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Works303 from public data
- Learning to Compare: Relation Network for Few-Shot Learning4,817
A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.
- Meta-Learning in Neural Networks: A Survey2,833
A new taxonomy is proposed that provides a more comprehensive breakdown of the space of meta-learning methods today and surveys promising applications and successes ofMeta-learning such as few-shot learning and reinforcement learning.
- Deep Mutual Learning2,054
Surprisingly, it is revealed that no prior powerful teacher network is necessary - mutual learning of a collection of simple student networks works, and moreover outperforms distillation from a more powerful yet static teacher.
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- Deeper, Broader and Artier Domain Generalization1,831
This paper builds upon the favorable domain shift-robust properties of deep learning methods, and develops a low-rank parameterized CNN model for end-to-end DG learning that outperforms existing DG alternatives.
- Learning to Generalize: Meta-Learning for Domain Generalization1,813
A novel meta-learning method for domain generalization that trains models with good generalization ability to novel domains and achieves state of the art results on a recent cross-domain image classification benchmark, as well demonstrating its potential on two classic reinforcement learning tasks.
- Learning to Generate Novel Domains for Domain Generalization566
This paper employs a data generator to synthesize data from pseudo-novel domains to augment the source domains, and outperforms current state-of-the-art DG methods on four benchmark datasets.
- Multi-level Factorisation Net for Person Re-identification549
Multi-Level Factorisation Net (MLFN), a novel network architecture that factorises the visual appearance of a person into latent discriminative factors at multiple semantic levels without manual annotation, achieves state-of-the-art results on three Re-ID datasets, as well as compelling results on the general object categorisation CIFAR-100 dataset.
- Transductive Multi-View Zero-Shot Learning513
A novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space that rectifies the projection shift between the auxiliary and target domains, exploits the complementarity of multiple semantic representations, and significantly outperforms existing methods for both zero- shot and N-shot recognition.
- Deep Domain-Adversarial Image Generation for Domain Generalisation511
This paper proposes a novel DG approach based on Deep Domain-Adversarial Image Generation based on augmenting the source training data with the generated unseen domain data to make the label classifier more robust to unknown domain changes.
- Episodic Training for Domain Generalization504
Using the Visual Decathlon benchmark, it is demonstrated that the episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training a feature for robustness to novel problems, showing that DG training can benefit standard practice in computer vision.
- 488
- Self-Supervised Representation Learning: Introduction, advances, and challenges442
This article introduces this vibrant area, including key concepts, the four main families of approaches and associated state-of-the-art techniques, and how self-supervised methods are applied to diverse modalities of data.
- Sketch-a-Net: A Deep Neural Network that Beats Humans345
It is shown that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photos or sketches.
- When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition334
An extensive evaluation of CNN-based face recognition systems (CNN-FRS) and proposes three CNN architectures which are the first reported architectures trained using LFW data to make the work easily reproducible.
- Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference279
It is shown that a simple transformer-based pipeline yields surprisingly good performance on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset.
- Incremental Few-Shot Object Detection269
OpeN-ended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples, is proposed by an elegant adaptation of the efficient CentreNet detector to the few-shot learning scenario, and meta-learning a class-wise code generator model for registering novel classes.
- 269
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- Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation251
This paper proposes a novel framework, transductive multi-view embedding, that rectifies the projection shift between the auxiliary and target domains, exploits the complementarity of multiple semantic representations, achieves state-of-the-art recognition results on image and video benchmark datasets, and enables novel cross-view annotation tasks.
- Sketch-a-Net that Beats Humans246
A multi-scale multi-channel deep neural network framework that yields sketch recognition performance surpassing that of humans, and not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.
- Hypernetwork Knowledge Graph Embeddings220
It is demonstrated that convolution simply offers a convenient computational means of introducing sparsity and parameter tying to find an effective trade-off between non-linear expressiveness and the number of parameters to learn.
- Transferring a semantic representation for person re-identification and search214
This paper presents a new semantic attribute learning approach for person re-identification and search that is trained on existing fashion photography datasets and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision.
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 2026-10-13. 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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