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Found 33 papers, 8 papers with code
BRepFormer: Transformer-Based B-rep Geometric Feature Recognition
BRepFormer encodes and fuses the geometric and topological features of the models.
DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions
To this end, we introduce DragScene, a framework that integrates drag-style editing with diverse 3D representations.
SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization
Using the size estimator and MPQ, we develop a calibrated algorithm to identify optimal hyperparameters in just 10 minutes, achieving a 1. 69$\times$ efficiency increase with quality comparable to state-of-the-art methods.
FiffDepth: Feed-forward Transformation of Diffusion-Based Generators for Detailed Depth Estimation
Monocular Depth Estimation (MDE) is essential for applications like 3D scene reconstruction, autonomous navigation, and AI content creation.
MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation
More specifically, we first replace rotation quaternions with Euler angles; then, we apply region adaptive hierarchical transform to key attributes to reduce entropy.
Tuning-Free Visual Customization via View Iterative Self-Attention Control
Extensive experiments show the efficacy of VisCtrl across a spectrum of tasks, including personalized editing of images, videos, and 3D scenes.
NOFA: NeRF-based One-shot Facial Avatar Reconstruction
In this work, we propose a one-shot 3D facial avatar reconstruction framework that only requires a single source image to reconstruct a high-fidelity 3D facial avatar.
DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text.
Temporal-spatial Correlation Attention Network for Clinical Data Analysis in Intensive Care Unit
Our method can also find key clinical indicators of important outcomes that can be used to improve treatment options.
Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance
The chest X-ray is often utilized for diagnosing common thoracic diseases.
Instrumental Variable Learning for Chest X-ray Classification
The chest X-ray (CXR) is commonly employed to diagnose thoracic illnesses, but the challenge of achieving accurate automatic diagnosis through this method persists due to the complex relationship between pathology.
Chest X-ray Image Classification: A Causal Perspective
The chest X-ray (CXR) is one of the most common and easy-to-get medical tests used to diagnose common diseases of the chest.
TextIR: A Simple Framework for Text-based Editable Image Restoration
We use the text-image feature compatibility of the CLIP to alleviate the difficulty of fusing text and image features.
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
This general and dataset-independent property makes us search for the MPQ policy over a rather small-scale proxy dataset and then the policy can be directly used to quantize the model trained on a large-scale dataset.
ITstyler: Image-optimized Text-based Style Transfer
Text-based style transfer is a newly-emerging research topic that uses text information instead of style image to guide the transfer process, significantly extending the application scenario of style transfer.
Towards Arbitrary Text-driven Image Manipulation via Space Alignment
Then, the text input can be directly accessed into the StyleGAN space and be used to find the semantic shift according to the text description.
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models
Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors.
High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors
Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance.
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
Our experiments conducted on four tasks and four offline RL algorithms expose a disquieting fact: none of the existing offline RL algorithms is immune to such a backdoor attack.
PS-NeRV: Patch-wise Stylized Neural Representations for Videos
We study how to represent a video with implicit neural representations (INRs).
Ranked #6 on
Video Reconstruction
on UVG
Improving the Latent Space of Image Style Transfer
The content contrastive loss enables the encoder to retain more available details.
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network
The application of deep reinforcement learning in multi-agent systems introduces extra challenges.
Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards.
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization
To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner.
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism
Recently, some challenging tasks in multi-agent systems have been solved by some hierarchical reinforcement learning methods.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+5
The $f$-Divergence Reinforcement Learning Framework
In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards.
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning
In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations.
Distributional Reinforcement Learning
reinforcement-learning
+4
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning
As one of the solutions to the decentralized partially observable Markov decision process (Dec-POMDP) problems, the value decomposition method has achieved significant results recently.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
CMS-LSTM: Context Embedding and Multi-Scale Spatiotemporal Expression LSTM for Predictive Learning
To tackle the increasing ambiguity during forecasting, we design CMS-LSTM to focus on context correlations and multi-scale spatiotemporal flow with details on fine-grained locals, containing two elaborate designed blocks: Context Embedding (CE) and Spatiotemporal Expression (SE) blocks.
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification
Correspondingly, different models need to be designed for different datasets, which further increases the workload of designing architectures; 2) the mainstream framework is a patch-to-pixel framework.
Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network
However, the existing CNN-based models operate at the patch-level, in which pixel is separately classified into classes using a patch of images around it.
Locality-Aware Rotated Ship Detection in High-Resolution Remote Sensing Imagery Based on Multi-Scale Convolutional Network
Ship detection has been an active and vital topic in the field of remote sensing for a decade, but it is still a challenging problem due to the large scale variations, the high aspect ratios, the intensive arrangement, and the background clutter disturbance.
