Search Results for author:
Found 62 papers, 25 papers with code
Surfacing Privacy Settings Using Semantic Matching
Online services utilize privacy settings to provide users with control over their data.
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians
Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation.
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques.
GaussianPro: 3D Gaussian Splatting with Progressive Propagation
The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed.
From Pixel to Slide image: Polarization Modality-based Pathological Diagnosis Using Representation Learning
This structure includes a pathology structure recognition method to predict structures related to thyroid tumors, an encoder-decoder network to extract pixel-level annotation information by learning the feature representations of image blocks, and an attention-based learning mechanism for the final classification task.
A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
Our experimental results underscore the potential of this fusion network as a powerful tool for computer-aided diagnosis of HCC and ICC, showcasing the benefits and prospects of integrating polarization imaging techniques into the current image-intensive digital pathological diagnosis.
Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle
Moreover, the explicit deformation modeling for discretized Gaussian points ensures ultra-fast training and rendering of a 4D scene, which is comparable to the original 3DGS designed for static 3D reconstruction.
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion
The multi-view 2. 5D diffusion directly models the structural distribution of 3D data, while still maintaining the strong generalization ability of the original 2D diffusion model, filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation.
Relightable 3D Gaussian: Real-time Point Cloud Relighting with BRDF Decomposition and Ray Tracing
We present a novel differentiable point-based rendering framework for material and lighting decomposition from multi-view images, enabling editing, ray-tracing, and real-time relighting of the 3D point cloud.
AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance
Image animation is a key task in computer vision which aims to generate dynamic visual content from static image.
Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video
This is achieved by leveraging the object-level 3D-aware image diffusion model as the primary supervision signal for training Dynamic Neural Radiance Fields (DyNeRF).
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models
In this paper, we present a scalable and efficient paradigm to address data sparsity and cold-start issues in CDR, named CDR-Adapter, by decoupling the original recommendation model from the mapping function, without requiring re-engineering the network structure.
JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation
With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation from scratch.
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e. g., depth maps).
Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detail
Drawing inspiration from voxel-based representations with the level of detail (LoD), we introduce a multi-scale tri-plane-based scene representation that is capable of capturing the LoD of the signed distance function (SDF) and the space radiance.
TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes
Existing trackers can be categorized into two association paradigms: single-feature paradigm (based on either motion or appearance feature) and serial paradigm (one feature serves as secondary while the other is primary).
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization.
Ranked #1 on
Text-to-Music Generation
on MusicCaps
AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing.
Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models
Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph.
NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation
We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images.
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
In this work, we formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints.
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and clustering.
Critical Regularizations for Neural Surface Reconstruction in the Wild
The first one is the Hessian regularization that smoothly diffuses the signed distance values to the entire distance field given noisy and incomplete input.
i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems
Noisy features and inappropriate embedding dimension assignments can deteriorate the performance of recommender systems and introduce unnecessary complexity in model training and online serving.
NeILF: Neural Incident Light Field for Physically-based Material Estimation
We present a differentiable rendering framework for material and lighting estimation from multi-view images and a reconstructed geometry.
Large-scale Optimization of Partial AUC in a Range of False Positive Rates
The partial AUC, as a generalization of the AUC, summarizes only the TPRs over a specific range of the FPRs and is thus a more suitable performance measure in many real-world situations.
CLS: Cross Labeling Supervision for Semi-Supervised Learning
Based on FixMatch, where a pseudo label is generated from a weakly-augmented sample to teach the prediction on a strong augmentation of the same input sample, CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning.
POI-Transformers: POI Entity Matching through POI Embeddings by Incorporating Semantic and Geographic Information
A general and robust POI embedding framework, the POI-Transformers, is initially proposed in this study to address these problems of POI entity matching.
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control
In this paper, we present a model-based robust RL framework for autonomous greenhouse control to meet the sample efficiency and safety challenges.
