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Found 16 papers, 7 papers with code
PhasePerturbation: Speech Data Augmentation via Phase Perturbation for Automatic Speech Recognition
Most of the current speech data augmentation methods operate on either the raw waveform or the amplitude spectrum of speech.
Survey on deep learning in multimodal medical imaging for cancer detection
Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection.
Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization
We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature.
Multi-stage Factorized Spatio-Temporal Representation for RGB-D Action and Gesture Recognition
RGB-D action and gesture recognition remain an interesting topic in human-centered scene understanding, primarily due to the multiple granularities and large variation in human motion.
A Novel Self-training Approach for Low-resource Speech Recognition
In this paper, we propose a self-training approach for automatic speech recognition (ASR) for low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
How to Design Translation Prompts for ChatGPT: An Empirical Study
Machine translation relies heavily on the abilities of language understanding and generation.
Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection
A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features.
Improved Meta Learning for Low Resource Speech Recognition
We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach.
3D Human Motion Prediction: A Survey
3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors.
Improving Entity Linking through Semantic Reinforced Entity Embeddings
We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality.
TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search
Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description.
Ranked #11 on
Text based Person Retrieval
on CUHK-PEDES
DEEPF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals
We propose a novel pitch estimation technique called DeepF0, which leverages the available annotated data to directly learns from the raw audio in a data-driven manner.
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies
However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training.
Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales objects.
A novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images
This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images.
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning.
