Search Results for author:
Found 20 papers, 9 papers with code
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion
Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy.
SeisFusion: Constrained Diffusion Model with Input Guidance for 3D Seismic Data Interpolation and Reconstruction
In response to this challenge, we propose a novel diffusion model reconstruction framework tailored for 3D seismic data.
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models
However, in the vision domain, existing RL-based reward finetuning methods are limited by their instability in large-scale training, rendering them incapable of generalizing to complex, unseen prompts.
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
Additionally, Parrot employs a joint optimization approach for the T2I model and the prompt expansion network, facilitating the generation of quality-aware text prompts, thus further enhancing the final image quality.
Simple Hierarchical Planning with Diffusion
Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets.
CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning
Neural Radiance Fields (NeRF) have demonstrated impressive performance in novel view synthesis.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Recently, several attempts have been made to replace such domain-specific, human-designed transformations with generated views that are learned.
Object-Centric Slot Diffusion
The recent success of transformer-based image generative models in object-centric learning highlights the importance of powerful image generators for handling complex scenes.
Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity
Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable.
SphereDepth: Panorama Depth Estimation from Spherical Domain
The panorama image can simultaneously demonstrate complete information of the surrounding environment and has many advantages in virtual tourism, games, robotics, etc.
Ranked #17 on
Depth Estimation
on Stanford2D3D Panoramic
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
Top-performing Model-Based Reinforcement Learning (MBRL) agents, such as Dreamer, learn the world model by reconstructing the image observations.
Model-based Reinforcement Learning
reinforcement-learning
+1
Illiterate DALL-E Learns to Compose
In this paper, we propose a simple but novel slot-based autoencoding architecture, called SLATE, for combining the best of both worlds: learning object-centric representations that allows systematic generalization in zero-shot image generation without text.
Illiterate DALL$\cdot$E Learns to Compose
In experiments, we show that this simple architecture achieves zero-shot generation of novel images without text and better quality in generation than the models based on mixture decoders.
Generative Scene Graph Networks
We formulate GSGN as a variational autoencoder in which the latent representation is a tree-structured probabilistic scene graph.
ROOTS: Object-Centric Representation and Rendering of 3D Scenes
A crucial ability of human intelligence is to build up models of individual 3D objects from partial scene observations.
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes.
IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation
Besides, we present DTN-Net, a two-stage deep model for surface normal estimation.
Generative Hierarchical Models for Parts, Objects, and Scenes
Compositional structures between parts and objects are inherent in natural scenes.
Abstraction Learning
Specifically, we propose a partition structure that contains pre-allocated abstraction neurons; we formulate abstraction learning as a constrained optimization problem, which integrates abstraction properties; we develop a network evolution algorithm to solve this problem.
