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Found 12 papers, 4 papers with code
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text
In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification.
Let Real Images be as a Judger, Spotting Fake Images Synthesized with Generative Models
Unfortunately, the artifact patterns in fake images synthesized by different generative models are inconsistent, leading to the failure of previous research that relied on spotting subtle differences between real and fake.
Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG
This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition.
Zero-shot Cross-lingual Transfer without Parallel Corpus
We got the new SOTA on different tasks without any dependencies on the parallel corpus or translation models.
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory.
Attention Weighted Local Descriptors
Local features detection and description are widely used in many vision applications with high industrial and commercial demands.
A Block-Based Adaptive Decoupling Framework for Graph Neural Networks
Graph neural networks (GNNs) with feature propagation have demonstrated their power in handling unstructured data.
MTLDesc: Looking Wider to Describe Better
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context.
A Deep Learning Inference Scheme Based on Pipelined Matrix Multiplication Acceleration Design and Non-uniform Quantization
Matrix multiplication is the bedrock in Deep Learning inference application.
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks.
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform
Stereo matching is a critical task for robot navigation and autonomous vehicles, providing the depth estimation of surroundings.
An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform
In our past few years' of commercial deployment experiences, we identify localization as a critical task in autonomous machine applications, and a great acceleration target.
