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Found 28 papers, 15 papers with code
Discovering Dynamic Causal Space for DAG Structure Learning
Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.
Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention
In this paper, we aim to learn a semantic radiance field from multiple scenes that is accurate, efficient and generalizable.
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.
MonoNeRF: Learning a Generalizable Dynamic Radiance Field from Monocular Videos
More specifically, we learn an implicit velocity field to estimate point trajectory from temporal features with Neural ODE, which is followed by a flow-based feature aggregation module to obtain spatial features along the point trajectory.
Diffusion-SDF: Text-to-Shape via Voxelized Diffusion
With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e. g. text) has become a hot issue.
Dynamics-aware Adversarial Attack of Adaptive Neural Networks
In this paper, we investigate the dynamics-aware adversarial attack problem of adaptive neural networks.
SEFormer: Structure Embedding Transformer for 3D Object Detection
Effectively preserving and encoding structure features from objects in irregular and sparse LiDAR points is a key challenge to 3D object detection on point cloud.
Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
Thus the facial radiance field can be flexibly adjusted to the new identity with few reference images.
6D Camera Relocalization in Visually Ambiguous Extreme Environments
To this end, we propose: (i) a hierarchical localization system, where we leverage temporal information and (ii) a novel environment-aware image enhancement method to boost the robustness and accuracy.
HyperDet3D: Learning a Scene-conditioned 3D Object Detector
A bathtub in a library, a sink in an office, a bed in a laundry room -- the counter-intuition suggests that scene provides important prior knowledge for 3D object detection, which instructs to eliminate the ambiguous detection of similar objects.
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos
The generated text prompts are paired with corresponding video clips, and together co-train the text encoder and the video encoder via a contrastive approach.
Ranked #4 on
Action Segmentation
on GTEA
(using extra training data)
Learning Transferable Human-Object Interaction Detector With Natural Language Supervision
More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition.
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network
It results in a serious issue of lagged gradient, making the learned attack at the current step ineffective due to the architecture changes afterward.
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations.
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds.
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.
SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation
Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene.
IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
The former aims to recover the surface of point cloud through implicit function, while the latter encourages evenly-distributed points.
Graph-Based Social Relation Reasoning
Human beings are fundamentally sociable -- that we generally organize our social lives in terms of relations with other people.
Curriculum DeepSDF
When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions.
Structural Relational Reasoning of Point Clouds
The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to reason for them.
Ranked #46 on
3D Part Segmentation
on ShapeNet-Part
UniformFace: Learning Deep Equidistributed Representation for Face Recognition
In this paper, we propose a new supervision objective named uniform loss to learn deep equidistributed representations for face recognition.
Deep Embedding Learning With Discriminative Sampling Policy
Deep embedding learning aims to learn a distance metric for effective similarity measurement, which has achieved promising performance in various tasks.
Deep Variational Metric Learning
Deep metric learning has been extensively explored recently, which trains a deep neural network to produce discriminative embedding features.
Deep Adversarial Metric Learning
Learning an effective distance metric between image pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples.
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning
Specifically, we design a deep reinforcement learning model to learn the structure of the graph for bitwise interaction mining, reducing the uncertainty of binary codes by maximizing the mutual information with inputs and related bits, so that the ambiguous bits receive additional instruction from the graph for confident binarization.
Learning Deep Binary Descriptor With Multi-Quantization
In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching.
Image Set Querying Based Localization
Conventional single image based localization methods usually fail to localize a querying image when there exist large variations between the querying image and the pre-built scene.
