One common problem in interactive retrieval is that a specific user interaction (e.g., changing the color of a T-shirt) causes other aspects to change inadvertently (e.g., the results have a sleeve type different from that of the query). This is a consequence of existing methods learning visual representations that are entangled in the embedding space, which limits the controllability of the retrieved results. We propose to leverage on the semantics of visual attributes to train convolutional networks that learn attribute-specific subspaces for each attribute type to obtain disentangled representations. Operations, such as swapping out a particular attribute value for another, impact the attribute at hand and leave others untouched. We show that our model can be tailored to deal with different retrieval tasks while maintaining its disentanglement property. We obtained state-of-the-art performance on three interactive fashion retrieval tasks: attribute manipulation retrieval, conditional similarity retrieval, and outfit complementary item retrieval. We will make code and models publicly available.
Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance
Authors: Nian Liu, Wangbo Zhao, Dingwen Zhang, Junwei Han, Ling Shao
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: All, Graph Learning, object-detection, Object Detection, Saliency Detection, Saliency Prediction, Salient Object Detection

    The application of light field data in salient object detection is becoming increasingly popular in recent years. The difficulty lies in how to effectively fuse the features within the focal stack and how to cooperate them with the feature of the all-focus image. Previous methods usually fuse focal stack features via convolution or ConvLSTM, which are both less effective and ill-posed. In this paper, we model the information fusion within focal stack via graph networks. They introduce powerful context propagation from neighbouring nodes and also avoid ill-posed implementations. On the one hand, we construct local graph connections thus avoiding prohibitive computational costs of traditional graph networks. On the other hand, instead of processing the two kinds of data separately, we build a novel dual graph model to guide the focal stack fusion process using all-focus patterns. To handle the second difficulty, previous methods usually implement one-shot fusion for focal stack and all-focus features, hence lacking a thorough exploration of their supplements. We introduce a reciprocative guidance scheme and enable mutual guidance between these two kinds of information at multiple steps. As such, both kinds of features can be enhanced iteratively, finally benefiting the saliency prediction. Extensive experimental results show that the proposed models are all beneficial and we achieve significantly better results than state-of-the-art methods.
Interaction via Bi-Directional Graph of Semantic Region Affinity for Scene Parsing
Authors: Henghui Ding, HUI ZHANG, Jun Liu, Jiaxin Li, Zijian Feng, Xudong Jiang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Scene Parsing

    In this work, we devote to address the challenging problem of scene parsing. Previous methods, though capture context to exploit global clues, handle scene parsing as a pixel-independent task. However, it is well known that pixels in an image are highly correlated with each other, especially those from the same semantic region, while treating pixels independently fails to take advantage of such correlations. In this work, we treat each respective region in an image as a whole, and capture the structure topology as well as the affinity among different regions. To this end, we first divide the entire feature maps to different regions and extract respective global features from them. Next, we construct a directed graph whose nodes are regional features, and the edge connecting every two nodes is the affinity between the regional features they represent. After that, we transfer the affinity-aware nodes in the directed graph back to corresponding regions of the image, which helps to model the region dependencies and mitigate unrealistic results. In addition, to further boost the correlation among pixels, we propose a region-level loss that evaluates all pixels in a region as a whole and motivates the network to learn the exclusive regional feature per class. With the proposed approach, we achieves new state-of-the-art segmentation results on PASCAL-Context, ADE20K, and COCO-Stuff consistently.
Motion-Aware Dynamic Architecture for Efficient Frame Interpolation
Authors: Myungsub Choi, Suyoung Lee, Heewon Kim, Kyoung Mu Lee
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 2k, Video Frame Interpolation

