| |
Records |
Links |
|
Author |
Kai Wang; Xialei Liu; Andrew Bagdanov; Luis Herranz; Shangling Jui; Joost Van de Weijer |
|
| |
Title |
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3728-3738 |
|
| |
Keywords |
Training; Computer vision; Image recognition; Upper bound; Conferences; Pattern recognition; Task analysis |
|
| |
Abstract |
In this paper we consider the problem of incremental meta-learning in which classes are presented incrementally in discrete tasks. We propose Episodic Replay Distillation (ERD), that mixes classes from the current task with exemplars from previous tasks when sampling episodes for meta-learning. To allow the training to benefit from a large as possible variety of classes, which leads to more gener-
alizable feature representations, we propose the cross-task meta loss. Furthermore, we propose episodic replay distillation that also exploits exemplars for improved knowledge distillation. Experiments on four datasets demonstrate that ERD surpasses the state-of-the-art. In particular, on the more challenging one-shot, long task sequence scenarios, we reduce the gap between Incremental Meta-Learning and
the joint-training upper bound from 3.5% / 10.1% / 13.4% / 11.7% with the current state-of-the-art to 2.6% / 2.9% / 5.0% / 0.2% with our method on Tiered-ImageNet / Mini-ImageNet / CIFAR100 / CUB, respectively. |
|
| |
Address  |
New Orleans, USA; 20 June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
LAMP; 600.147 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ WLB2022 |
Serial |
3686 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta |
|
| |
Title |
Area Under the ROC Curve Maximization for Metric Learning |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition |
|
| |
Abstract |
Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification. |
|
| |
Address |
New Orleans, USA; 20 June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
CIC; LAMP; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GAB2022 |
Serial |
3700 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alex Gomez-Villa; Bartlomiej Twardowski; Lu Yu; Andrew Bagdanov; Joost Van de Weijer |
|
| |
Title |
Continually Learning Self-Supervised Representations With Projected Functional Regularization |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
CVPR 2022 Workshop on Continual Learning (CLVision, 3rd Edition) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3866-3876 |
|
| |
Keywords |
Computer vision; Conferences; Self-supervised learning; Image representation; Pattern recognition |
|
| |
Abstract |
Recent self-supervised learning methods are able to learn high-quality image representations and are closing the gap with supervised approaches. However, these methods are unable to acquire new knowledge incrementally – they are, in fact, mostly used only as a pre-training phase over IID data. In this work we investigate self-supervised methods in continual learning regimes without any replay
mechanism. We show that naive functional regularization,also known as feature distillation, leads to lower plasticity and limits continual learning performance. Instead, we propose Projected Functional Regularization in which a separate temporal projection network ensures that the newly learned feature space preserves information of the previous one, while at the same time allowing for the learning of new features. This prevents forgetting while maintaining the plasticity of the learner. Comparison with other incremental learning approaches applied to self-supervision demonstrates that our method obtains competitive performance in
different scenarios and on multiple datasets. |
|
| |
Address |
New Orleans, USA; 20 June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
LAMP: 600.147; 600.120 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GTY2022 |
Serial |
3704 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Dipam Goswami; Yuyang Liu ; Bartlomiej Twardowski; Joost Van de Weijer |
|
| |
Title |
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning |
Type |
Conference Article |
| |
Year |
2023 |
Publication |
37th Annual Conference on Neural Information Processing Systems |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Poster |
|
| |
Address |
New Orleans; USA; December 2023 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
NEURIPS |
|
| |
Notes |
LAMP |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GLT2023 |
Serial |
3934 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Kai Wang; Fei Yang; Shiqi Yang; Muhammad Atif Butt; Joost Van de Weijer |
|
| |
Title |
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing |
Type |
Conference Article |
| |
Year |
2023 |
Publication |
37th Annual Conference on Neural Information Processing Systems |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Poster |
|
| |
Address |
