pwc-archive/papers-with-abstracts huggingface dataset:
Pyramid Architecture Search for Real-Time Image Deblurring
Authors: Xiaobin Hu, Wenqi Ren, Kaicheng Yu, Kaihao Zhang, Xiaochun Cao, Wei Liu, Bjoern Menze
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Binarization, Deblurring, Image Deblurring, Neural Architecture Search, Video Deblurring

    Multi-scale and multi-patch deep models have been shown effective in removing blurs of dynamic scenes. However, these methods still have one major obstacle: manually designing a lightweight and high-efficiency network is challenging and time-consuming. To tackle this problem, we propose a novel deblurring method, dubbed PyNAS (pyramid neural architecture search network), towards automatically designing hyper-parameters including the scales, patches, and standard cell operators. The proposed PyNAS adopts gradient-based search strategies and innovatively searches the hierarchy patch and scale scheme not limited to the cell searching. Specifically, we introduce a hierarchical search strategy tailored for the multi-scale and multi-patch deblurring task. The strategy follows the principle that the first distinguishes between the top-level (pyramid-scales and pyramid-patches) and bottom-level variables (cell operators) and then searches multi-scale variables using the top-to-bottom principle. During the search stage, PyNAS employs an early stopping strategy to avoid the collapse and computational issue. Furthermore, we use a path-level binarization mechanism for multi-scale cell searching to save memory consumption. Our model is a real-time deblurring algorithm (around 58 fps) for 720p images while achieves state-of-the-art deblurring performance on the GoPro and Video Deblurring dataset.
Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision
Authors: Liangke Gui, Adrien Bardes, Ruslan Salakhutdinov, Alexander Hauptmann, Martial Hebert, Yu-Xiong Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Contrastive Learning, Few-Shot Learning, Hallucination

    Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks. This work investigates two important yet overlooked natural supervision signals for guiding the hallucination process -- (i) extrinsic: classifiers trained on hallucinated examples should be close to strong classifiers that would be learned from a large amount of real examples; and (ii) intrinsic: clusters of hallucinated and real examples belonging to the same class should be pulled together, while simultaneously pushing apart clusters of hallucinated and real examples from different classes. We achieve (i) by introducing an additional mentor model on data-abundant base classes for directing the hallucinator, and achieve (ii) by performing contrastive learning between hallucinated and real examples. As a general, model-agnostic framework, our dual mentor- and self-directed (DMAS) hallucinator significantly improves few-shot learning performance on widely used benchmarks in various scenarios.
MLVSNet: Multi-Level Voting Siamese Network for 3D Visual Tracking
Authors: Zhoutao Wang, Qian Xie, Yu-Kun Lai, Jing Wu, Kun Long, Jun Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Object Detection, object-detection, Object Detection, Visual Tracking

    Benefiting from the excellent performance of Siamese-based trackers, huge progress on 2D visual tracking has been achieved. However, 3D visual tracking is still under-explored. Inspired by the idea of Hough voting in 3D object detection, in this paper, we propose a Multi-level Voting Siamese Network (MLVSNet) for 3D visual tracking from outdoor point cloud sequences. To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the final level feature as in previous methods. We also design an efficient and lightweight Target-Guided Attention (TGA) module to transfer the target information and highlight the target points in the search area. Moreover, we propose a Vote-cluster Feature Enhancement (VFE) module to exploit the relationships between different vote clusters. Extensive experiments on the 3D tracking benchmark of KITTI dataset demonstrate that our MLVSNet outperforms state-of-the-art methods with significant margins. Code will be available at https://github.com/CodeWZT/MLVSNet.
Distinctiveness Oriented Positional Equilibrium for Point Cloud Registration
Authors: Taewon Min, Chonghyuk Song, Eunseok Kim, Inwook Shim
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Point Cloud Registration

