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Found 13 papers, 4 papers with code
Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences
CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings.
CMVAE: Causal Meta VAE for Unsupervised Meta-Learning
Unsupervised meta-learning aims to learn the meta knowledge from unlabeled data and rapidly adapt to novel tasks.
Searching Similarity Measure for Binarized Neural Networks
Being a promising model to be deployed in resource-limited devices, Binarized Neural Networks (BNNs) have drawn extensive attention from both academic and industry.
GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm
To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs.
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature
Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios.
Gated Domain-Invariant Feature Disentanglement for Domain Generalizable Object Detection
For Domain Generalizable Object Detection (DGOD), Disentangled Representation Learning (DRL) helps a lot by explicitly disentangling Domain-Invariant Representations (DIR) from Domain-Specific Representations (DSR).
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks
Although convolution neural network based stereo matching architectures have made impressive achievements, there are still some limitations: 1) Convolutional Feature (CF) tends to capture appearance information, which is inadequate for accurate matching.
Transductive Few-Shot Classification on the Oblique Manifold
Few-shot learning (FSL) attempts to learn with limited data.
Fractal Pyramid Networks
We propose a new network architecture, the Fractal Pyramid Networks (PFNs) for pixel-wise prediction tasks as an alternative to the widely used encoder-decoder structure.
BCNN: Binary Complex Neural Network
Motivated by the complex neural networks, in this paper we introduce complex representation into the BNNs and propose Binary complex neural network -- a novel network design that processes binary complex inputs and weights through complex convolution, but still can harvest the extraordinary computation efficiency of BNNs.
Attributes-aided Part Detection and Refinement for Person Re-identification
Person attributes are often exploited as mid-level human semantic information to help promote the performance of person re-identification task.
External Patch Prior Guided Internal Clustering for Image Denoising
One category of denoising methods exploit the priors (e. g., TV, sparsity) learned from external clean images to reconstruct the given noisy image, while another category of methods exploit the internal prior (e. g., self-similarity) to reconstruct the latent image.
Deep Learning Shape Priors for Object Segmentation
In this paper we introduce a new shape-driven approach for object segmentation.
