Planar neural networks learn image transformations from sequences.
problem Learning image transformations for mental simulation.
method Using planar neural networks, the study investigates various factors affecting the learning of image transformations.
result The approach can effectively learn and transfer image transformations, including translation, rotation, and scaling.
This paper morphs images on manifold-valued spaces using discrete geodesics.
problem Morphing manifold-valued images with discrete geodesics.
method Time discrete geodesic paths model, numerical minimization alternating between deformations and images.
result Existence of minimizing sequences for morphing manifold-valued images.
Generative model uses captions to generate images, improving semantic understanding.
problem Complex image generation models require large datasets and intricate learning.
method Adapts captioning models to generate images, using learned sentence and frame vectors.
result Images generated from multiple captions better capture semantic meaning.
New k-means method clusters radar image sequences using SPD matrices.
problem Clustering radar image sequences efficiently.
method Developed k-means on SPD matrices for non-Euclidean data. result Effective clustering of radar image sequences via SPD matrices.
Convolutional network predicts DNA chromatin structure from sequence images.
problem Predicting chromatin structure from DNA sequences.
method Developed a convolutional neural network using image-representation of DNA sequences.
result The method outperforms existing methods in prediction accuracy and training time.
Deep neural network translates math formula images to LaTeX sequences.
problem Translating math formula images to LaTeX sequences accurately and efficiently.
method Encoder-decoder architecture with CNN and LSTM, sequence-level training with policy gradient.
result State-of-the-art performance on sequence-based and image-based evaluation metrics.
Improves sequence modeling with a flow-based recurrent mixture density network.
problem Sequence modeling and sequence-to-sequence mapping applications.
method Generalized recurrent mixture density networks using normalized flow transformations.
result Significantly improved fit to image sequences measured by log-likelihood.
The paper generates future brain imaging sequences for Alzheimer's disease detection.
problem Understanding brain aging and neurodegenerative diseases through sequential image data.
method Formulated a min-max problem based on f-divergence to learn a time series generator using a deep neural network. result Generated image sequences converge to the latent truth under specific conditions, enhancing downstream tasks like Alzheimer's disease detection.
SeqAttnGAN generates interactive images based on multi-turn text descriptions.
problem Interactive image editing with multi-turn textual commands.
method SeqAttnGAN uses a neural state tracker and GAN framework for sequential image generation and refinement.
result SeqAttnGAN outperforms state-of-the-art models on interactive image editing tasks.
Non-autoregressive model speeds up sequence generation tasks.
problem Efficiency in sequence generation tasks.
method Iterative refinement based on latent variable models and denoising autoencoders.
result Significant speedup in decoding with comparable quality.
Deep network predicts action sequences for complex tasks from a scene image.
problem Scalable task and motion planning from initial scene images.
method Deep convolutional recurrent neural network that predicts action sequences.
result Predicts promising action sequences, reducing motion planning problems.
Paper tackles model failures producing outputs of undesirable length.
problem Model failures producing outputs of undesirable length.
method Develops a differentiable proxy objective and a verification approach.
result Can produce outputs 50 times longer than input and prove output length below a certain size.
DGE learns event representations from image sequences without manual annotations.
problem Data hunger and domain adaptation issues in self-supervised learning for temporal segmentation.
method Dynamic Graph Embedding (DGE) learns event representations by iteratively updating a graph and its embedding.
result DGE achieves robust temporal segmentation on benchmark datasets, outperforming state-of-the-art methods.
A GAN variant synthesizes missing MRI sequences from available ones.
problem Missing MRI sequences due to various constraints.
method Multi-modal Generative Adversarial Network (GAN) that combines multiple available sequences to synthesize missing ones.
result The proposed GAN method outperforms competing approaches in synthesizing missing MRI sequences.
The paper tackles video prediction by estimating conditional densities implicitly.
problem Temporal prediction uncertainty and high-dimensional probabilistic inference in natural scenes.
method Score-based conditional density estimation using sequence-to-image networks trained on a resilience-to-noise objective.
result The method handles occlusion boundaries and weights predictive evidence by reliability.
