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Found 28 papers, 15 papers with code
Softened Symbol Grounding for Neuro-symbolic Systems
Neuro-symbolic learning generally consists of two separated worlds, i. e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI.
Learning with Logical Constraints but without Shortcut Satisfaction
In this paper, we present a new framework for learning with logical constraints.
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, disparity, adversarial vulnerability, and inconsistency.
ADMap: Anti-disturbance framework for reconstructing online vectorized HD map
Therefore, this paper proposes the Anti-disturbance Map reconstruction framework (ADMap).
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI).
DebSDF: Delving into the Details and Bias of Neural Indoor Scene Reconstruction
This paper presents \textbf{DebSDF} to address these challenges, focusing on the utilization of uncertainty in monocular priors and the bias in SDF-based volume rendering.
EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection
In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method.
Ranked #1 on
3D Object Detection
on nuScenes
Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation
We consider a new problem of adapting a human mesh reconstruction model to out-of-domain streaming videos, where performance of existing SMPL-based models are significantly affected by the distribution shift represented by different camera parameters, bone lengths, backgrounds, and occlusions.
Ranked #1 on
3D Absolute Human Pose Estimation
on Surreal
PyTouch: A Machine Learning Library for Touch Processing
With the increased availability of rich tactile sensors, there is an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making.
Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction
This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos.
Ranked #39 on
3D Human Pose Estimation
on 3DPW
Joint Distribution across Representation Space for Out-of-Distribution Detection
Specifically, We construct a generative model, called Latent Sequential Gaussian Mixture (LSGM), to depict how the in-distribution latent features are generated in terms of the trace of DNN inference across representation spaces.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Skeleton2Mesh: Kinematics Prior Injected Unsupervised Human Mesh Recovery
The challenges of the latter task are two folds: (1) pose failure (i. e., pose mismatching -- different skeleton definitions in dataset and SMPL , and pose ambiguity -- endpoints have arbitrary joint angle configurations for the same 3D joint coordinates).
Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation
Furthermore, two temporal constraints are proposed to alleviate the scale and pose ambiguity respectively.
Monocular 3D Human Pose Estimation
Unsupervised 3D Human Pose Estimation
Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes
Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity.
Towards Good Practices of U-Net for Traffic Forecasting
This technical report presents a solution for the 2020 Traffic4Cast Challenge.
Hierarchical Style-based Networks for Motion Synthesis
Generating diverse and natural human motion is one of the long-standing goals for creating intelligent characters in the animated world.
Video Prediction via Example Guidance
In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics.
Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules
It is achieved by predicting future displacement field of each voxel with a WarpNet.
Deep Kinematics Analysis for Monocular 3D Human Pose Estimation
Firstly, we show that optimizing the kinematics structure of noisy 2D inputs is critical to obtain accurate 3D estimations.
AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
This paper addresses a fundamental challenge in 3D medical image processing: how to deal with imaging thickness.
Reinventing 2D Convolutions for 3D Images
Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size.
Operational Calibration: Debugging Confidence Errors for DNNs in the Field
Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field.
Composable Semi-parametric Modelling for Long-range Motion Generation
Learning diverse and natural behaviors is one of the longstanding goal for creating intelligent characters in the animated world.
Boosting Operational DNN Testing Efficiency through Conditioning
With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions.
Video Prediction via Selective Sampling
Most adversarial learning based video prediction methods suffer from image blur, since the commonly used adversarial and regression loss pair work rather in a competitive way than collaboration, yielding compromised blur effect.
Structure Preserving Video Prediction
Despite recent emergence of adversarial based methods for video prediction, existing algorithms often produce unsatisfied results in image regions with rich structural information (i. e., object boundary) and detailed motion (i. e., articulated body movement).
Skeleton-aided Articulated Motion Generation
This work make the first attempt to generate articulated human motion sequence from a single image.
Ranked #2 on
Gesture-to-Gesture Translation
on NTU Hand Digit
Depth Structure Preserving Scene Image Generation
Key to automatically generate natural scene images is to properly arrange among various spatial elements, especially in the depth direction.
