Italy

In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn
discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.

ICPR 2020

Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

multicut

embedding learning

triplet loss

clustering

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

The term “attribute transfer” refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes.
In this paper, we present a novel method called ATI-GAN (Attribute Transfer Inpainting Generative Adversarial Network) that modifies only parts of a face to achieve semantic changes (e.g. removing a mustache).

Local Facial Attribute Transfer through Inpainting

A method for calibrating a Trinocular Linear Camera Array (TLCA) for traffic surveillance applications, such as towards smart cities, is presented. A TLCA-specific parametrization guarantees that the calibration finds a model where all the cameras are on a straight line. The method uses both a chequerboard close to the camera, as well as measured 3D points far from the camera: points measured in world coordinates, as well as their corresponding 2D points found manually in the images. Superior calibration accuracy can be obtained compared to standard methods using only a single data source, largely due to the use of chequerboards, while the line constraint in the parametrization allows for joint rectification. The improved triangulation accuracy, from 8-12 cm to around 6 cm when calibrating with 30-50 points in our experiment, allowing better road user analysis. The method is demonstrated by a proof-of-concept application where a point cloud is generated from multiple disparity maps, visualizing road user detections in 3D.


Calibration and Absolute Pose Estimation of Trinocular Linear Camera Array for Smart City Applications

Anchors vs Attention: comparing XAI on a real-life use case

Explainable 3D-CNN for Multiple Sclerosis patients stratification

In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglect the relationship between imaging configuration and detection performance, and do not take into account the importance of detection performance feedback for improving image quality. Therefore, detection performance is limited by the passive nature of the conventional object detection framework. In order to solve the above limitations, this paper takes adaptive brightness adjustment and scale adjustment as examples, and proposes an active object detection method based on deep reinforcement learning. The goal of adaptive image attribute learning is to maximize the detection performance. With the help of active object detection and image attribute adjustment strategies, low-quality images can be converted into high-quality images, and the overall performance is improved without retraining the detector.

Adaptive Remote Sensing Image Attribute Learning for Active Object Detection

A general approach to compute the relevance of middle-level input features


A novel sentence embedding method built upon semantic subspace analysis, called semantic subspace sentence embedding (S3E), is proposed in this work. Given the fact that word embeddings can capture semantic relationship while semantically similar words tend to form semantic groups in a high-dimensional embedding space, we develop a sentence representation scheme by analyzing semantic subspaces of its constituent words. Specifically, we construct a sentence model from two aspects. First, we represent words that lie in the same semantic group using the intra-group descriptor. Second, we characterize the interaction between multiple semantic groups with the inter-group descriptor. The proposed S3E method is evaluated on both textual similarity tasks and supervised tasks. Experimental results show that it offers comparable or better performance than the state-of-the-art. The complexity of our S3E method is also much lower than other parameterized models.


Efficient Sentence Embedding via Semantic Subspace Analysis

Multi-dimensional classification (MDC) deals with the problem where each instance is associated with multiple class variables, each of which corresponds to a specific class space. One of the mainstream solutions for MDC is to adapt traditional machine learning techniques to deal with MDC data. In this paper, a first attempt towards adapting instance-based techniques for MDC is investigated, and a new approach named MD-KNN is proposed. Specifically, MD-KNN identifies unseen instance's k nearest neighbors and obtains its corresponding kNN counting statistics for each class space, based on which maximum a posteriori (MAP) inference is made for each pair of class spaces. After that, the class label w.r.t. each class space is determined by synergizing predictions from the learned classifiers via consulting empirical kNN accuracy. Comparative studies over ten benchmark data sets clearly validate MD-KNN's effectiveness.

MD-KNN: An Instance-based Approach for Multi-Dimensional Classification

Expert level evaluations for explainable AI(XAI) methods in the medical domain

3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point- cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.

Premium content

Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Downloads

Next from ICPR 2020

Local Facial Attribute Transfer through Inpainting

Similar lecture

Multi-Modal Deep Clustering: Unsupervised Partitioning of Images

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES