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Found 9 papers, 4 papers with code
ID-to-3D: Expressive ID-guided 3D Heads via Score Distillation Sampling
We propose ID-to-3D, a method to generate identity- and text-guided 3D human heads with disentangled expressions, starting from even a single casually captured in-the-wild image of a subject.
Tunable Convolutions with Parametric Multi-Loss Optimization
Behavior of neural networks is irremediably determined by the specific loss and data used during training.
Adaptive Spiral Layers for Efficient 3D Representation Learning on Meshes
The success of deep learning models on structured data has generated significant interest in extending their application to non-Euclidean domains.
Poly-NL: Linear Complexity Non-local Layers with Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
Ranked #1 on
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on WIDER Face (Hard)
Poly-NL: Linear Complexity Non-Local Layers With 3rd Order Polynomials
Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions.
TESA: Tensor Element Self-Attention via Matricization
Representation learning is a fundamental part of modern computer vision, where abstract representations of data are encoded as tensors optimized to solve problems like image segmentation and inpainting.
Exploring the Challenges towards Lifelong Fact Learning
So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups.
Memory Aware Synapses: Learning what (not) to forget
We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.