New Orleans; USA; December 2023 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
NEURIPS |
|
| |
Notes |
LAMP |
Approved |
no |
|
| |
Call Number |
Admin @ si @ WYY2023 |
Serial |
3935 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
ChuanMing Fang; Kai Wang; Joost Van de Weijer |
|
| |
Title |
IterInv: Iterative Inversion for Pixel-Level T2I Models |
Type |
Conference Article |
| |
Year |
2023 |
Publication |
37th Annual Conference on Neural Information Processing Systems |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Large-scale text-to-image diffusion models have been a ground-breaking development in generating convincing images following an input text prompt. The goal of image editing research is to give users control over the generated images by modifying the text prompt. Current image editing techniques are relying on DDIM inversion as a common practice based on the Latent Diffusion Models (LDM). However, the large pretrained T2I models working on the latent space as LDM suffer from losing details due to the first compression stage with an autoencoder mechanism. Instead, another mainstream T2I pipeline working on the pixel level, such as Imagen and DeepFloyd-IF, avoids this problem. They are commonly composed of several stages, normally with a text-to-image stage followed by several super-resolution stages. In this case, the DDIM inversion is unable to find the initial noise to generate the original image given that the super-resolution diffusion models are not compatible with the DDIM technique. According to our experimental findings, iteratively concatenating the noisy image as the condition is the root of this problem. Based on this observation, we develop an iterative inversion (IterInv) technique for this stream of T2I models and verify IterInv with the open-source DeepFloyd-IF model. By combining our method IterInv with a popular image editing method, we prove the application prospects of IterInv. The code will be released at \url{this https URL}. |
|
| |
Address |
New Orleans; USA; December 2023 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
NEURIPS |
|
| |
Notes |
LAMP |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FWW2023 |
Serial |
3936 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich |
|
| |
Title |
Multi-Modal Aerial View Object Classification Challenge Results – PBVS 2022 |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
350-358 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied Coincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021. |
|
| |
Address |
New Orleans; USA; June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
MSIAU |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LNS2022 |
Serial |
3768 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Aneesh Rangnekar; Zachary Mulhollan; Anthony Vodacek; Matthew Hoffman; Angel Sappa; Erik Blasch; Jun Yu; Liwen Zhang; Shenshen Du; Hao Chang; Keda Lu; Zhong Zhang; Fang Gao; Ye Yu; Feng Shuang; Lei Wang; Qiang Ling; Pranjay Shyam; Kuk-Jin Yoon; Kyung-Soo Kim |
|
| |
Title |
Semi-Supervised Hyperspectral Object Detection Challenge Results – PBVS 2022 |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
390-398 |
|
| |
Keywords |
Training; Computer visio; Conferences; Training data; Object detection; Semisupervised learning; Transformers |
|
| |
Abstract |
This paper summarizes the top contributions to the first semi-supervised hyperspectral object detection (SSHOD) challenge, which was organized as a part of the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop at the Computer Vision and Pattern Recognition (CVPR) conference. The SSHODC challenge is a first-of-its-kind hyperspectral dataset with temporally contiguous frames collected from a university rooftop observing a 4-way vehicle intersection over a period of three days. The dataset contains a total of 2890 frames, captured at an average resolution of 1600 × 192 pixels, with 51 hyperspectral bands from 400nm to 900nm. SSHOD challenge uses 989 images as the training set, 605 images as validation set and 1296 images as the evaluation (test) set. Each set was acquired on a different day to maximize the variance in weather conditions. Labels are provided for 10% of the annotated data, hence formulating a semi-supervised learning task for the participants which is evaluated in terms of average precision over the entire set of classes, as well as individual moving object classes: namely vehicle, bus and bike. The challenge received participation registration from 38 individuals, with 8 participating in the validation phase and 3 participating in the test phase. This paper describes the dataset acquisition, with challenge formulation, proposed methods and qualitative and quantitative results. |
|
| |
Address |
New Orleans; USA; June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
MSIAU; no menciona |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RMV2022 |
