pwc-archive/papers-with-abstracts huggingface dataset:
An Inference Algorithm for Multi-Label MRF-MAP Problems with Clique Size 100
Authors: Ishant Shanu, Siddhant Bharti, Chetan Arora, S. N. Maheshwari
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Semantic Segmentation, Stereo Matching

In this paper, we propose an algorithm for optimal solutions to submodular higher-order multi-label MRF-MAP energy functions which can handle practical computer vision problems with up to 16 labels and cliques of size 100. The algorithm uses a transformation which transforms a multi-label problem to a 2-label problem on a much larger clique. Earlier algorithms based on this transformation could not handle problems larger than 16 labels on cliques of size 4. The proposed algorithm optimizes the resultant 2-label problem using the submodular polyhedron based Min Norm Point algorithm. The task is challenging because the state space of the transformed problem has a very large number of invalid states. For polyhedral based algorithms the presence of invalid states poses a challenge as apart from numerical instability, the transformation also increases the dimension of the polyhedral space making the straightforward use of known algorithms impractical. The approach reported in this paper allows us to bypass the large costs associated with invalid configurations, resulting in a stable, practical, optimal and efficient inference algorithm that, in our experiments, gives high-quality outputs on problems like pixel-wise object segmentation and stereo matching.
Dual Refinement Underwater Object Detection Network
Authors: Baojie Fan, Wei Chen, Yang Cong, Jiandong Tian
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Object, object-detection, Object Detection

Due to the complex underwater environment, underwater imaging often encounters some problems such as blur, scale variation, color shift, and texture distortion. Generic detection algorithms can not work well when we use them directly in the underwater scene. To address these problems, we propose an underwater detection framework with feature enhancement and anchor refinement. It has a composite connection backbone to boost the feature representation and introduces a receptive field augmentation module to exploit multi-scale contextual features. The developed underwater object detection framework also provides a prediction refinement scheme according to six prediction layers, it can refine multi-scale features to better align with anchors by learning from offsets, which solve the problem of sample imbalance to a certain extent. We also construct a new underwater detection dataset, denoted as UWD, which has more than 10,000 train-val and test underwater images. The extensive experiments on PASCAL VOC and UWD demonstrate the favorable performance of the proposed underwater detection framework against the states-of-the-arts methods in terms of accuracy and robustness. Source code and models are available at: https://github.com/Peterchen111/FERNet.
Task-Aware Quantization Network for JPEG Image Compression
Authors: Jinyoung Choi, Bohyung Han
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Decoder, Image Compression, Quantization

We propose to learn a deep neural network for JPEG image compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective function of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approximates bitrates using simple network blocks---two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses---two for semantic image understanding and another two for conventional image compression---and demonstrate the effectiveness of our approach to the individual tasks.
CLNet: A Compact Latent Network for Fast Adjusting Siamese Trackers
Authors: Xingping Dong, Jianbing Shen, Ling Shao, Fatih Porikli
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8

In this paper, we provide a deep analysis for Siamese-based trackers and find that the one core reason for their failure on challenging cases can be attributed to the problem of {\it decisive samples missing} during offline training. Furthermore, we notice that the samples given in the first frame can be viewed as the decisive samples for the sequence since they contain rich sequence-specific information. To make full use of these sequence-specific samples, {we propose a compact latent network to quickly adjust the tracking model to adapt to new scenes.} A statistic-based compact latent feature is proposed to efficiently capture the sequence-specific information for the fast adjustment. In addition, we design a new training approach based on a diverse sample mining strategy to further improve the discrimination ability of our compact latent network. To evaluate the effectiveness of our method, we apply it to adjust a recent state-of-the-art tracker, SiamRPN++. Extensive experimental results on five recent benchmarks demonstrate that the adjusted tracker achieves promising improvement in terms of tracking accuracy, with almost the same speed. The code and models are available at \url{https://github.com/xingpingdong/CLNet-tracking}.
Occlusion-Aware Siamese Network for Human Pose Estimation
Authors: Lu Zhou, Yingying Chen, Yunze Gao, Jinqiao Wang, Hanqing Lu
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Pose Estimation

