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Found 19 papers, 6 papers with code
AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism
The latter is from the cross-modal perspective, which is used to learn the sentence-level and word-level motion-text cross-modal relationship.
Ranked #4 on
Motion Synthesis
on HumanML3D
Pose-aware Attention Network for Flexible Motion Retargeting by Body Part
Moreover, we also show that our framework can generate reasonable results even for a more challenging retargeting scenario, like retargeting between bipedal and quadrupedal skeletons because of the body part retargeting strategy and PAN.
Robots in the Garden: Artificial Intelligence and Adaptive Landscapes
This paper introduces ELUA, the Ecological Laboratory for Urban Agriculture, a collaboration among landscape architects, architects and computer scientists who specialize in artificial intelligence, robotics and computer vision.
The Future of Artificial Intelligence (AI) and Machine Learning (ML) in Landscape Design: A Case Study in Coastal Virginia, USA
There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline.
Cultivated Wildness: Technodiversity and Wildness in Machines
Drawing cases in environmental engineering, computer science, and landscape architecture research, this paper explores a framework to construct wild places with intelligent machines.
Cybernetic Environment: A Historical Reflection on System, Design, and Machine Intelligence
Taking on a historical lens, this paper traces the development of cybernetics and systems thinking back to the 1950s, when a group of interdisciplinary scholars converged to create a new theoretical model based on machines and systems for understanding matters of meaning, information, consciousness, and life.
PNM: Pixel Null Model for General Image Segmentation
A major challenge in image segmentation is classifying object boundaries.
Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction
However, considering the variants and diverse action types in human motion data, the cross-dependency of the spatio-temporal relationships will be difficult to depict due to the decoupled modeling strategy, which may also exacerbate the problem of insufficient generalization.
Deep Open Set Identification for RF Devices
Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process.
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments.
Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy
In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences.
Ranked #1 on
Pose Estimation
on ITOP front-view
Rail-5k: a Real-World Dataset for Rail Surface Defects Detection
This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario, namely the rail surface defects detection task.
Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units
We design multi-horizon forecasting models for limit order book (LOB) data by using deep learning techniques.
Deep Learning for Market by Order Data
Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information.
Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels
We also provide a theoretical explanation of our method.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment
The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods.
Deep Learning for Portfolio Optimization
We adopt deep learning models to directly optimise the portfolio Sharpe ratio.
Deep Reinforcement Learning for Trading
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts.
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities.
Computational Finance