Serial |
3774 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla; Jin Kim; Dogun Kim; Zhihao Li; Yingchun Jian; Bo Yan; Leilei Cao; Fengliang Qi; Hongbin Wang Rongyuan Wu; Lingchen Sun; Yongqiang Zhao; Lin Li; Kai Wang; Yicheng Wang; Xuanming Zhang; Huiyuan Wei; Chonghua Lv; Qigong Sun; Xiaolin Tian; Zhuang Jia; Jiakui Hu; Chenyang Wang; Zhiwei Zhong; Xianming Liu; Junjun Jiang |
|
| |
Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2022 |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
418-426 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. |
|
| |
Address |
New Orleans; USA; June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
MSIAU; no menciona |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RSV2022c |
Serial |
3775 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Francesco Pelosin; Saurav Jha; Andrea Torsello; Bogdan Raducanu; Joost Van de Weijer |
|
| |
Title |
Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Learning systems; Weight measurement; Image recognition; Surgery; Benchmark testing; Transformers; Stability analysis |
|
| |
Abstract |
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) – while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners. 1 |
|
| |
Address |
New Orleans; USA; June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
LAMP; 600.147 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PJT2022 |
Serial |
3784 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Hector Laria Mantecon; Yaxing Wang; Joost Van de Weijer; Bogdan Raducanu |
|
| |
Title |
Transferring Unconditional to Conditional GANs With Hyper-Modulation |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. |
|
| |
Address |
New Orleans; USA; June 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPRW |
|
| |
Notes |
LAMP; 600.147; 602.200 |
Approved |
no |
|
| |
Call Number |
LWW2022a |
Serial |
3785 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria |
|
| |
Title |
Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining |
Type |
Conference Article |
| |
Year |
2009 |
Publication |
10th IEEE International Conference on Multimedia and Expo |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1616–1619 |
|
| |
Keywords |
|
|
| |
Abstract |
Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content. |
|
| |
Address |
New York (USA) |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-1-4244-4291-1 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICME |
|
| |
Notes |
OR;MILAB;HuPBA;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ BER2009 |
Serial |
1189 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jaume Amores; N. Sebe; Petia Radeva; Theo Gevers; A. Smeulders |
|
| |
Title |
Boosting Contextual Information in Content-based Image Retrieval |
Type |
Miscellaneous |
| |
Year |
2004 |
Publication |
6th ACM SIGMM International Workshop on Multimedia Information Retrieval |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
New York, USA |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;ISE |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ ASR2004 |
Serial |
466 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
D. Jayagopi; Bogdan Raducanu; D. Gatica-Perez |
|
| |
Title |
Characterizing conversational group dynamics using nonverbal behaviour |
Type |
Conference Article |
| |
Year |
2009 |
Publication |
10th IEEE International Conference on Multimedia and Expo |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
370–373 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper addresses the novel problem of characterizing conversational group dynamics. It is well documented in social psychology that depending on the objectives a group, the dynamics are different. For example, a competitive meeting has a different objective from that of a collaborative meeting. We propose a method to characterize group dynamics based on the joint description of a group members' aggregated acoustical nonverbal behaviour to classify two meeting datasets (one being cooperative-type and the other being competitive-type). We use 4.5 hours of real behavioural multi-party data and show that our methodology can achieve a classification rate of upto 100%. |
|
| |
Address |
New York, USA |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1945-7871 |
ISBN |
978-1-4244-4290-4 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICME |
|
| |
Notes |
OR;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ JRG2009 |
Serial |
1217 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
A.F. Sole; Antonio Lopez; G. Sapiro |
|
| |
Title |
Crease Enhancement Diffusion |
Type |
Journal Article |
| |
Year |
2001 |
Publication |
Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
New York; USA |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ SLS2001 |
Serial |
485 |
|
| Permanent link to this record |
