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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.
- 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.
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- The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification401
Experimental results show the superiority of the MC-Loss when implemented on top of common base networks can achieve state-of-the-art performance on all four fine-grained categorization datasets, and ablative studies further demonstrate the supremacy of the loss when compared with other recently proposed general-purpose losses for visual classification.
- Fine-Grained Visual Classification via Progressive Multi-granularity Training of Jigsaw Patches389
This work proposes a novel framework for fine-grained visual classification with a progressive training strategy that effectively fuses features from different granularities, and a random jigsaw patch generator that encourages the network to learn features at specificgranularities.
- 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.
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- 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.
- Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer243
This work proposes to simplify the meta-learning task by focusing solely on the simplest component – the classifier, whilst leaving the en-coder and decoder to pre-training, and introduces a Classifier Weight Transformer (CWT) designed to dynamically adapt the support-set trained classifier’s weights to each query image in an inductive way.
- Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval217
This paper proposes a novel ZS-SBIR framework to jointly model sketches and photos into a common embedding space, and forms a novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap.
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- Stochastic Classifiers for Unsupervised Domain Adaptation179
This paper introduces a novel method called STochastic clAssifieRs (STAR) for addressing the problem of misaligned local regions between source and target domain, which finds that using more classifiers leads to better performance, but also introduces more model parameters, therefore risking overfitting.
- Text extraction from natural scene image: A survey175
This paper offers the researchers a link to public image database for the algorithm assessment of text extraction from natural scene images and draws attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist.
- Style-Based Global Appearance Flow for Virtual Try-On174
A novel global appearance flow estimation model is proposed in this work which enables the use of a global style vector to encode a whole-image context to cope with the aforementioned challenges.
- CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not163
The proposed CLIP and prompt learning paradigm carries great promise in tackling other sketch-related tasks (not limited to ZS-SBIR) where data scarcity remains a great challenge.
- Deep Learning for Free-Hand Sketch: A Survey and A Toolbox160
A comprehensive survey of the deep learning techniques oriented at free-hand sketch data, and the applications that they enable.
- Your “Flamingo” is My “Bird”: Fine-Grained, or Not155
The key intuition that: coarse- level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier is discovered, which enables the design of a very simple albeit surprisingly effective solution to a new problem.
- Learning to Sketch with Shortcut Cycle Consistency137
A novel approach for translating an object photo to a sketch, mimicking the human sketching process, and shows that the synthetic sketches can be used to train a better fine-grained sketch-based image retrieval model, effectively alleviating the problem of sketch data scarcity.
- A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution133
This survey provides a comprehensive review of established techniques and recent developments in HFR, and offers a detailed account of datasets and benchmarks commonly used for evaluation.
- DemoFusion: Democratising High-Resolution Image Generation With No $$$131
This paper aims to democratise high-resolution GenAl by advancing the frontier of high-resolution generation while remaining accessible to a broad audience, and demonstrates that existing Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution image generation.
- Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval131
A reinforcement learning based cross-modal retrieval framework that directly optimizes rank of the ground-truth photo over a complete sketch drawing episode and introduces a novel reward scheme that circumvents the problems related to irrelevant sketch strokes, and thus provides us with a more consistent rank list during the retrieval.
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- SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval130
This work proposes a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset and shows that state-of-the-art hashing models specifically engineered for static images fail to perform well on temporal sketch data.
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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