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Found 17 papers, 7 papers with code
Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation
However, these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets, reducing class discrimination, and an exclusive focus on mean feature consistency, lacking precision and comprehensiveness.
Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring
Contemporary deep learning multi-scale deblurring models suffer from many issues: 1) They perform poorly on non-uniformly blurred images/videos; 2) Simply increasing the model depth with finer-scale levels cannot improve deblurring; 3) Individual RGB frames contain a limited motion information for deblurring; 4) Previous models have a limited robustness to spatial transformations and noise.
Multi-level Second-order Few-shot Learning
The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning.
Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification
Our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN into a positive definite matrix with second-order statistics to which PN operators are applied, forming so-called Second-order Pooling (SOP).
Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration
The heavy defocus blur in DP pairs affects the performance of matching-based depth estimation approaches.
Few-shot Action Recognition with Permutation-invariant Attention
Such encoded blocks are aggregated by permutation-invariant pooling to make our approach robust to varying action lengths and long-range temporal dependencies whose patterns are unlikely to repeat even in clips of the same class.
Ranked #6 on
Few Shot Action Recognition
on Kinetics-100
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
The majority of existing few-shot learning methods describe image relations with binary labels.
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer
In this paper, we study the impact of scale and location mismatch in the few-shot learning scenario, and propose a novel Spatially-aware Matching (SM) scheme to effectively perform matching across multiple scales and locations, and learn image relations by giving the highest weights to the best matching pairs.
Few-Shot Learning via Saliency-guided Hallucination of Samples
To the best of our knowledge, we are the first to leverage saliency maps for such a task and we demonstrate their usefulness in hallucinating additional datapoints for few-shot learning.
Deep Stacked Hierarchical Multi-patch Network for Image Deblurring
depth, we propose a stacked version of our multi-patch model.
Ranked #9 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
Power Normalizing Second-order Similarity Network for Few-shot Learning
In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations.
Model Selection for Generalized Zero-shot Learning
Specifically, we leverage two sources of datapoints (observed and auxiliary) to train some classifier to recognize which test datapoints come from seen and which from unseen classes.
Generalized Zero-Shot Learning
Generative Adversarial Network
+1
Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond
To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams.
A Deeper Look at Power Normalizations
In this paper, we reconsider these operators in the deep learning setup by introducing a novel layer that implements PN for non-linear pooling of feature maps.
Zero-Shot Kernel Learning
In contrast, we apply well-established kernel methods to learn a non-linear mapping between the feature and attribute spaces.
Museum Exhibit Identification Challenge for Domain Adaptation and Beyond
To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams [15].
Limits on Axion Couplings from the first 80-day data of PandaX-II Experiment
We report new searches for the solar axions and galactic axion-like dark matter particles, using the first low-background data from PandaX-II experiment at China Jinping Underground Laboratory, corresponding to a total exposure of about $2. 7\times 10^4$ kg$\cdot$day.
High Energy Physics - Experiment Solar and Stellar Astrophysics High Energy Physics - Phenomenology