    Recent state-of-the-art learning-based approaches to point cloud registration have largely been based on graph neural networks (GNN). However, these prominent GNN backbones suffer from the indistinguishable features problem associated with over-smoothing and structural ambiguity of the high-level features, a crucial bottleneck to point cloud registration that has evaded scrutiny in the recent relevant literature. To address this issue, we propose the Distinctiveness oriented Positional Equilibrium (DoPE) module, a novel positional embedding scheme that significantly improves the distinctiveness of the high-level features within both the source and target point clouds, resulting in superior point matching and hence registration accuracy. Specifically, we use the DoPE module in an iterative registration framework, whereby the two point clouds are gradually registered via rigid transformations that are computed from DoPE's position-aware features. With every successive iteration, the DoPE module feeds increasingly consistent positional information to would-be corresponding pairs, which in turn enhances the resulting point-to-point correspondence predictions used to estimate the rigid transformation. Within only a few iterations, the network converges to a desired equilibrium, where the positional embeddings given to matching pairs become essentially identical. We validate the effectiveness of DoPE through comprehensive experiments on various registration benchmarks, registration task settings, and prominent backbones, yielding unprecedented performance improvement across all combinations.
TransView: Inside, Outside, and Across the Cropping View Boundaries
Authors: Zhiyu Pan, Zhiguo Cao, Kewei Wang, Hao Lu, Weicai Zhong
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Relation

    We show that relation modeling between visual elements matters in cropping view recommendation. Cropping view recommendation addresses the problem of image recomposition conditioned on the composition quality and the ranking of views (cropped sub-regions). This task is challenging because the visual difference is subtle when a visual element is reserved or removed. Existing methods represent visual elements by extracting region-based convolutional features inside and outside the cropping view boundaries, without probing a fundamental question: why some visual elements are of interest or of discard? In this work, we observe that the relation between different visual elements significantly affects their relative positions to the desired cropping view, and such relation can be characterized by the attraction inside/outside the cropping view boundaries and the repulsion across the boundaries. By instantiating a transformer-based solution that represents visual elements as visual words and that models the dependencies between visual words, we report not only state of-the-art performance on public benchmarks, but also interesting visualizations that depict the attraction and repulsion between visual elements, which may shed light on what makes for effective cropping view recommendation.
Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments
Authors: Difei Gao, Ruiping Wang, Ziyi Bai, Xilin Chen
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Question Answering, Video Question Answering

    Visual understanding goes well beyond the study of images or videos on the web. To achieve complex tasks in volatile situations, the human can deeply understand the environment, quickly perceive events happening around, and continuously track objects' state changes, which are still challenging for current AI systems. To equip AI system with the ability to understand dynamic ENVironments, we build a video Question Answering dataset named Env-QA. Env-QA contains 23K egocentric videos, where each video is composed of a series of events about exploring and interacting in the environment. It also provides 85K questions to evaluate the ability of understanding the composition, layout, and state changes of the environment presented by the events in videos. Moreover, we propose a video QA model, Temporal Segmentation and Event Attention network (TSEA), which introduces event-level video representation and corresponding attention mechanisms to better extract environment information and answer questions. Comprehensive experiments demonstrate the effectiveness of our framework and show the formidable challenges of Env-QA in terms of long-term state tracking, multi-event temporal reasoning and event counting, etc.
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning
Authors: Jiahe Shi, YaLi Li, Shengjin Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Diversity, Generative Adversarial Network, Image Captioning, Language Modeling, Language Modelling, Reinforcement Learning (RL)

    Human-oriented image captioning with both high diversity and accuracy is a challenging task in vision+language modeling. The reinforcement learning (RL) based frameworks promote the accuracy of image captioning, yet seriously hurt the diversity. In contrast, other methods based on variational auto-encoder (VAE) or generative adversarial network (GAN) can produce diverse yet less accurate captions. In this work, we devote our attention to promote the diversity of RL-based image captioning. To be specific, we devise a partial off-policy learning scheme to balance accuracy and diversity. First, we keep the model exposed to varied candidate captions by sampling from the initial state before RL launched. Second, a novel criterion named max-CIDEr is proposed to serve as the reward for promoting diversity. We combine the above-mentioned off-policy strategy with the on-policy one to moderate the exploration effect, further balancing the diversity and accuracy for human-like image captioning. Experiments show that our method locates the closest to human performance in the diversity-accuracy space, and achieves the highest Pearson correlation as 0.337 with human performance.
DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation
Authors: Mohammad Mahdi Johari, Camilla Carta, Francois Fleuret
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Depth Estimation, Optical Flow Estimation

    We present DepthInSpace, a self-supervised deep-learning method for depth estimation using a structured-light camera. The design of this method is motivated by the commercial use case of embedded depth sensors in nowadays smartphones. We first propose to use estimated optical flow from ambient information of multiple video frames as a complementary guide for training a single-frame depth estimation network, helping to preserve edges and reduce over-smoothing issues. Utilizing optical flow, we also propose to fuse the data of multiple video frames to get a more accurate depth map. In particular, fused depth maps are more robust in occluded areas and incur less in flying pixels artifacts. We finally demonstrate that these more precise fused depth maps can be used as self-supervision for fine-tuning a single-frame depth estimation network to improve its performance. Our models' effectiveness is evaluated and compared with state-of-the-art models on both synthetic and our newly introduced real datasets. The implementation code, training procedure, and both synthetic and captured real datasets are available at https://www.idiap.ch/paper/depthinspace.
HiNet: Deep Image Hiding by Invertible Network
Authors: Junpeng Jing, Xin Deng, Mai Xu, Jianyi Wang, Zhenyu Guan
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Steganography

    Image hiding aims to hide a secret image into a cover image in an imperceptible way, and then recover the secret image perfectly at the receiver end. Capacity, invisibility and security are three primary challenges in image hiding task. This paper proposes a novel invertible neural network (INN) based framework, HiNet, to simultaneously overcome the three challenges in image hiding. For large capacity, we propose an inverse learning mechanism by simultaneously learning the image concealing and revealing processes. Our method is able to achieve the concealing of a full-size secret image into a cover image with the same size. For high invisibility, instead of pixel domain hiding, we propose to hide the secret information in wavelet domain. Furthermore, we propose a new low-frequency wavelet loss to constrain that secret information is hidden in high-frequency wavelet sub-bands, which significantly improves the hiding security. Experimental results show that our HiNet significantly outperforms other state-of-the-art image hiding methods, with more than 10 dB PSNR improvement in secret image recovery on ImageNet, COCO and DIV2K datasets. Codes are available at https://github.com/TomTomTommi/HiNet.
Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion
Authors: Qinghao Ye, Xiyue Shen, Yuan Gao, ZiRui Wang, Qi Bi, Ping Li, Guang Yang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Highlight Detection, Model Optimization

    Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i.e., where the highlight moments are in long videos) for supervised learning. In this paper, we propose a novel weakly supervised method that can learn to detect highlights by mining video characteristics with video level annotations (topic tags) only. Particularly, we exploit audio-visual features to enhance video representation and take temporal cues into account for improving detection performance. Our contributions are threefold: 1) we propose an audio-visual tensor fusion mechanism that efficiently models the complex association between two modalities while reducing the gap of the heterogeneity between the two modalities; 2) we introduce a novel hierarchical temporal context encoder to embed local temporal clues in between neighboring segments; 3) finally, we alleviate the gradient vanishing problem theoretically during model optimization with attention-gated instance aggregation. Extensive experiments on two benchmark datasets (YouTube Highlights and TVSum) have demonstrated our method outperforms other state-of-the-art methods with remarkable improvements.
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition
Authors: Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: GPU, Neural Architecture Search, Vocal Bursts Intensity Prediction

    Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation. To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures. The Zen-Score represents the network expressivity and positively correlates with the model accuracy. The calculation of Zen-Score only takes a few forward inferences through a randomly initialized network, without training network parameters. Built upon the Zen-Score, we further propose a new NAS algorithm, termed as Zen-NAS, by maximizing the Zen-Score of the target network under given inference budgets. Within less than half GPU day, Zen-NAS is able to directly search high performance architectures in a data-free style. Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet. Searching and training code as well as pre-trained models are available from https://github.com/idstcv/ZenNAS.
Scene Context-Aware Salient Object Detection
Authors: Avishek Siris, Jianbo Jiao, Gary K.L. Tam, Xianghua Xie, Rynson W.H. Lau
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Object, object-detection, Object Detection, Saliency Detection, Salient Object Detection