    Video frame interpolation aims to synthesize accurate intermediate frames given a low-frame-rate video. While the quality of the generated frames is increasingly getting better, state-of-the-art models have become more and more computationally expensive. However, local regions with small or no motion can be easily interpolated with simple models and do not require such heavy compute, whereas some regions may not be correct even after inference through a large model. Thus, we propose an effective framework that assigns varying amounts of computation for different regions. Our dynamic architecture first calculates the approximate motion magnitude to use as a proxy for the difficulty levels for each region, and decides the depth of the model and the scale of the input. Experimental results show that static regions pass through a smaller number of layers, while the regions with larger motion are downscaled for better motion reasoning. In doing so, we demonstrate that the proposed framework can significantly reduce the computation cost (FLOPs) while maintaining the performance, often up to 50% when interpolating a 2K resolution video.
Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration From Single Noisy Volume With Sparsity Constraint
Authors: Zhidong Yang, Fa Zhang, Renmin Han
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Electron Tomography, Image Restoration, Self-Supervised Learning

    Cryo-Electron Tomography (cryo-ET) is a powerful tool for 3D cellular visualization. Due to instrumental limitations, cryo-ET images and their volumetric reconstruction suffer from extremely low signal-to-noise ratio. In this paper, we propose a novel end-to-end self-supervised learning model, the Sparsity Constrained Network (SC-Net), to restore volumetric image from single noisy data in cryo-ET. The proposed method only requires a single noisy data as training input and no ground-truth is needed in the whole training procedure. A new target function is proposed to preserve both local smoothness and detailed structure. Additionally, a novel procedure for the simulation of electron tomographic photographing is designed to help the evaluation of methods. Experiments are done on three simulated data and four real-world data. The results show that our method could produce a strong enhancement for a single very noisy cryo-ET volumetric data, which is much better than the state-of-the-art Noise2Void, and with a competitive performance comparing with Noise2Noise. Code is available at https://github.com/icthrm/SC-Net.
Multiple Pairwise Ranking Networks for Personalized Video Summarization
Authors: Yassir Saquil, Da Chen, Yuan He, Chuan Li, Yong-Liang Yang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Video Summarization

    In this paper, we investigate video summarization in the supervised setting. Since video summarization is subjective to the preference of the end-user, the design of a unique model is limited. In this work, we propose a model that provides personalized video summaries by conditioning the summarization process with predefined categorical user labels referred to as preferences. The underlying method is based on multiple pairwise rankers (called Multi-ranker), where the rankers are trained jointly to provide local summaries as well as a global summarization of a given video. In order to demonstrate the relevance and applications of our method in contrast with a classical global summarizer, we conduct experiments on multiple benchmark datasets, notably through a user study and comparisons with the state-of-art methods in the global video summarization task.
Transfusion: A Novel SLAM Method Focused on Transparent Objects
Authors: Yifan Zhu, Jiaxiong Qiu, Bo Ren
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Transparent objects

    Recently RGB-D sensors have become very popular in the area of Simultaneous Localisation and Mapping (SLAM). The RGB-D SLAM approach relies heavily on the accuracy of the input depth map. However, refraction and reflection of transparent objects will result in false depth input of RGB-D cameras, which makes the traditional RGB-D SLAM algorithm unable to work correctly in the presence of transparent objects. In this paper, we propose a novel SLAM approach called transfusion that allows transparent object existence and recovery in the video input. Our method is composed of two parts. Transparent Objects Cut Iterative Closest Points (TC-ICP)is first used to recover camera pose, detecting and removing transparent objects from input to reduce the trajectory errors. Then Transparent Objects Reconstruction (TO-Reconstruction) is used to reconstruct the transparent objects and opaque objects separately. The opaque objects are reconstructed with the traditional method, and the transparent objects are reconstructed with the visual hull-based method. To evaluate our algorithm, we construct a new RGB-D SLAM database containing 25 video sequences. Each sequence has at least one transparent object. Experiments show that our approach can work adequately in scenes contain transparent objects while the existing approach can not handle them. Our approach significantly improves the accuracy of the camera trajectory and the quality of environment reconstruction.