Italy

Monocular depth estimation plays a key role in 3D scene understanding, and a number of recent papers have achieved significant improvements using deep learning based algorithms. Most papers among them proposed methods that use a pre-trained network as a deep feature extractor and then decode the obtained features to create a depth map. In this study, we focus on how to use this encoder-decoder structure to deliver meaningful representation throughout the entire network. We propose a new network architecture with our suggested modules to create a more accurate depth map by bridging the context between the encoding and decoding phase. First, we place the pyramid block at the bottleneck of the network to enlarge the view and convey rich information about the global context to the decoder. Second, we suggest a skip connection with the fuse module to aggregate the encoder and decoder feature. Finally, we validate our approach on the NYU Depth V2 and KITTI datasets. The experimental results prove the efficacy of the suggested model and show performance gains over the state-of-the-art model.

ICPR 2020

Delivering Meaningful Representation for Monocular Depth Estimation

scene understanding

applications of computer vision

deep learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


poster


Automatic liver tumor segmentation from 3D Computed Tomography (CT) is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on Fully Convolutional Network (FCN) in liver tumor segmentation draw on success of learning discriminative multi-level features. In this paper, we propose a Deep Attentive Refinement Network (DARN) for improved liver tumor segmentation from CT volumes by fully exploiting both low and high level features embedded in different layers of FCN. Different from existing works, we exploit attention mechanism to leverage the relation of different levels of features encoded in different layers of FCN. Specifically, we introduce a Semantic Attention Refinement (SemRef) module to selectively emphasize global semantic information in low level features with the guidance of high level ones, and a Spatial Attention Refinement (SpaRef) module to adaptively enhance spatial details in high level features with the guidance of low level ones. We evaluate our network on the public MICCAI 2017 Liver Tumor Segmentation Challenge dataset (LiTS dataset) and it achieves state-of-the-art performance. The proposed refinement modules are an effective strategy to exploit multi-level features and has great potential to generalize to other medical image segmentation tasks.

DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT volume

Anthropometric body measurements are importantfor industrial design, garment fitting, medical diagnosis andergonomics. A number of methods have been proposed toestimate the body measurements from images, but progress hasbeen slow due to the lack of realistic and publicly availabledatasets. The existing works train and test on silhouettes of 3D body meshes obtained by fitting a human body model to the commercial CAESAR scans. In this work, we introduce theBODY-fit dataset that contains fitted meshes of 2,675 female and1,474 male 3D body scans. We unify evaluation on the CAESAR-fit and BODY-fit datasets by computing body measurements fromgeodesic surface paths as the ground truth and by generating two-view silhouette images. We also introduce BODY-rgb - a realisticdataset of 86 male and 108 female subjects captured with an RGBcamera and manually tape measured ground truth. We propose a simple yet effective deep CNN architecture as a baseline method which obtains competitive accuracy on the three datasets.

Silhouette Body Measurement Benchmarks

Reinforcement learning is an area of machine learning which recently has been applied in a variety of domains. A lot of challenging problems such as playing Atari video games, controlling robotics and many other fields have been solved in past few years. But when it comes to multi-agent environment, there remains much more complex challenges due to the some multiagent pathologies such as the environment non-stationary problem, curse of dimensionality, credit assignment problem and relative overgeneralization. Inspired by the application of attention mechanism in machine translation and other related domains, we propose an attention based approach called attention value decomposition network (AVD-Net), which capitalizes on the coordination relations between agents.

AVD-Net: Attention Value Decomposition Network For Deep Multi-Agent Reinforcement Learning


Degraded document image binarization is one of the most challenging tasks in the domain of document image analysis. In this paper, we present a novel approach towards document image binarization by introducing three-player min-max adversarial game. We train the network in an unsupervised setup by assuming that we do not have any paired-training data. In our approach, an Adversarial Texture Augmentation Network (ATANet) first superimposes the texture of a degraded reference image over a clean image. Later, the clean image along with its generated degraded version constitute the pseudo paired-data which is used to train the Unsupervised Document Binarization Network (UDBNet). Following this approach, we have enlarged the document binarization datasets as it generates multiple images having same content feature but different textual feature. These generated noisy images are then fed into the UDBNet to get back the clean version. The joint discriminator which is the third-player of our three-player min-max adversarial game tries to couple both the ATANet and UDBNet. The three-player min-max adversarial game stops, when the distributions modelled by the ATANet and the UDBNet align to the same joint distribution over time. Thus, the joint discriminator enforces the UDBNet to perform better on real degraded image. The experimental results indicate the superior performance of the proposed model over existing state-of-the-art algorithm on widely used DIBCO datasets.