Pose estimation usually suffers from varying degrees of performance degeneration owing to occlusion. To conquer this dilemma, we propose an occlusion-aware siamese network to improve the performance. Specifically, we introduce scheme of feature erasing and reconstruction. Firstly, we utilize attention mechanism to predict the occlusion-aware attention map which is explicitly supervised and clean the feature map which is contaminated by different types of occlusions. Nevertheless, the cleaning procedure not only removes the useless information but also erases some valuable details. To overcome the defects caused by the erasing operation, we perform feature reconstruction to recover the information destroyed by occlusion and details lost in cleaning procedure. To make reconstructed features more precise and informative, we adopt siamese network equipped with OT divergence to guide the features of occluded images towards those of the un-occluded images. Algorithm is validated on MPII, LSP and COCO benchmarks and we achieve promising results.
Rotation-robust Intersection over Union for 3D Object Detection
Authors: Yu Zheng, Danyang Zhang, Sinan Xie, Jiwen Lu, Jie zhou
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: 2D Object Detection, 3D Object Detection, Object, object-detection, Object Detection

In this paper, we propose a Rotation-robust Intersection over Union ($ extit{RIoU}$) for 3D object detection, which aims to jointly learn the overlap of rotated bounding boxes. In most existing 3D object detection methods, the norm-based loss is adopted to individually regress the parameters of bounding boxes, which may suffer from the loss-metric mismatch due to the scaling problem. Motivated by the IoU loss in the axis-aligned 2D object detection which is invariant to the scale, our method jointly optimizes the parameters via the $ extit{RIoU}$ loss. To tackle the uncertainty of convex caused by rotation, a projection operation is defined to estimate the intersection area. The calculation process of $ extit{RIoU}$ and its loss function is robust to the rotation condition and feasible for back-propagation, which only comprises basic numerical operations. By incorporating the $ extit{RIoU}$ loss with the conventional norm-based loss function, we enforce the network to directly optimize the $ extit{RIoU}$ . Experimental results on the KITTI and nuScenes datasets validate the effectiveness of our proposed method. Moreover, we show that our method is suitable for the detection task of 2D rotated objects, such as text boxes and cluttered targets in the aerial images.
New Threats against Object Detector with Non-local Block
Authors: Yi Huang, Fan Wang, Adams Wai-Kin Kong, Kwok-Yan Lam
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Object

The introduction of non-local blocks to the traditional CNN architecture enhances its performance for various computer vision tasks by improving its capabilities of capturing long range dependencies. However, the usage of non-local blocks may also introduce new threats to computer vision systems. Therefore, it is important to study the threats caused by non-local blocks before directly applying them to commercial systems. In this paper, two new threats named disappearing attack and appearing attack against object detectors with a non-local block are investigated. The former aims at misleading an object detector with a non-local block such that it is unable to detect a target object category while the latter aims at misleading the object detector such that it detects a predefined object category, which is not present in images. Different from the existing attacks against object detectors, these threats are able to be performed in long range cases. This means that the target object and the universal adversarial patches learned from the proposed algorithms can have long distance between them. To examine the threats, digital and physical experiments are conducted on Faster R-CNN with a non-local block and 6331 images from 56 videos. The experiments show that the universal patches are able to mislead the detector with greater probabilities. To explain the threats from non-local blocks, the reception fields of CNN models with and without non-local blocks are studied empirically and theoretically.
Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation
Authors: Xinpeng Xie, Jia-Wei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation, Generative Adversarial Network, Image-to-Image Translation, Segmentation, Semantic Segmentation, Translation

Recent generative adversarial network (GAN) based methods (e.g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation. Some frameworks have been proposed to adopt a segmentation network as the auxiliary regularization to prevent the content distortion. However, all of them require extra pixel-wise annotations, which is difficult to fulfill in practical applications. In this paper, we propose a novel GAN (namely OP-GAN) to address the problem, which involves a self-supervised module to enforce the image content consistency during image-to-image translations without any extra annotations. We evaluate the proposed OP-GAN on three publicly available datasets. The experimental results demonstrate that our OP-GAN can yield visually plausible translated images and significantly improve the semantic segmentation accuracy in different domain adaptation scenarios with off-the-shelf deep learning networks such as PSPNet and U-Net.
On the Usage of the Trifocal Tensor in Motion Segmentation
Authors: Federica Arrigoni, Luca Magri, Tomas Pajdla
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Clustering, Motion Segmentation, Segmentation

Motion segmentation, i.e., the problem of clustering data in multiple images based on different 3D motions, is an important task for reconstructing and understanding dynamic scenes. In this paper we address motion segmentation in multiple images by combining partial results coming from triplets of images, which are obtained by fitting a number of trifocal tensors to correspondences. We exploit the fact that the trifocal tensor is a stronger model than the fundamental matrix, as it provides fewer but more reliable matches over three images than fundamental matrices provide over the two. We also consider an alternative solution which merges partial results coming from both triplets and pairs of images, showing the strength of three-frame segmentation in a combination with two-frame segmentation. Our real experiments on standard as well as new datasets demonstrate the superior accuracy of the proposed approaches when compared to previous techniques.
Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language
Authors: Shaoxiang Chen, Yu-Gang Jiang
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Sentence