New properties of weighted Hilbert transform derived, useful for imaging applications.
problem Properties of weighted Hilbert transform in L2 spaces.
method Derivation of Plancherel-like equations, coerciveness, iterative sequences.
result Iterative sequences for inversion are applicable to specific cases.
Generates high-quality images using sparse DCT representations.
problem Challenges in generating images due to high dimensionality.
method Transformers trained on sparse DCT block sequences.
result Competitive image generation quality with state-of-the-art methods.
Neural Physicist learns physical dynamics from images.
problem Learning meaningful physical state representations and accurate state transitions from image sequences.
method Neural Physicist uses VAE for state extraction, NP for parameters, and SSM for dynamics.
result Achieves long-term predictions and identifies system degrees of freedom.
Learn object dynamics from unlabeled images.
problem Unsupervised learning of multiple object dynamics from unlabeled video sequences.
method Probabilistic model generating noisy positions, followed by non-linear rendering. Efficient inference method for querying the model.
result Efficient inference of object dynamics from unlabeled images.
Improves deep network generalization for image sequence reconstruction.
problem Improving generalization of deep networks for inverse image reconstruction.
method Proposes a network optimized by a variational approximation of the information bottleneck principle with stochastic latent space.
result Demonstrates improved generalization ability of inverse reconstruction networks through stochasticity and information bottleneck.
New method combines personal and reference genomes for better machine learning in DNA sequencing.
problem Improving accuracy of genetic variant calls in sequencing data.
method Interlaces personal and reference genomes to generate images for machine learning.
result Significant improvement in germline variant calling and somatic variant calling across tumor/normal data.
OnAIR reconstructs dynamic images from sparse measurements online.
problem Reconstructing dynamic images from limited or corrupted measurements.
method Online adaptive reconstruction using sparsity and low-rank models with dictionary learning.
result Memory-efficient online algorithms for sequential estimation of dictionary and images.
This paper uses CNNs to automatically segment ischaemic stroke lesions from MRI sequences.
problem Automatically segmenting ischaemic stroke lesions from MRI sequences is challenging.
method Adversarial training of CNNs on multi-sequence MRI data.
result The method achieves high Dice scores for core and penumbra segmentation.
Combines CNN and RNN for hierarchical image classification.
problem Hierarchical relations between image categories are not captured by flat classifiers.
method Uses a CNN for feature extraction and an RNN for capturing hierarchical class relations. Incorporates residual learning.
result Hierarchical networks outperform state-of-the-art CNNs on a real-world dataset.
Neural network iteratively refines image registration, achieving compactness and speed.
problem Non-compact representation of deformations in image registration.
method Recurrent registration neural network that computes local deformations iteratively.
result Our method achieves similar accuracy but is more compact and faster.
STARK improves denoising of low-depth spatial transcriptomics images.
problem Denoising spatial transcriptomics images at ultra-low sequencing depths.
method Adaptive regularization with kernel ridge regression and graph Laplacian.
result STARK optimizes denoising performance over competing methods.
SRS improves deep learning accuracy on small image datasets.
problem Training deep neural networks with limited images per class.
method Fixed sequence index sampling with batch replacement.
result Significantly improved classification accuracy on CIFAR-100.
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. Our algorithm, based on recent theoretical results for ℓ1-$…
Improves discrete latent representations using differentiable approximation bridges.
problem Improving discrete latent representations in neural networks.
method Training with a differentiable approximation bridge (DAB) neural network.
result Improves state-of-the-art performance in various domains.
ReGAN uses GANs for improved text sequence generation.
problem Limited use of GANs for text sequence generation, especially for longer sequences.
method ReGAN employs GANs with efficient policy gradient estimators and unbiased low variance techniques.
result Improved sequence generation quality and training stability.
Study Palais-Smale sequences for Ricci curvature on homogeneous spaces.
problem Characterize Palais-Smale sequences for prescribed Ricci curvature.
method Complete description of divergent sequences on compact homogeneous spaces.
result Existence of saddle points on generalized Wallach and flag manifolds.
