GenAI4ELab/papercli-papers huggingface dataset:
P²Net: Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
Authors: Zehao Yu; Lei Jin; Shenghua Gao
Venue: ECCV 2020 (main)

Abstract: This paper tackles the unsupervised depth estimation task in indoor environments. The task is extremely challenging because of the vast areas of non-texture regions in these scenes. These areas could overwhelm the optimization process in the commonly used unsupervised depth estimation framework proposed for outdoor environments. However, even when those regions are masked out, the performance is still unsatisfactory. In this paper, we argue that the poor performance suffers from the non-discriminative point-based matching. To this end, we propose P$^2$Net. We first extract points with large local gradients and adopt patches centered at each point as its representation. Multiview consistency loss is then defined over patches. This operation significantly improves the robustness of the network training. Furthermore, because those textureless regions in indoor scenes (g, wall, floor, roof, tc) usually correspond to planar regions, we propose to leverage superpixels as a plane prior. We enforce the predicted depth to be well fitted by a plane within each superpixel. Extensive experiments on NYUv2 and ScanNet show that our P$^2$Net outperforms existing approaches by a large margin."
Efficient Attention Mechanism for Visual Dialog that can Handle All the Interactions between Multiple Inputs
Authors: Van-Quang Nguyen; Masanori Suganuma; Takayuki Okatani
Venue: ECCV 2020 (main)

Abstract: It has been a primary concern in recent studies of vision and language tasks to design an effective attention mechanism dealing with interactions between the two modalities. The Transformer has recently been extended and applied to several bi-modal tasks, yielding promising results. For visual dialog, it becomes necessary to consider interactions between three or more inputs, i.e., an image, a question, and a dialog history, or even its individual dialog components. In this paper, we present a neural architecture named Light-weight Transformer for Many Inputs (LTMI) that can efficiently deal with all the interactions between multiple such inputs in visual dialog. It has a block structure similar to the Transformer and employs the same design of attention computation, whereas it has only a small number of parameters, yet has sufficient representational power for the purpose. Assuming a standard setting of visual dialog, a layer built upon the proposed attention block has less than one-tenth of parameters as compared with its counterpart, a natural Transformer extension. The experimental results on the VisDial datasets validate the effectiveness of the proposed approach, showing improvements of the best NDCG score on the VisDial v1.0 dataset from 57.59 to 60.92 with a single model, from 64.47 to 66.53 with ensemble models, and even to 74.88 with additional finetuning."
Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting
Authors: Xiyang Liu; Jie Yang; Wenrui Ding; Tieqiang Wang; Zhijin Wang; Junjun Xiong
Venue: ECCV 2020 (main)

Abstract: The crowd counting task aims at estimating the number of people located in an image or a frame from videos. Existing methods widely adopt density maps as the training targets to optimize the point-to-point loss. While in testing phase, we only focus on the differences between the crowd numbers and the global summation of density maps, which indicate the inconsistency between the training targets and the evaluation criteria. To solve this problem, we introduce a new target, named local counting map (LCM), to obtain more accurate results than density map based approaches. Moreover, we also propose an adaptive mixture regression framework with three modules in a coarse-to-fine manner to further improve the precision of the crowd estimation: scale-aware module (SAM), mixture regression module (MRM) and adaptive soft interval module (ASIM). Specifically, SAM fully utilizes the context and multi-scale information from different convolutional features; MRM and ASIM perform more precise counting regression on local patches of images. Compared with current methods, the proposed method reports better performances on the typical datasets. The source code is available at https://github.com/xiyang1012/Local-Crowd-Counting ."
BIRNAT: Bidirectional Recurrent Neural Networks with Adversarial Training for Video Snapshot Compressive Imaging
Authors: Ziheng Cheng; Ruiying Lu; Zhengjue Wang; Hao Zhang; Bo Chen; Ziyi Meng; Xin Yuan
Venue: ECCV 2020 (main)

