Search Results for author:
Found 11 papers, 7 papers with code
Exploiting Category Names for Few-Shot Classification with Vision-Language Models
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks.
FlowNAS: Neural Architecture Search for Optical Flow Estimation
To address this issue, we propose a neural architecture search method named FlowNAS to automatically find the better encoder architecture for flow estimation task.
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance.
Learning Contrastive Representation for Semantic Correspondence
Dense correspondence across semantically related images has been extensively studied, but still faces two challenges: 1) large variations in appearance, scale and pose exist even for objects from the same category, and 2) labeling pixel-level dense correspondences is labor intensive and infeasible to scale.
Learnable Cost Volume Using the Cayley Representation
Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors.
Semi-Supervised Learning with Meta-Gradient
In this work, we propose a simple yet effective meta-learning algorithm in semi-supervised learning.
Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle
Recent advances in convolutional neural networks(CNNs) usually come with the expense of excessive computational overhead and memory footprint.
Adversarial Learning of Privacy-Preserving and Task-Oriented Representations
For instance, there could be a potential privacy risk of machine learning systems via the model inversion attack, whose goal is to reconstruct the input data from the latent representation of deep networks.
ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes
Recent studies on face attribute transfer have achieved great success.
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
Disentangling factors of variation has become a very challenging problem on representation learning.
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
In this work, we propose a model that can learn object transfiguration from two unpaired sets of images: one set containing images that "have" that kind of object, and the other set being the opposite, with the mild constraint that the objects be located approximately at the same place.