RAU integrates attention into GRU for better sequence learning.
problem Lack of attention mechanism in GRU leads to information redundancy or loss.
method RAU adds an attention gate to GRU to adaptively focus on regions of interest.
result RAU consistently outperforms GRU and other methods in various tasks.
Bayesian approach improves image captioning quality metrics.
problem Improving image captioning quality metrics.
method Bayesian Self-Critical Sequence Training (B-SCST) with Monte Carlo dropout approximate variational inference.
result B-SCST improves CIDEr-D scores on various image captioning datasets.
Deep learning animates objects from input images and videos.
problem Animating arbitrary objects from input images and videos.
method A deep learning framework with three modules: Keypoint Detector, Dense Motion prediction network, and Motion Transfer Network.
result Our method outperforms state-of-the-art image animation and video generation methods.
The paper develops a Mayer-Vietoris sequence for groupoid homology.
problem Homology of ample groupoids via compactly supported Moore complex.
method Functoriality, compatibility with reductions, chain level isomorphism, Mayer-Vietoris sequence construction.
result Natural universal coefficient short exact sequence for groupoid homology.
We use Bayesian optimal experimental design to generate near-optimal attention sequences for faster hard attention training.
problem Training hard attention mechanisms is computationally expensive and high-variance.
method We frame hard attention as a BOED problem and use approximation methods from BOED to generate near-optimal attention sequences.
result Near-optimal attention sequences can speed up hard attention training and be reused by other networks.
Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the onet…
Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has de…
New algorithm improves GAIL for image sequences with global encoder and reward penalization.
problem Low-level, high-dimensional state input in GAIL framework.
method Global encoder and reward penalization mechanism.
result Significant performance improvement in low-level and high-dimensional tasks.
Unsupervised model separates appearance and geometry from images and videos.
problem Disentangling appearance and geometry from images and videos without supervision.
method Deformable generator network with two independent latent inputs for appearance and geometry.
result The model successfully disentangles appearance and geometry from images and videos.
We study the compactness of sequences of diffeomorphisms in almost complex manifolds in terms of the direct images of the standard integrable structure.
StoryGen uses images to generate coherent text from multiple images.
problem Generating coherent text from multiple images.
method Designing a Relational Text Data Generator algorithm to relate features from plural images.
result The model can generate meaningful paragraphs containing extracted features from input images.
Optimizes synthetic image augmentation for sim2real policy transfer in robotics.
problem Difficulty in transferring learned policies from simulated to real environments.
method Optimizes random transformations to augment synthetic images, enabling policy learning without real data.
result Significant improvement in policy accuracy on real robots for three manipulation tasks.
Seq-SetNet processes sequence sets directly, improving protein structure prediction.
problem Processing sequence sets (MSAs) for structural inference without considering sequence order.
method Developed a symmetric function module to integrate features from MSAs.
result Seq-SetNet outperforms state-of-the-art approaches by 3.6% in precision.
CAFLOW uses auto-regressive flows to translate images efficiently.
problem Image-to-image translation tasks.
method Transforms conditioning image into latent encodings using normalizing flows, models conditional distribution with auto-regressive distributions.
result Outperforms former conditional flow designs.
Novel video prediction method for complex urban scenes using optical flow.
problem Making accurate future frame predictions in complex urban scenes.
method Optical flow conditioned method using video sequences and optical flow sequences.
result Empirical evaluations show the effectiveness of the method on KITTI and Cityscapes datasets.
Parallel decoding speeds up deep autoregressive models.
problem Sequential generation limits deep autoregressive models' speed.
method Blockwise parallel decoding scheme for multiple time steps.
result Up to 7x speedup in wall-clock time with no loss in quality.
We show a connection between a surgery exact sequence in knot Floer homology and the sequence derived in [18]. As a consequence of this relationship we see that the exact sequence in [18] also works with coherent orientations and admits refinements with respect to spinc-structures. As an application of this discussion,…