Search Results for author:
Found 25 papers, 11 papers with code
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
LLMs are Good Action Recognizers
Motivated by this, we propose a novel LLM-AR framework, in which we investigate treating the Large Language Model as an Action Recognizer.
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds.
Energy-Calibrated VAE with Test Time Free Lunch
To address these issues, we introduce a conditional EBM for calibrating the generative direction of VAE during training, without requiring it for the generation at test time.
Primacy Effect of ChatGPT
We have two main findings: i) ChatGPT's decision is sensitive to the order of labels in the prompt; ii) ChatGPT has a clearly higher chance to select the labels at earlier positions as the answer.
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition
Open-set object recognition aims to identify if an object is from a class that has been encountered during training or not.
How Fragile is Relation Extraction under Entity Replacements?
In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context.
A Characteristic Function-Based Method for Bottom-Up Human Pose Estimation
Therefore, via minimizing the distance between the two characteristic functions, we can optimize the model to provide a more accurate localization result for the body joints in different sub-regions of the predicted heatmap.
Social Diffusion: Long-term Multiple Human Motion Anticipation
We propose Social Diffusion, a novel method for short-term and long-term forecasting of the motion of multiple persons as well as their social interactions.
UmeTrack: Unified multi-view end-to-end hand tracking for VR
In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space.
Heatmap Distribution Matching for Human Pose Estimation
In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction.
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.
GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction
GRAPHCACHE aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences.
Time-Aware Neighbor Sampling for Temporal Graph Networks
In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time.
Geometry-Guided Progressive NeRF for Generalizable and Efficient Neural Human Rendering
Meanwhile, for achieving higher rendering efficiency, we introduce a progressive rendering pipeline through geometry guidance, which leverages the geometric feature volume and the predicted density values to progressively reduce the number of sampling points and speed up the rendering process.
Structure-Aware Label Smoothing for Graph Neural Networks
Representing a label distribution as a one-hot vector is a common practice in training node classification models.
Adaptive Data Augmentation on Temporal Graphs
To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.
Direct Multi-view Multi-person 3D Pose Estimation
Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks.
Ranked #3 on
3D Multi-Person Pose Estimation
on Panoptic
(using extra training data)
Mixup for Node and Graph Classification
In this work, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification.
Ranked #16 on
Node Classification
on Pubmed
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
GraphCrop: Subgraph Cropping for Graph Classification
We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification.
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning
We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance.
Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks
Despite great progress in 3D pose estimation from single-view images or videos, it remains a challenging task due to the substantial depth ambiguity and severe self-occlusions.
Ranked #146 on
3D Human Pose Estimation
on Human3.6M
Weakly-supervised 3D Hand Pose Estimation from Monocular RGB Images
Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D hand pose from monocular RGB images, due to substantial depth ambiguity and the difficulty of obtaining fully-annotated training data.
Hand PointNet: 3D Hand Pose Estimation Using Point Sets
Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images.
Ranked #7 on
Hand Pose Estimation
on HANDS 2017
