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Found 20 papers, 5 papers with code
Rethinking FID: Towards a Better Evaluation Metric for Image Generation
It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient.
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation
Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts.
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
Large neural models (such as Transformers) achieve state-of-the-art performance for information retrieval (IR).
When does mixup promote local linearity in learned representations?
Towards this, we study two questions: (1) how does the Mixup loss that enforces linearity in the \emph{last} network layer propagate the linearity to the \emph{earlier} layers?
Less data is more: Selecting informative and diverse subsets with balancing constraints
Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost.
Model-Efficient Deep Learning with Kernelized Classification
We investigate the possibility of using the embeddings produced by a lightweight network more effectively with a nonlinear classification layer.
In defense of dual-encoders for neural ranking
Transformer-based models such as BERT have proven successful in information retrieval problem, which seek to identify relevant documents for a given query.
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces
Negative sampling schemes enable efficient training given a large number of classes, by offering a means to approximate a computationally expensive loss function that takes all labels into account.
Less is more: Selecting informative and diverse subsets with balancing constraints
Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost.
Kernelized Classification in Deep Networks
We propose a kernelized classification layer for deep networks.
Long-tail learning via logit adjustment
Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels are associated with only a few samples.
Ranked #48 on
Long-tail Learning
on ImageNet-LT
Bipartite Conditional Random Fields for Panoptic Segmentation
To tackle this problem, we propose a CRF model, named Bipartite CRF or BCRF, with two types of random variables for semantic and instance labels.
Prototypical Priors: From Improving Classification to Zero-Shot Learning
Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and then use these relationships to draw inference on new unseen classes at test time.
Higher Order Conditional Random Fields in Deep Neural Networks
Recent deep learning approaches have incorporated CRFs into Convolutional Neural Networks (CNNs), with some even training the CRF end-to-end with the rest of the network.
Ranked #56 on
Semantic Segmentation
on PASCAL Context
Conditional Random Fields as Recurrent Neural Networks
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding.
Ranked #36 on
Semantic Segmentation
on PASCAL VOC 2012 test
A Framework for Shape Analysis via Hilbert Space Embedding
We propose a framework for 2D shape analysis using positive definite kernels defined on Kendall's shape manifold.
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
To encode the geometry of the manifold in the mapping, we introduce a family of provably positive definite kernels on the Riemannian manifold of SPD matrices.
Optimizing Over Radial Kernels on Compact Manifolds
We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification.
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
We then use the proposed framework to identify positive definite kernels on two specific manifolds commonly encountered in computer vision: the Riemannian manifold of symmetric positive definite matrices and the Grassmann manifold, i. e., the Riemannian manifold of linear subspaces of a Euclidean space.
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks.