Learning Signed Distance Field for Multi-view Surface Reconstruction
In this work, we introduce a novel neural surface reconstruction framework that leverages the knowledge of stereo matching and feature consistency to optimize the implicit surface representation.
MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
MBDP consists of two kinds of dropout mechanisms, where the rollout-dropout aims to improve the robustness with a small cost of sample efficiency, while the model-dropout is designed to compensate for the lost efficiency at a slight expense of robustness.
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task.
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics.
Excess-noise suppression for a squeezed state propagating through random amplifying media via wave-front shaping
After propagating through a random amplifying medium, a squeezed state commonly shows excess noise above the shot-noise level.
Quantum Physics
Remarks on stationary and uniformly-rotating vortex sheets: Rigidity results
In this paper, we show that the only solution of the vortex sheet equation, either stationary or uniformly rotating with negative angular velocity $\Omega$, such that it has positive vorticity and is concentrated in a finite disjoint union of smooth curves with finite length is the trivial one: constant vorticity amplitude supported on a union of nested, concentric circles.
Analysis of PDEs
Understanding the drivers of sustainable land expansion using a patch-level simulation model: A case study in Wuhan, China
The change complexity lies in the detailed scale of high granularity data, and in the geometric units used to simulate the change.
Computers and Society
Practical Option Valuations of Futures Contracts with Negative Underlying Prices
Here we propose two alternatives to Black 76 to value European option future contracts in which the underlying market prices can be negative or mean reverting.
Visibility-aware Multi-view Stereo Network
As such, the adverse influence of occluded pixels is suppressed in the cost fusion.
Ranked #1 on
Point Clouds
on DTU
Learning Stereo Matchability in Disparity Regression Networks
Finally, a matchability-aware disparity refinement is introduced to improve the depth inference in weakly matchable regions.
Ranked #2 on
Stereo Disparity Estimation
on KITTI 2015
KFNet: Learning Temporal Camera Relocalization using Kalman Filtering
Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image.
ASLFeat: Learning Local Features of Accurate Shape and Localization
This work focuses on mitigating two limitations in the joint learning of local feature detectors and descriptors.
Network Cooperation with Progressive Disambiguation for Partial Label Learning
Partial Label Learning (PLL) aims to train a classifier when each training instance is associated with a set of candidate labels, among which only one is correct but is not accessible during the training phase.
BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks
Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures.
Self-Supervised Learning of Depth and Motion Under Photometric Inconsistency
The self-supervised learning of depth and pose from monocular sequences provides an attractive solution by using the photometric consistency of nearby frames as it depends much less on the ground-truth data.
ContextDesc: Local Descriptor Augmentation with Cross-Modality Context
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations.
Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes.
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong generalization ability on recent benchmarks of image-based 3D reconstruction.
Reconstructing Thin Structures of Manifold Surfaces by Integrating Spatial Curves
We present a novel surface reconstruction method using both curves and point clouds.
MVSNet: Depth Inference for Unstructured Multi-view Stereo
We present an end-to-end deep learning architecture for depth map inference from multi-view images.
Ranked #19 on
Point Clouds
on Tanks and Temples
(Mean F1 (Intermediate) metric)
Pulsar Candidate Identification with Artificial Intelligence Techniques
To address these problems, we proposed a framework which combines deep convolution generative adversarial network (DCGAN) with support vector machine (SVM) to deal with imbalance class problem and to improve pulsar identification accuracy.
Optimal Resource Allocation in Distributed Broadband Wireless Communication Systems
In this paper, we consider a combined optimization of BWC systems.
Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models
To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features.
Extracting urban impervious surface from GF-1 imagery using one-class classifiers
In this study, we investigate several one-class classifiers, such as Presence and Background Learning (PBL), Positive Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious surface area using high spatial resolution imagery of GF-1, China's new generation of high spatial remote sensing satellite, and evaluate the classification accuracy based on artificial interpretation results.
