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Found 18 papers, 8 papers with code
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
Heterogeneous Text-Attributed Graphs (HTAGs), where different types of entities are not only associated with texts but also connected by diverse relationships, have gained widespread popularity and application across various domains.
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate relationships and diverse relational semantics inherent in heterogeneous graph structures.
Negative-Free Self-Supervised Gaussian Embedding of Graphs
Therefore, we can minimize the distance between the distribution of learned representations and the isotropic Gaussian distribution to promote the uniformity of node representations.
Enhancing Vectorized Map Perception with Historical Rasterized Maps
To fully exploit a historical map, we propose two novel modules to enhance BEV features and map element queries.
Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling
The results demonstrate that our method can learn the absolute scale with geometric modeling and accurately estimate scale-aware depth for monocular scenes.
Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering
In this paper, we introduce Scalable and Adaptive Spectral Embedding (SASE), a simple attributed graph clustering method devoid of parameter learning.
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
Therefore, we propose to expand the positive pair set with node-neighbor pairs.
Reliable Node Similarity Matrix Guided Contrastive Graph Clustering
We argue that an ideal node similarity matrix within the representation space should accurately reflect the inherent semantic relationships among nodes, ensuring the preservation of semantic similarities in the learned representations.
Co-benefits of Agricultural Diversification and Technology for Food and Nutrition Security in China
While a strategic choice and management of crop and livestock species can improve nutrition, the environmental and production benefits of agricultural diversification are currently not well interlinked with Chinas food and nutrition security discussions.
$E^{3}$Gen: Efficient, Expressive and Editable Avatars Generation
Extensive experiments demonstrate that our method achieves superior performance in avatar generation and enables expressive full-body pose control and editing.
IPAD: Industrial Process Anomaly Detection Dataset
Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes.
Rethinking Clothes Changing Person ReID: Conflicts, Synthesis, and Optimization
However, such improvement sacrifices the performance under the standard protocol, caused by the inner conflict between standard and CC.
Inter-X: Towards Versatile Human-Human Interaction Analysis
We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions, semantic interaction categories, interaction order, and the relationship and personality of the subjects.
GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots
In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots.
AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy
In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.
A Visual Navigation Perspective for Category-Level Object Pose Estimation
In this paper, we take a deeper look at the inference of analysis-by-synthesis from the perspective of visual navigation, and investigate what is a good navigation policy for this specific task.
Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept
Inspired by this, we propose a new framework that leverages the labeled 3D human poses to learn a 3D concept of the human body to reduce the ambiguity.
Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery
Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth maps into a unified reference coordinate for tissue reconstruction.
