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Works42 from public data
- 646
- Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning161
This work introduces a new meta-learning framework with a loss function that adapts to each task, named Meta-Learning with Task-Adaptive Loss Function (MeTAL), which demonstrates the effectiveness and the flexibility across various domains, such as few-shot classification and few- shot regression.
- Learning to Forget for Meta-Learning114
This work proposes task-and-layer-wise attenuation on the compromised initialization of model-agnostic meta-learning to reduce its influence and names the method as L2F (Learn to Forget).
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- DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks55
This work introduces a new ultra-low precision yet effective quantization approach specifically designed for SR, dubbed Distribution-Aware Quantization (DAQ), which manages to greatly reduce the computational and resource costs without the significant sacrifice in SR performance, compared to other quantization methods.
- Scene-Adaptive Video Frame Interpolation via Meta-Learning50
This work shows the benefits of 'test-time adaptation' through simple fine-tuning of a network, then improves its efficiency by incorporating meta-learning, and obtains significant performance gains with only a single gradient update without any additional parameters.
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- CLOSER: Towards Better Representation Learning for Few-Shot Class-Incremental Learning33
It is claimed that the closer different classes are, the better for FSCIL, in stark contrast to prior beliefs that the inter-class distance should be maximized.
- LAN: Learning to Adapt Noise for Image Denoising32
A new denoising algorithm is proposed, dubbed Learning-to-Adapt-Noise (LAN), where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle.
- SANFlow: Semantic-Aware Normalizing Flow for Anomaly Detection28
This work trains NF to map the feature distributions of normal data to different distributions at each location in the given image to enhance the discriminability and highlight the efficacy of the proposed framework in improving the density modeling and thus anomaly detection performance.
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- MEIL-NeRF: Memory-Efficient Incremental Learning of Neural Radiance Fields17
A Memory-Efficient Incremental Learning algorithm for NeRF (MEIL-NeRF), which takes inspiration from NeRF itself in that a neural network can serve as a memory that returns pixel RGB values, given rays as queries.
- Batch Normalization Tells You Which Filter is Important13
This work shares the observation that the batch normalization (BN) parameters of pre-trained CNNs can be used to estimate the feature distribution of activation outputs, without processing of training data.
- Visual Tracking by Adaptive Continual Meta-Learning11
This work forms the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled, and dynamically generates the hyperparameters needed for fast initialization and online update to achieve more robustness via adaptively regulating the learning process.
- Searching for Controllable Image Restoration Networks9
A novel framework for controllable image restoration that can effectively restore multiple types and levels of degradation of a corrupted image, named TASNet, which is automatically determined by the neural architecture search algorithm, which optimizes the efficiency-accuracy trade-off of the candidate model architectures.
- Deep Variational Bayesian Modeling of Haze Degradation Process8
This work proposes to take not only a clean image and but also transmission map as latent variables, the posterior distributions of which are parameterized by corresponding neural networks: dehazing and transmission networks, respectively, to account for uncertainties and factors involved in haze degradation.
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- Rethinking RGB Color Representation for Image Restoration Models7
This work augments the RGB representation to hold structural information of local neighborhoods at each pixel while keeping the color information and pixel-grainedness unharmed, resulting in a new representation space, dubbed augmented RGB ($a$RGB) space.
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- Domain Adaptation of Learned Featuresfor Visual Localization6
This work proposes a few-shot domain adaptation framework for learned local features that deals with varying conditions in visual localization, and demonstrates the superior performance over baselines, while using a scarce number of training examples from the target domain.
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Publication data from OpenAlex; 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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