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Claim this profileWorks276 from public data
- 1,512
- Class-Incremental Learning: Survey and Performance Evaluation on Image Classification940
This paper provides a complete survey of existing class-incremental learning methods for image classification, and in particular, it performs an extensive experimental evaluation on thirteen class-Incremental methods.
- Class-incremental learning: survey and performance evaluation940
This paper provides a complete survey of existing class-incremental learning methods for image classification, and in particular, it performs an extensive experimental evaluation on thirteen class-Incremental methods.
- 928
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- 762
- 596
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
- RankIQA: Learning from Rankings for No-Reference Image Quality Assessment538
This work proposes a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA), and demonstrates how this approach can be made significantly more efficient than traditional Siamese Networks by forward propagating a batch of images through a single network and backpropagating gradients derived from all pairs of images in the batch.
- 513
- Coloring Local Feature Extraction512
The results show that color descriptors remain reliable under photometric and geometrical changes, and with decreasing image quality, and for all experiments a combination of color and shape outperforms a pure shape-based approach.
- 496
- Semantic Drift Compensation for Class-Incremental Learning444
This work proposes a new method to estimate the drift, called semantic drift, of features and compensate for it without the need of any exemplars, and shows that the proposed SDC when combined with existing methods to prevent forgetting consistently improves results.
- Learning the Model Update for Siamese Trackers431
This work uses a convolutional neural network, called UpdateNet, which given the initial template, the accumulated template and the template of the current frame aims to estimate the optimal template for the next frame, and demonstrates the generality of the proposed approach by applying it to two Siamese trackers.
- Fast Anisotropic Gauss Filtering402
The decomposition of the anisotropic Gaussian in a one-dimensional (1-D) Gauss filter in the x-direction followed by a 1-D filter in a nonorthogonal direction phi is derived.
- Generalized Source-free Domain Adaptation342
This paper proposes a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation.
- Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting338
An approach to avoiding catastrophic forgetting in sequential task learning scenarios based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters, which leads to significantly better performance on lifelong learning of sequential tasks.
- 333
- Transferring GANs: Generating Images from Limited Data324
The results show that using knowledge from pretrained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when the target data is limited and it is suggested that density may be more important than diversity.
- 324
- Leveraging Unlabeled Data for Crowd Counting by Learning to Rank311
This work proposes a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework and demonstrates how to efficiently learn from these unlabeling datasets by incorporating learning- to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps.
- Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification287
This paper shows that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models.
- Generalized Gamut Mapping using Image Derivative Structures for Color Constancy241
It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output and it is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light.
- Avalanche: an End-to-End Library for Continual Learning225
The proposed Avalanche, an open-source end-to-end library for continual learning research based on PyTorch is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.
- Multi-Modal Fusion for End-to-End RGB-T Tracking219
This work proposes an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking, and evaluates the effectiveness of modality fusion in each of the main components in DiMP, i.e. feature extractor, target estimation network, and classifier.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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