Abstract: We consider the problem of video snapshot compressive imaging (SCI), where multiple high-speed frames are coded by different masks and then summed to a single measurement. This measurement and the modulation masks are fed into our Recurrent Neural Network (RNN) to reconstruct the desired high-speed frames. Our end-to-end sampling and reconstruction system is dubbed BIdirectional Recurrent Neural networks with Adversarial Training (BIRNAT). To our best knowledge, this is the first time that recurrent networks are employed to SCI problem. Our proposed BIRNAT outperforms other deep learning based algorithms and the state-of-the-art optimization based algorithm, DeSCI, through exploiting the underlying correlation of sequential video frames. BIRNAT employs a deep convolutional neural network with Resblock and feature map self-attention (AttRes-CNN) to reconstruct the first frame, based on which bidirectional RNN (Bi-RNN) is utilized to reconstruct the following frames in a sequential manner. To improve the quality of the reconstructed video, BIRNAT is further equipped with the adversarial training besides the mean square error loss. Extensive results on both simulation and real data (from two SCI cameras) demonstrate the superior performance of our BIRNAT system."
Ultra Fast Structure-aware Deep Lane Detection
Authors: Zequn Qin; Huanyu Wang; Xi Li
Venue: ECCV 2020 (main)

Abstract: Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problem of challenging scenarios and speed. Inspired by human perception, the recognition of lanes under severe occlusion and extreme lighting conditions is mainly based on contextual and global information. Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at extremely fast speed and challenging scenarios. Specifically, we treat the process of lane detection as a row-based selecting problem using global features. With the help of row-based selecting, our formulation could significantly reduce the computational cost. Using a large receptive field on global features, we could also handle the challenging scenarios. Moreover, based on the formulation, we also propose a structural loss to explicitly model the structure of lanes. Extensive experiments on two lane detection benchmark datasets show that our method could achieve the state-of-the-art performance in terms of both speed and accuracy. A light weight version could even achieve 300+ frames per second with the same resolution, which is at least 4x faster than previous state-of-the-art methods. Our code is available at https://github.com/cfzd/Ultra-Fast-Lane-Detection."
Cross-Identity Motion Transfer for Arbitrary Objects through Pose-Attentive Video Reassembling
Authors: Subin Jeon; Seonghyeon Nam; Seoung Wug Oh; Seon Joo Kim
Venue: ECCV 2020 (main)

Abstract: We propose an attention-based networks for transferring motions between arbitrary objects. Given a source image(s) and a driving video, our networks animate the subject in the source images according to the motion in the driving video. In our attention mechanism, dense similarities between the learned keypoints in the source and the driving images are computed in order to retrieve the appearance information from the source images. Taking a different approach from the well-studied warping based models, our attention-based model has several advantages. By reassembling non-locally searched pieces from the source contents, our approach can produce more realistic outputs. Furthermore, our system can make use of multiple observations of the source appearance (e.g. front and sides of faces) to make the results more accurate. To reduce the training-testing discrepancy of the self-supervised learning, a novel cross-identity training scheme is additionally introduced. With the training scheme, our networks is trained to transfer motions between different subjects, as in the real testing scenario. Experimental results validate that our method produces visually pleasing results in various object domains, showing better performances compared to previous works."
Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN
Authors: Zhenwei He; Lei Zhang
Venue: ECCV 2020 (main)

Abstract: Conventional object detection models inevitably encounter a performance drop as the domain disparity exists. Unsupervised domain adaptive object detection is proposed recently to reduce the disparity between domains, where the source domain is label-rich while the target domain is label-agnostic. The existing models follow a parameter shared siamese structure for adversarial domain alignment, which, however, easily leads to the collapse and out-of-control risk of the source domain and brings negative impact to feature adaption. The main reason is that the labeling unfairness (asymmetry) between source and target makes the parameter sharing mechanism unable to adapt. Therefore, in order to avoid the source domain collapse risk caused by parameter sharing, we propose an asymmetric tri-way Faster-RCNN (ATF) for domain adaptive object detection. Our ATF model has two distinct merits: 1) A ancillary net supervised by source label is deployed to learn ancillary target features and simultaneously preserve the discrimination of source domain, which enhances the structural discrimination (object classification vs. bounding box regression) of domain alignment. 2) The asymmetric structure consisting of a chief net and an independent ancillary net essentially overcomes the parameter sharing aroused source risk collapse. The adaption safety of the proposed ATF detector is guaranteed. Extensive experiments on a number of datasets, including Cityscapes, Foggy-cityscapes, KITTI, Sim10k, Pascal VOC, Clipart and Watercolor, demonstrate the SOTA performance of our method."
Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition
Authors: Xiaobo Wang; Tianyu Fu; Shengcai Liao; Shuo Wang; Zhen Lei; Tao Mei
Venue: ECCV 2020 (main)

