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Found 10 papers, 4 papers with code
CLHA: A Simple yet Effective Contrastive Learning Framework for Human Alignment
Reinforcement learning from human feedback (RLHF) is a crucial technique in aligning large language models (LLMs) with human preferences, ensuring these LLMs behave in beneficial and comprehensible ways to users.
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding.
Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information
To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information.
Memory Augmented Design of Graph Neural Networks
Specifically, we allow every node in the original graph to interact with a group of memory nodes.
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method
Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance.
Towards in-store multi-person tracking using head detection and track heatmaps
In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion.
Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting
The task of crowd counting in varying density scenes is an extremely difficult challenge due to large scale variations.
Qiniu Submission to ActivityNet Challenge 2018
We also propose new non-local-based models for further improvement on the recognition accuracy.
Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning method
In this paper, we first test the state of the art semantic segmentation deep learning classifiers for LUCC mapping with 7 categories in the TGRA area with rapideye 5m resolution data.
