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What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment

ParitoshParmar/MTL-AQA CVPR 2019

Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality?

A Comparison of deep learning methods for environmental sound

lijuncheng16/AudioTaggingDoneRight 20 Mar 2017

On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.

Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations

Aditi138/Embeddings EMNLP 2018

Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging.

Testing DNN Image Classifiers for Confusion & Bias Errors

ARiSE-Lab/DeepInspect 20 May 2019

We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others.

BERTphone: Phonetically-Aware Encoder Representations for Utterance-Level Speaker and Language Recognition

awslabs/speech-representations 30 Jun 2019

We introduce BERTphone, a Transformer encoder trained on large speech corpora that outputs phonetically-aware contextual representation vectors that can be used for both speaker and language recognition.

Mitigating large adversarial perturbations on X-MAS (X minus Moving Averaged Samples)

stonylinux/mitigating_large_adversarial_perturbations_on_X-MAS 19 Dec 2019

We propose the scheme that mitigates the adversarial perturbation $\epsilon$ on the adversarial example $X_{adv}$ ($=$ $X$ $\pm$ $\epsilon$, $X$ is a benign sample) by subtracting the estimated perturbation $\hat{\epsilon}$ from $X$ $+$ $\epsilon$ and adding $\hat{\epsilon}$ to $X$ $-$ $\epsilon$.

DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts

Markus-Zlabinger/pico-annotation 17 May 2020

of 0. 68 to experts in DEXA vs. 0. 40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0. 78/0. 75/0. 69 for P/I/O (in CONTROL 0. 73/0. 58/0. 46).

A Symbolic Temporal Pooling method for Video-based Person Re-Identification

aru05c/SymbolicTemporalPooling 19 Jun 2020

In video-based person re-identification, both the spatial and temporal features are known to provide orthogonal cues to effective representations.

Revealing the Myth of Higher-Order Inference in Coreference Resolution

lxucs/coref-hoi EMNLP 2020

We find that given a high-performing encoder such as SpanBERT, the impact of HOI is negative to marginal, providing a new perspective of HOI to this task.

CNN Model & Tuning for Global Road Damage Detection

vishwakarmarhl/rdd2020 17 Mar 2021

We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual Network (Resnet) and Feature Pyramid Network (FPN) backbone.