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Ensembling is a popular and effective method for improving machine learning (ML) models. It proves its value not only in classical ML but also for deep learning. Ensembles enhance the quality and trustworthiness of ML solutions, and allow…

Machine Learning · Computer Science 2022-06-28 Polina Proscura , Alexey Zaytsev

In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss because this has been shown…

Computer Vision and Pattern Recognition · Computer Science 2016-08-02 Bohan Zhuang , Guosheng Lin , Chunhua Shen , Ian Reid

Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised…

Computer Vision and Pattern Recognition · Computer Science 2023-12-19 Deen Dayal Mohan , Bhavin Jawade , Srirangaraj Setlur , Venu Govindaraj

While visual imitation learning offers one of the most effective ways of learning from visual demonstrations, generalizing from them requires either hundreds of diverse demonstrations, task specific priors, or large, hard-to-train…

Robotics · Computer Science 2021-12-07 Jyothish Pari , Nur Muhammad Shafiullah , Sridhar Pandian Arunachalam , Lerrel Pinto

Representation learning approaches typically rely on images of objects captured from a single perspective that are transformed using affine transformations. Additionally, self-supervised learning, a successful paradigm of representation…

Computer Vision and Pattern Recognition · Computer Science 2022-08-17 Omiros Pantazis , Mathew Salvaris

We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks. With the proposed method, the label embedding is adaptively and automatically learned…

Machine Learning · Computer Science 2017-10-31 Xu Sun , Bingzhen Wei , Xuancheng Ren , Shuming Ma

Deep metric learning maps visually similar images onto nearby locations and visually dissimilar images apart from each other in an embedding manifold. The learning process is mainly based on the supplied image negative and positive training…

Computer Vision and Pattern Recognition · Computer Science 2020-09-14 Chang-Hui Liang , Wan-Lei Zhao , Run-Qing Chen

Unified and scalable Transformers have recently achieved remarkable success in modeling diverse phenomena traditionally associated with computer graphics, such as 3D visual effects, rendering processes, and motion in videos. In this work,…

Graphics · Computer Science 2026-05-28 Yu Zhang , Yidi Shao , Wenqi Ouyang , Yushi Lan , Zhexin Liang , Chengrui Wu , Xudong Xu , Xingang Pan

The uprising trend of deep learning in computer vision and artificial intelligence can simply not be ignored. On the most diverse tasks, from recognition and detection to segmentation, deep learning is able to obtain state-of-the-art…

Computer Vision and Pattern Recognition · Computer Science 2020-07-10 Steven Puttemans , Timothy Callemein , Toon Goedemé

Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As challenging as this…

Computer Vision and Pattern Recognition · Computer Science 2021-05-14 Avi Ben-Cohen , Nadav Zamir , Emanuel Ben Baruch , Itamar Friedman , Lihi Zelnik-Manor

Deep neural networks (DNNs) have made a revolution in numerous fields during the last decade. However, in tasks with high safety requirements, such as medical or autonomous driving applications, providing an assessment of the models…

Machine Learning · Computer Science 2020-11-20 Omer Achrack , Raizy Kellerman , Ouriel Barzilay

While the majority of today's object class models provide only 2D bounding boxes, far richer output hypotheses are desirable including viewpoint, fine-grained category, and 3D geometry estimate. However, models trained to provide richer…

Computer Vision and Pattern Recognition · Computer Science 2014-02-18 Bojan Pepik , Michael Stark , Peter Gehler , Bernt Schiele

Precise segmentation of objects with highly similar shapes remains a challenging problem in dense prediction, especially in scenarios with ambiguous boundaries, overlapping instances, and weak inter-instance visual differences. While…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Rui Xiao

In this paper, we present a method of clothes retargeting; generating the potential poses and deformations of a given 3D clothing template model to fit onto a person in a single RGB image. The problem is fundamentally ill-posed as attaining…

Computer Vision and Pattern Recognition · Computer Science 2021-02-02 Jae Shin Yoon , Kihwan Kim , Jan Kautz , Hyun Soo Park

We propose a transfer learning-based solution for the problem of multiple class novelty detection. In particular, we propose an end-to-end deep-learning based approach in which we investigate how the knowledge contained in an external,…

Computer Vision and Pattern Recognition · Computer Science 2019-03-07 Pramuditha Perera , Vishal M. Patel

With the recent advances in the object detection research field, tracking-by-detection has become the leading paradigm adopted by multi-object tracking algorithms. By extracting different features from detected objects, those algorithms can…

Computer Vision and Pattern Recognition · Computer Science 2020-07-14 Michel Meneses , Leonardo Matos , Bruno Prado , André de Carvalho , Hendrik Macedo

While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural net- works on the foreground (object) and…

Computer Vision and Pattern Recognition · Computer Science 2017-05-29 Zhuotun Zhu , Lingxi Xie , Alan L. Yuille

In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity. We present a deep siamese architecture that when trained on positive and negative pairs…

Computer Vision and Pattern Recognition · Computer Science 2019-01-14 Rishab Sharma , Anirudha Vishvakarma

This paper presents a unified approach to understanding dynamic scenes from casual videos. Large pretrained vision foundation models, such as vision-language, video depth prediction, motion tracking, and segmentation models, offer promising…

Computer Vision and Pattern Recognition · Computer Science 2025-03-28 David Yifan Yao , Albert J. Zhai , Shenlong Wang

Outfits in online fashion data are composed of items of many different types (e.g. top, bottom, shoes) that share some stylistic relationship with one another. A representation for building outfits requires a method that can learn both…

Computer Vision and Pattern Recognition · Computer Science 2018-07-31 Mariya I. Vasileva , Bryan A. Plummer , Krishna Dusad , Shreya Rajpal , Ranjitha Kumar , David Forsyth