Abstract: Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices. The success of which mainly comes from two aspects: the designed student network and the exploited knowledge. However, current methods usually suffer from the low-capability of mobile-level student network and the unsatisfactory knowledge for distillation. In this paper, we propose a novel position-aware exclusivity to encourage large diversity among different filters of the same layer to alleviate the low-capability of student network. Moreover, we investigate the effect of several prevailing knowledge for face recognition distillation and conclude that the knowledge of feature consistency is more flexible and preserves much more information than others. Experiments on a variety of face recognition benchmarks have revealed the superiority of our method over the state-of-the-arts."
Learning Camera-Aware Noise Models
Authors: Ke-Chi Chang; Ren Wang; Hung-Jin Lin; Yu-Lun Liu; Chia-Ping Chen; Yu-Lin Chang; Hwann-Tzong Chen
Venue: ECCV 2020 (main)

Abstract: Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/"
Towards Precise Completion of Deformable Shapes
Authors: Oshri Halimi; Ido Imanuel; Or Litany; Giovanni Trappolini; Emanuele Rodolà; Leonidas Guibas; Ron Kimmel
Venue: ECCV 2020 (main)

Abstract: According to Aristotle, a philosopher in Ancient Greece, {\it ``the whole is greater than the sum of its parts''}. This statement was adopted to explain human perception by the Gestalt psychology school of thought in the twentieth century. Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner. More specifically, given the geometry of a full, articulated object in a given pose, as well as a partial scan of the same object in a different pose, we address the {\it new} problem of matching the part to the whole while simultaneously reconstructing the new pose from its partial observation. Our approach is data-driven, and takes the form of a Siamese autoencoder without the requirement of a consistent vertex labeling at inference time; as such, it can be used on unorganized point clouds as well as on triangle meshes. We demonstrate the practical effectiveness of our model in the applications of single-view deformable shape completion and dense shape correspondence, both on synthetic and real-world geometric data, where we outperform prior work on these tasks by a large margin."
Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration
Authors: Jiahao Li; Changhao Zhang; Ziyao Xu; Hangning Zhou; Chi Zhang
Venue: ECCV 2020 (main)

Abstract: In this paper, we propose a novel learning-based pipeline for partially overlapping 3D point cloud registration. The proposed model includes an iterative distance-aware similarity matrix convolution module to incorporate information from both the feature and Euclidean space into the pairwise point matching process. These convolution layers learn to match points based on joint information of the entire geometric features and Euclidean offset for each point pair, overcoming the disadvantage of matching by simply taking the inner product of feature vectors. Furthermore, a two-stage learnable point elimination technique is presented to improve computational efficiency and reduce false positive correspondence pairs. A novel mutual-supervision loss is proposed to train the model without extra annotations of keypoints. The pipeline can be easily integrated with both traditional (e.g. FPFH) and learning-based features. Experiments on partially overlapping and noisy point cloud registration show that our method outperforms the current state-of-the-art, while being more computationally efficient."
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
Authors: Amir Rahimi; Amirreza Shaban; Thalaiyasingam Ajanthan; Richard Hartley; Byron Boots
Venue: ECCV 2020 (main)

Abstract: Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. We study the problem of learning localization model on target classes with weakly supervised image labels, helped by a fully annotated source dataset. Typically, a WSOL model is first trained to predict class generic objectness scores on an off-the-shelf fully supervised source dataset and then it is progressively adapted to learn the objects in the weakly supervised target dataset. In this work, we argue that learning only an objectness function is a weak form of knowledge transfer and propose to learn a classwise pairwise similarity function that directly compares two input proposals as well. The combined localization model and the estimated object annotations are jointly learned in an alternating optimization paradigm as is typically done in standard WSOL methods. In contrast to the existing work that learns pairwise similarities, our approach optimizes a unified objective with convergence guarantee and it is computationally efficient for large-scale applications. Experiments on the COCO and ILSVRC 2013 detection datasets show that the performance of the localization model improves significantly with the inclusion of pairwise similarity function. For instance, in the ILSVRC dataset, the Correct Localization (CorLoc) performance improves from 72.8% to 78.2% which is a new state-of-the-art for WSOL task in the context of knowledge transfer."
Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
Authors: Xin Eric Wang; Vihan Jain; Eugene Ie; William Yang Wang; Zornitsa Kozareva; Sujith Ravi[2]
Venue: ECCV 2020 (main)