UDBNET: Unsupervised Document Binarization Network via Adversarial Game

In this work, we propose a long-term RGBD trackerthat achieves state-of-the-art performance on the PrincetonRGBD, STC and CDTB datasets and runs nearly real-time(20fps). The tracker is based on non-stationary formulation of the deep Discriminative Correlation Filter (DCF). The DCFfeature channels are modulated by the depth context in eachlocation which makes the tracker robust against occlusion andbackground clutter. Moreover, the non-stationary DCF also performs well in target re-detection enabling long-term tracking.

DAL: A Deep Depth-Aware Long-term Tracker


We present FourierNet, a single shot, anchor-free, fully convolutional instance segmentation method that predicts a shape vector. Consequently, this shape vector is converted into the masks’ contour points using a fast numerical transform. Compared to previous methods, we introduce a new training technique, where we utilize a differentiable shape decoder, which manages the automatic weight balancing of the shape vector’s coefficients. We used the Fourier series as a shape encoder because of its coefficient interpretability and fast implementation.
FourierNet shows promising results compared to polygon representation methods, achieving 30.6 mAP on the MS COCO 2017 benchmark. At lower image resolutions, it runs at 26.6 FPS with 24.3 mAP. It reaches 23.3 mAP using just eight parameters to represent the mask (note that at least four parameters are needed
for bounding box prediction only). Qualitative analysis shows that suppressing a reasonable proportion of higher frequencies of Fourier series, still generates meaningful masks. These results validate our understanding that lower frequency components hold higher information for the segmentation task, and therefore, we can achieve a compressed representation. Code is available at github.com/cogsys-tuebingen/FourierNet.

FourierNet: Compact Mask Representation for Instance Segmentation Using Differentiable Shape Decoders

Getting deep convolutional neural networks to per- form well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of multi-view graph embedding and dimensionality reduction. Instead of solving the generalised eigenvalue problem to perform the embedding, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework which generalises the prior methods CCSA and d- SNE, and enables simple and effective loss designs; an LDA- inspired instantiation of the framework leads to performance on par with the state-of-the-art on the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.

Supervised Domain Adaptation using Graph Embedding

Most state of the art object detectors output multiple detections per object. The duplicates are removed in a post-processing step called Non-Maximum Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that contain objects with high overlap: This heuristic assumes that a high overlap between two bounding boxes corresponds to a high probability of one being a duplicate.
We propose FeatureNMS to solve this problem. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance. Our approach outperforms classical NMS and derived approaches and achieves state of the art performance.

FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings

Unsupervised image-to-image translation is an inherently ill-posed problem. Recent methods based on deep encoder-decoder architectures have shown impressive results, but we show that they only succeed due to a strong locality bias, and they fail to learn very simple nonlocal transformations (e.g. mapping upside down faces to upright faces). When the locality bias is removed, the methods are too powerful and may fail to learn simple local transformations. In this paper we introduce linear encoder-decoder architectures for unsupervised image to image translation. We show that learning is much easier and faster with these architectures and yet the results are surprisingly effective. In particular, we show a number of local problems for which the results of the linear methods are comparable to those of state-of-the-art architectures but with a fraction of the training time, and a number of nonlocal problems for which the state-of-the-art fails while linear methods succeed.

The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation

Extracting features containing both high-resolution and high-level-semantic information is important for human pose estimation, while most existing methods of extracting high-resolution high-level-semantic features suffer from spatial information loss or semantic information mismatch. To efficiently address these issues, we propose a novel Dilation Pyramid Module (DPM), which can enlarge the receptive field multiplicatively to achieve high-level-semantic information as subsampling without reducing spatial resolution. DPM is composed of several consecutive dilated convolution layers of which dilation radius is specially designed to enlarge the receptive field multiplicatively and avoid the gridding issue of dilated convolution. Based on DPM, the Dilation Pyramid Net (DPN) is proposed to efficiently extract high-resolution high-level-semantic features. We experimentally demonstrate the effectiveness and efficiency of the proposed DPN
with comparable performance to the state-of-the-art methods over two challenging benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.

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Delivering Meaningful Representation for Monocular Depth Estimation

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