Search Results for author:
Found 28 papers, 13 papers with code
3D Face Modeling via Weakly-supervised Disentanglement Network joint Identity-consistency Prior
Generative 3D face models featuring disentangled controlling factors hold immense potential for diverse applications in computer vision and computer graphics.
Can Large Language Model Agents Simulate Human Trust Behaviors?
In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans.
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of computer vision realm.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers
Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github. com/camel-ai/camel.
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.
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations
To this end, we propose a Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge Regularizations.
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance
Diffusion generative models have recently greatly improved the power of text-conditioned image generation.
UnrealNAS: Can We Search Neural Architectures with Unreal Data?
Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs).
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture.
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels
To provide an alternative, we propose a novel approach that utilizes monocular RGB images and point clouds to generate pseudo scene flow labels for training scene flow networks.
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy.
Ranked #33 on
3D Part Segmentation
on ShapeNet-Part
A CLIP-Enhanced Method for Video-Language Understanding
This technical report summarizes our method for the Video-And-Language Understanding Evaluation (VALUE) challenge (https://value-benchmark. github. io/challenge\_2021. html).
Training Graph Neural Networks with 1000 Layers
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions
We add our generalized aggregation into a deep GCN framework and show it achieves state-of-the-art results in six benchmarks from OGB.
Robust Optimization as Data Augmentation for Large-scale Graphs
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks
In this paper, we introduce a new NAS framework, dubbed LC-NAS, where we search for point cloud architectures that are constrained to a target latency.
DeeperGCN: All You Need to Train Deeper GCNs
Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
SGAS: Sequential Greedy Architecture Search
Architecture design has become a crucial component of successful deep learning.
Ranked #4 on
Node Classification
on PPI
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.
DeepGCNs: Making GCNs Go as Deep as CNNs
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #5 on
3D Semantic Segmentation
on PartNet
Ordinal Distribution Regression for Gait-based Age Estimation
This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation.
Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing
A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.
DeepGCNs: Can GCNs Go as Deep as CNNs?
Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3. 7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation.
OIL: Observational Imitation Learning
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.
A novel improved fuzzy support vector machine based stock price trend forecast model
Support vector machine is a new type of machine learning method proposed in 1990s.
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks
To address this issue, we propose a novel framework which endows the model capabilities in answering more complex questions by leveraging massive external knowledge with dynamic memory networks.