Temporal Activity Localization via Language (TALL) in video is a recently proposed challenging vision task, and tackling it requires fine-grained understanding of the video content, however, this is overlooked by most of the existing works. In this paper, we propose a novel TALL method which builds a Hierarchical Visual-Textual Graph to model interactions between the objects and words as well as among the objects to jointly understand the video contents and the language. We also design a convolutional network with cross-channel communication mechanism to further encourage the information passing between the visual and textual modalities. Finally, we propose a loss function that enforces alignment of the visual representation of the localized activity and the sentence representation, so that the model can predict more accurate temporal boundaries. We evaluated our proposed method on two popular benchmark datasets: Charades-STA and ActivityNet Captions, and achieved state-of-the-art performances on both datasets. Code is available at \url{https://github.com/forwchen/HVTG}.
AssembleNet++: Assembling Modality Representations via Attention Connections - Supplementary Material -
Authors: Michael S. Ryoo, AJ Piergiovanni, Juhana Kangaspunta, Anelia Angelova
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Activity Recognition

We create a family of powerful video models which are able to: (i) learn interactions between semantic object information and raw appearance and motion features, and (ii) deploy attention in order to better learn the importance of features at each convolutional block of the network. A new network component named peer-attention is introduced, which dynamically learns the attention weights using another block or input modality. Even without pre-training, our models outperform the previous work on standard public activity recognition datasets with continuous videos, establishing new state-of-the-art. We also confirm that our findings of having neural connections from the object modality and the use of peer-attention is generally applicable for different existing architectures, improving their performances. We name our model explicitly as AssembleNet++. The code will be available at: https://sites.google.com/corp/view/assemblenet/
DDGCN: A Dynamic Directed Graph Convolutional Network for Action Recognition
Authors: Matthew Korban, Xin Li
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Action Recognition

We propose a Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features of human actions from their skeletal representations. The DDGCN consists of three new feature modeling modules: (1) Dynamic Convolutional Sampling (DCS), (2) Dynamic Convolutional Weight (DCW) assignment, and (3) Directed Graph Spatial-Temporal (DGST) feature extraction. Comprehensive experiments show that the DDGCN outperforms existing state-of-the-art action recognition approaches in various testing datasets. Our source code and model will be released at http://www.ece.lsu.edu/xinli/ActionModeling/index.html .
Dynamic Low-light Imaging with Quanta Image Sensors
Authors: Yiheng Chi, Abhiram Gnanasambandam, Vladlen Koltun, Stanley H. Chan
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Denoising, Image Reconstruction

Imaging in low light is difficult because the number of photons arriving at the sensor is low. Imaging dynamic scenes in low-light environments is even more difficult because as the scene moves, pixels in adjacent frames need to be aligned before they can be denoised. Conventional CMOS image sensors (CIS) are at a particular disadvantage in dynamic low-light settings because the exposure cannot be too short lest the read noise overwhelms the signal. We propose a solution using Quanta Image Sensors (QIS) and present a new image reconstruction algorithm. QIS are single-photon image sensors with photon counting capabilities. Studies over the past decade have confirmed the effectiveness of QIS for low-light imaging but reconstruction algorithms for dynamic scenes in low light remain an open problem. We fill the gap by proposing a student-teacher training protocol that transfers knowledge from a motion teacher and a denoising teacher to a student network. We show that dynamic scenes can be reconstructed from a burst of frames at a photon level of 1 photon per pixel per frame. Experimental results confirm the advantages of the proposed method compared to existing methods.
Disambiguating Monocular Depth Estimation with a Single Transient
Authors: Mark Nishimura, David B. Lindell, Christopher Metzler, Gordon Wetzstein
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Depth Estimation, Monocular Depth Estimation, Sensor Fusion

