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Found 13 papers, 5 papers with code
RAF: Holistic Compilation for Deep Learning Model Training
In this paper, we present RAF, a deep learning compiler for training.
Complex Temporal Question Answering on Knowledge Graphs
This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions.
Manifoldron: Direct Space Partition via Manifold Discovery
A neural network with the widely-used ReLU activation has been shown to partition the sample space into many convex polytopes for prediction.
Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition
In order to solve this dilemma, we propose a multi-semantic fusion (MSF) model for improving the performance of GZSSAR, where two kinds of class-level textual descriptions (i. e., action descriptions and motion descriptions), are collected as auxiliary semantic information to enhance the learning efficacy of generalizable skeleton features.
Action Recognition
Generalized Zero Shot skeletal action recognition
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DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
This paper proposes a dynamic micro-batching approach to tackle sequence length variation and enable efficient multi-task model training.
TEQUILA: Temporal Question Answering over Knowledge Bases
An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled.
Focal and Efficient IOU Loss for Accurate Bounding Box Regression
(ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR.
Learning Domain Invariant Representations for Generalizable Person Re-Identification
Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community.
DRAformer: Differentially Reconstructed Attention Transformer for Time-Series Forecasting
Time-series forecasting plays an important role in many real-world scenarios, such as equipment life cycle forecasting, weather forecasting, and traffic flow forecasting.
Human Image Generation: A Comprehensive Survey
Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value.
Soft-IntroVAE for Continuous Latent space Image Super-Resolution
Continuous image super-resolution (SR) recently receives a lot of attention from researchers, for its practical and flexible image scaling for various displays.
Arbitrary point cloud upsampling via Dual Back-Projection Network
Point cloud upsampling is an approach to increase the density of the point cloud so that detailed geometric information can be restored.
Faithful Temporal Question Answering over Heterogeneous Sources
The method has three stages: (i) understanding the question and its temporal conditions, (ii) retrieving evidence from all sources, and (iii) faithfully answering the question.