    Salient object detection identifies objects in an image that grab visual attention. Although contextual features are considered in recent literature, they often fail in real-world complex scenarios. We observe that this is mainly due to two issues: First, most existing datasets consist of simple foregrounds and backgrounds that hardly represent real-life scenarios. Second, current methods only learn contextual features of salient objects, which are insufficient to model high-level semantics for saliency reasoning in complex scenes. To address these problems, we first construct a new large-scale dataset with complex scenes in this paper. We then propose a context-aware learning approach to explicitly exploit the semantic scene contexts. Specifically, two modules are proposed to achieve the goal: 1) a Semantic Scene Context Refinement module to enhance contextual features learned from salient objects with scene context, and 2) a Contextual Instance Transformer to learn contextual relations between objects and scene context. To our knowledge, such high-level semantic contextual information of image scenes is under-explored for saliency detection in the literature. Extensive experiments demonstrate that the proposed approach outperforms state-of-the-art techniques in complex scenarios for saliency detection, and transfers well to other existing datasets. The code and dataset are available at https://github.com/SirisAvishek/Scene_Context_Aware_Saliency.
Multi-View 3D Reconstruction With Transformers
Authors: Dan Wang, Xinrui Cui, Xun Chen, Zhengxia Zou, Tianyang Shi, Septimiu Salcudean, Z. Jane Wang, Rabab Ward
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Object Reconstruction, 3D Reconstruction, Multi-View 3D Reconstruction, Object Reconstruction

    Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these methods - view feature extraction and multi-view fusion, are usually investigated separately, and the relations among multiple input views are rarely explored. Inspired by the recent great success in Transformer models, we reformulate the multi-view 3D reconstruction as a sequence-to-sequence prediction problem and propose a framework named 3D Volume Transformer. Unlike previous CNN-based methods using a separate design, we unify the feature extraction and view fusion in a single Transformer network. A natural advantage of our design lies in the exploration of view-to-view relationships using self-attention among multiple unordered inputs. On ShapeNet - a large-scale 3D reconstruction benchmark, our method achieves a new state-of-the-art accuracy in multi-view reconstruction with fewer parameters (70% less) than CNN-based methods. Experimental results also suggest the strong scaling capability of our method. Our code will be made publicly available.
Dance With Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos
Authors: Didik Purwanto, Yie-Tarng Chen, Wen-Hsien Fang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Anomaly Detection

    This paper proposes a novel weakly supervised approach for anomaly detection, which begins with a relation-aware feature extractor to capture the multi-scale convolutional neural network (CNN) features from a video. Afterwards, self-attention is integrated with conditional random fields (CRFs), the core of the network, to make use of the ability of self-attention in capturing the short-range correlations of the features and the ability of CRFs in learning the inter-dependencies of these features. Such a framework can learn not only the spatio-temporal interactions among the actors which are important for detecting complex movements, but also their short- and long-term dependencies across frames. Also, to deal with both local and non-local relationships of the features, a new variant of self-attention is developed by taking into consideration a set of cliques with different temporal localities. Moreover, a contrastive multi-instance learning scheme is considered to broaden the gap between the normal and abnormal instances, resulting in more accurate abnormal discrimination. Simulations reveal that the new method provides superior performance to the state-of-the-art works on the widespread UCF-Crime and ShanghaiTech datasets.
Region-Aware Contrastive Learning for Semantic Segmentation
Authors: Hanzhe Hu, Jinshi Cui, LiWei Wang
Date: 2021-01-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: Contrastive Learning, Semantic Segmentation

    Recent works have made great success in semantic segmentation by exploiting contextual information in a local or global manner within individual image and supervising the model with pixel-wise cross entropy loss. However, from the holistic view of the whole dataset, semantic relations not only exist inside one single image, but also prevail in the whole training data, which makes solely considering intra-image correlations insufficient. Inspired by recent progress in unsupervised contrastive learning, we propose the region-aware contrastive learning (RegionContrast) for semantic segmentation in the supervised manner. In order to enhance the similarity of semantically similar pixels while keeping the discrimination from others, we employ contrastive learning to realize this objective. With the help of memory bank, we explore to store all the representative features into the memory. Without loss of generality, to efficiently incorporate all training data into the memory bank while avoiding taking too much computation resource, we propose to construct region centers to represent features from different categories for every image. Hence, the proposed region-aware contrastive learning is performed in a region level for all the training data, which saves much more memory than methods exploring the pixel-level relations. The proposed RegionContrast brings little computation cost during training and requires no extra overhead for testing. Extensive experiments demonstrate that our method achieves state-of-the-art performance on three benchmark datasets including Cityscapes, ADE20K and COCO Stuff.