Abstract: Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, existing methods tend to overfit training data in seen environments and fail to generalize well in previously unseen environments. To close the gap between seen and unseen environments, we aim at learning a generalized navigation model from two novel perspectives:(1) we introduce a multitask navigation model that can be seamlessly trained on both Vision-Language Navigation (VLN) and Navigation from Dialog History (NDH) tasks, which benefits from richer natural language guidance and effectively transfers knowledge across tasks;(2) we propose to learn environment-agnostic representations for the navigation policy that are invariant among the environments seen during training, thus generalizing better on unseen environments. Extensive experiments show that environment-agnostic multitask learning significantly reduces the performance gap between seen and unseen environments, and the navigation agent trained so outperforms baselines on unseen environments by 16\% (relative measure on success rate) on VLN and 120\% (goal progress) on NDH. Our submission to the CVDN leaderboard establishes a new state-of-the-art for the NDH task on the holdout test set. Code is available at https://github.com/google-research/valan ."
TPFN: Applying Outer Product along Time to Multimodal Sentiment Analysis Fusion on Incomplete Data
Authors: Binghua Li; Chao Li; Feng Duan; Ning Zheng; Qibin Zhao
Venue: ECCV 2020 (main)

Abstract: Multimodal sentiment analysis (MSA) has been widely investigated in both computer vision and natural language processing. However, studies on the imperfect data especially with missing values are still far from success and challenging, even though such an issue is ubiquitous in the real world. Although previous works show the promising performance by exploiting the low-rank structures of the fused features, only the first-order statistics of the temporal dynamics are concerned. To this end, we propose a novel network architecture termed Time Product Fusion Network (TPFN), which takes the high-order statistics over both modalities and temporal dynamics into account. We construct the fused features by the outer product along adjacent time-steps, such that richer modal and temporal interactions are utilized. In addition, we claim that the low-rank structures can be obtained by regularizing the Frobenius norm of latent factors instead of the fused features. Experiments on CMU-MOSI and CMU-MOSEI datasets show that TPFN can compete with state-of-the art approaches in multimodal sentiment analysis in cases of both random and structured missing values."
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
Authors: Eu Wern Teh; Terrance DeVries; Graham W. Taylor
Venue: ECCV 2020 (main)

Abstract: We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several enhancements. We find that low temperature scaling is a performance-critical component and explain why it works. Besides, we also discover that Global Max Pooling works better in general when compared to Global Average Pooling. Additionally, our proposed fast moving proxies also addresses small gradient issue of proxies, and this component synergizes well with low temperature scaling and Global Max Pooling. Our enhanced model, called ProxyNCA++, achieves a 22.9 percentage point average improvement of Recall@1 across four different zero-shot retrieval datasets compared to the original ProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the CUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2, 90.1, 81.4, and 90.9, respectively. "
Learning with Privileged Information for Efficient Image Super-Resolution
Authors: Wonkyung Lee; Junghyup Lee; Dohyung Kim; Bumsub Ham
Venue: ECCV 2020 (main)

Abstract: Convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Most SR methods based on CNNs have focused on achieving performance gains in terms of quality metrics, such as PSNR and SSIM, over classical approaches. They typically require a large amount of memory and computational units. FSRCNN, consisting of few numbers of convolutional layers, has shown promising results, while using an extremely small number of network parameters. We introduce in this paper a novel distillation framework, consisting of teacher and student networks, that allows to boost the performance of FSRCNN drastically. To this end, we propose to use ground-truth high-resolution (HR) images as privileged information. The encoder in the teacher learns the degradation process, subsampling of HR images, using an imitation loss. The student and the decoder in the teacher, having the same network architecture as FSRCNN, try to reconstruct HR images. Intermediate features in the decoder, affordable for the student to learn, are transferred to the student through feature distillation. Experimental results on standard benchmarks demonstrate the effectiveness and the generalization ability of our framework, which significantly boosts the performance of FSRCNN as well as other SR methods."
Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification
Authors: Jianing Li; Shiliang Zhang
Venue: ECCV 2020 (main)

Abstract: Unsupervised domain adaptive person Re-IDentification (ReID) is challenging because of the large domain gap between source and target domains, as well as the lackage of labeled data on the target domain. This paper tackles this challenge through jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification. The local one-hot classification assigns images in a training batch with different person IDs, then adopts a Self-Adaptive Classification (SAC) model to classify them. The global multi-class classification is achieved by predicting labels on the entire unlabeled training set with the Memory-based Temporal-guided Cluster (MTC). MTC predicts multi-class labels by considering both visual similarity and temporal consistency to ensure the quality of label prediction. The two classification models are combined in a unified framework, which effectively leverages the unlabeled data for discriminative feature learning. Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks. For example, under unsupervised setting, our method outperforms recent unsupervised domain adaptive methods, which leverage more labels for training."