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Found 10 papers, 4 papers with code
SM$^3$: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects
Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics.
ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode
The challenges faced by text classification with large tag systems in natural language processing tasks include multiple tag systems, uneven data distribution, and high noise.
A separation logic for sequences in pointer programs and its decidability
To deal with properties on variable-length sequences and multilevel data structures, we propose sequence-heap separation logic which integrates sequences into logical reasoning on heap-manipulated programs.
Context-Aware Alignment and Mutual Masking for 3D-Language Pre-Training
The current approaches for 3D visual reasoning are task-specific, and lack pre-training methods to learn generic representations that can transfer across various tasks.
Geometry and Uncertainty-Aware 3D Point Cloud Class-Incremental Semantic Segmentation
Despite the significant recent progress made on 3D point cloud semantic segmentation, the current methods require training data for all classes at once, and are not suitable for real-life scenarios where new categories are being continuously discovered.
Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware Synthesis
Given only the class-level semantic information for unseen objects, we strive to enhance the correspondence, alignment and consistency between the visual and semantic spaces, to synthesise diverse, generic and transferable visual features.
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds
With that, we develop a large-scale synthetic scene flow dataset GTA-SF.
Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Context
Physical measurements constitute a large portion of numbers in academic papers, engineering reports, and web tables.
Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction
In this paper, a deep learning-based UAS instantaneous density prediction model is presented.
GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction
The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system.
