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Found 14 papers, 11 papers with code
Neural Inverse Kinematics
In this work, we propose a neural IK method that employs the hierarchical structure of the problem to sequentially sample valid joint angles conditioned on the desired position and on the preceding joints along the chain.
Unsupervised Disentanglement with Tensor Product Representations on the Torus
This way, the obtained representations are naturally disentangled.
Meta Internal Learning
Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image.
Cross-Modal Retrieval Augmentation for Multi-Modal Classification
Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing.
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers
Transformers are increasingly dominating multi-modal reasoning tasks, such as visual question answering, achieving state-of-the-art results thanks to their ability to contextualize information using the self-attention and co-attention mechanisms.
Transformer Interpretability Beyond Attention Visualization
Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.
Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization
However, as we show, these methods are limited in their ability to identify the support for alternative classifications, an effect we name {\em the saliency bias} hypothesis.
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
We consider the task of generating diverse and novel videos from a single video sample.
Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation
Recently developed methods for rapid continuous volumetric two-photon microscopy facilitate the observation of neuronal activity in hundreds of individual neurons and changes in blood flow in adjacent blood vessels across a large volume of living brain at unprecedented spatio-temporal resolution.
Single Image Depth Estimation Trained via Depth from Defocus Cues
We evaluate our method on data derived from five common datasets for depth estimation and lightfield images, and present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches.
Ranked #57 on
Monocular Depth Estimation
on KITTI Eigen split
End to End Trainable Active Contours via Differentiable Rendering
We present an image segmentation method that iteratively evolves a polygon.
Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network
We present a novel deep learning method for unsupervised segmentation of blood vessels.
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks
As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs.
From Square Pieces to Brick Walls: The Next Challenge in Solving Jigsaw Puzzles
Research into computational jigsaw puzzle solving, an emerging theoretical problem with numerous applications, has focused in recent years on puzzles that constitute square pieces only.