Monocular depth estimation algorithms successfully predict the relative depth order of objects in a scene. However, because of the fundamental scale ambiguity associated with monocular images, these algorithms fail at correctly predicting true metric depth. In this work, we demonstrate how a depth histogram of the scene, which can be readily captured using a single-pixel time-resolved detector, can be fused with the output of existing monocular depth estimation algorithms to resolve the depth ambiguity problem. We validate this novel sensor fusion technique experimentally and in extensive simulation. We show that it significantly improves the performance of several state-of-the-art monocular depth estimation algorithms.
EGDCL: An Adaptive Curriculum Learning Framework for Unbiased Glaucoma Diagnosis
Authors: Rongchang Zhao, Xuanlin Chen, Zailiang Chen, Shuo Li
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Specificity

Today's computer-aided diagnosis (CAD) model is still far from the clinical practice of glaucoma detection, mainly due to the training bias originating from 1) the normal-abnormal class imbalance and 2) the rare but significant hard samples in fundus images. However, debiasing in CAD is not trivial because existing methods cannot cure the two types of bias to categorize fundus images. In this paper, we propose a novel curriculum learning paradigm (EGDCL) to train an unbiased glaucoma diagnosis model with the adaptive dual-curriculum. Innovatively, the dual-curriculum is designed with the guidance of evidence maps to build a training criterion, which gradually cures the bias in training data. In particular, the dual-curriculum emphasizes unbiased training contributions of data from easy to hard, normal to abnormal, and the dual-curriculum is optimized jointly with model parameters to obtain the optimal solution. In comparison to baselines, EGDCL significantly improves the convergence speed of the training process and obtains the top performance in the test procedure. Experimental results on challenging glaucoma datasets show that our EGDCL delivers unbiased diagnosis (0.9721 of Sensitivity, 0.9707 of Specificity, 0.993 of AUC, 0.966 of F2-score) and outperform the other methods. It endows our EGDCL a great advantage to handle the unbiased CAD in clinical application.
On Dropping Clusters to Regularize Graph Convolutional Neural Networks
Authors: Xikun Zhang, Chang Xu, DaCheng Tao
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Action Recognition, Skeleton Based Action Recognition

Dropout has been widely adopted to regularize graph convolutional networks (GCNs) by randomly zeroing entries of the node feature vectors and obtains promising performance on various tasks. However, the information of individually zeroed entries could still present in other correlated entries by propagating (1) spatially between entries of different node feature vectors and (2) depth-wisely between different entries of each node feature vector, which essentially weakens the effectiveness of dropout. This is mainly because in a GCN, neighboring node feature vectors after linear transformations are aggregated to produce new node feature vectors in the subsequent layer. To effectively regularize GCNs, we devise DropCluster which first randomly zeros some seed entries and then zeros entries that are spatially or depth-wisely correlated to those seed entries. In this way, the information of the seed entries is thoroughly removed and cannot flow to subsequent layers via the correlated entries. We validate the effectiveness of the proposed DropCluster by comprehensively comparing it with dropout and its representative variants, such as SpatialDropout, Gaussian dropout and DropEdge, on skeleton-based action recognition.
SPAN: Spatial Pyramid Attention Network for Image Manipulation Localization
Authors: Xuefeng Hu, Zhihan Zhang, Zhenye Jiang, Syomantak Chaudhuri, Zhenheng Yang, Ram Nevatia
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Manipulation, Image Manipulation Detection, Image Manipulation Localization, Misinformation, Position

Tehchniques for manipulating images are advancing rapidly; while these are helpful for many useful tasks, they also pose a threat to society with their ability to create believable misinformation. We present a novel, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations. The proposed architecture efficiently and effecively models the relationship between image patches at multiple scales by constructing a pyramid of local self-attention blocks. The design includes a novel position projection to encode the spatial positions of the patches. SPAN is trained on a synthetic dataset but can also be fine tuned for specific datasets; The proposed method shows significant gains in performance on standard datasets over previous state-of-art methods.
YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models -
Authors: Yukihiro Sasagawa, Hajime Nagahara
Date: 2020-08-01 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation, Knowledge Distillation, object-detection, Object Detection

Generating new visual tasks requires additional datasets, and it costs a considerable effort. We propose a new method of domain adaptation for merging multiple models with less effort than creating an additional dataset. This method merges pre-trained models in different domains using the glue layers and the generative model, which feeds latent features to train the glue layers without an additional dataset. We also propose the generative model created by knowledge distillation from pre-trained models. It also allows reusing the dataset to create latent features for training the glue layers. We apply this method to object detection in a low-light situation. The “YOLO in the Dark” contains two models, “Learning to See in the Dark” and YOLO. We report the new method and the result of domain adaptation that detect objects from raw short-exposure low-light images. The “YOLO in the Dark” costs fewer computing resources compared to the naive approach.