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Found 23 papers, 10 papers with code
Data Center Audio/Video Intelligence on Device (DAVID) -- An Edge-AI Platform for Smart-Toys
An overview is given of the DAVID Smart-Toy platform, one of the first Edge AI platform designs to incorporate advanced low-power data processing by neural inference models co-located with the relevant image or audio sensors.
Synthetic Speaking Children -- Why We Need Them and How to Make Them
Contemporary Human Computer Interaction (HCI) research relies primarily on neural network models for machine vision and speech understanding of a system user.
A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognition
We demonstrate that finetuning Conformer-transducer models on child speech yields significant improvements in ASR performance on child speech, compared to the non-finetuned models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer Learning
The approach involved finetuning a multi-speaker TTS model to work with child speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings
Bodily behavioral language is an important social cue, and its automated analysis helps in enhancing the understanding of artificial intelligence systems.
Automatic Concept Embedding Model (ACEM): No train-time concepts, No issue!
Interpretability and explainability of neural networks is continuously increasing in importance, especially within safety-critical domains and to provide the social right to explanation.
Adaptation of Whisper models to child speech recognition
We demonstrate that finetuning Whisper on child speech yields significant improvements in ASR performance on child speech, compared to non finetuned Whisper models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Neural Priority Queues for Graph Neural Networks
Graph Neural Networks (GNNs) have shown considerable success in neural algorithmic reasoning.
Ranked #20 on
Graph Regression
on Peptides-struct
FODVid: Flow-guided Object Discovery in Videos
Segmentation of objects in a video is challenging due to the nuances such as motion blurring, parallax, occlusions, changes in illumination, etc.
Parameter Efficient Local Implicit Image Function Network for Face Segmentation
Face parsing is defined as the per-pixel labeling of images containing human faces.
UMFuse: Unified Multi View Fusion for Human Editing applications
Numerous pose-guided human editing methods have been explored by the vision community due to their extensive practical applications.
VGFlow: Visibility guided Flow Network for Human Reposing
The task of human reposing involves generating a realistic image of a person standing in an arbitrary conceivable pose.
Extending Logic Explained Networks to Text Classification
Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions.
Analysis of Distributed Deep Learning in the Cloud
We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud.
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition
Our models outperformed the wav2vec2 BASE 960 on child speech which is considered a state-of-the-art ASR model on adult speech by just using 10 hours of child speech data in finetuning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis
Speech synthesis has come a long way as current text-to-speech (TTS) models can now generate natural human-sounding speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ZFlow: Gated Appearance Flow-based Virtual Try-on with 3D Priors
Image-based virtual try-on involves synthesizing perceptually convincing images of a model wearing a particular garment and has garnered significant research interest due to its immense practical applicability.
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data
Can we develop visually grounded dialog agents that can efficiently adapt to new tasks without forgetting how to talk to people?
On Model Stability as a Function of Random Seed
In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general.
Model Explanations under Calibration
Explaining and interpreting the decisions of recommender systems are becoming extremely relevant both, for improving predictive performance, and providing valid explanations to users.
EvalAI: Towards Better Evaluation Systems for AI Agents
We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale.
nocaps: novel object captioning at scale
To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task.
