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Found 24 papers, 12 papers with code
Motion-aware Latent Diffusion Models for Video Frame Interpolation
With the advancement of AIGC, video frame interpolation (VFI) has become a crucial component in existing video generation frameworks, attracting widespread research interest.
Distribution-Aware Data Expansion with Diffusion Models
In this paper, we propose DistDiff, an effective data expansion framework based on the distribution-aware diffusion model.
Retrieval-Augmented Generation for AI-Generated Content: A Survey
We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators.
Structure-Guided Adversarial Training of Diffusion Models
In this pioneering approach, we compel the model to learn manifold structures between samples in each training batch.
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing
To address this issue, we propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample into forward and reverse processes.
RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models
In this paper, we propose a new training-free and transferred-friendly text-to-image generation framework, namely RealCompo, which aims to leverage the advantages of text-to-image and layout-to-image models to enhance both realism and compositionality of the generated images.
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
In this paper, we propose a brand new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG), harnessing the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models.
Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information.
Improving Diffusion-Based Image Synthesis with Context Prediction
In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context.
Ranked #1 on
Image Inpainting
on CelebA
(LPIPS metric)
MedXChat: Bridging CXR Modalities with a Unified Multimodal Large Model
Despite the success of Large Language Models (LLMs) in general image tasks, a gap persists in the medical field for a multimodal large model adept at handling the nuanced diversity of medical images.
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings.
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images.
The Importance of Suppressing Complete Reconstruction in Autoencoders for Unsupervised Outlier Detection
The core idea of our scheme is that in order to better detect high leverage points, we should suppress the complete reconstruction of the dataset to convert high leverage points into influential points, and it is also necessary to ensure that the differences between the eigenvalues of the covariance matrix of the original dataset and their corresponding reconstructed results in the direction of each principal component are equal.
Dimensionality Reduced Antenna Array for Beamforming/steering
Finally, the 16/8/4 -array beam steering was demonstrated by using 4/3/2 active controllers, respectively.
Diffusion Models: A Comprehensive Survey of Methods and Applications
This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Unsupervised/self-supervised graph representation learning is critical for downstream node- and graph-level classification tasks.
Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer
In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a unified way.
Spatial Autoregressive Coding for Graph Neural Recommendation
To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm.
Spectral Propagation Graph Network for Few-shot Time Series Classification
To the best of our knowledge, SPGN is the first to utilize spectral comparisons in different intervals and involve spectral propagation across all time series with graph networks for few-shot TSC.
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations.
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series
First, we revisit the augmentation methods for time series of existing works and note that they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context.
DPGN: Distribution Propagation Graph Network for Few-shot Learning
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #2 on
Few-Shot Learning
on Mini-ImageNet - 1-Shot Learning
DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction
The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series.
