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Found 8 papers, 4 papers with code
Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection
We specifically aim to attack the widely used Faster R-CNN by changing the predicted label for a particular object in an image: where prior work has targeted one specific object (a stop sign), we generalise to arbitrary objects, with the key challenge being the need to change the labels of all bounding boxes for all instances of that object type.
Towards Generating Stylized Image Captions via Adversarial Training
While most image captioning aims to generate objective descriptions of images, the last few years have seen work on generating visually grounded image captions which have a specific style (e. g., incorporating positive or negative sentiment).
Image Captioning using Facial Expression and Attention
An analysis of the generated captions finds that, perhaps unexpectedly, the improvement in caption quality appears to come not from the addition of adjectives linked to emotional aspects of the images, but from more variety in the actions described in the captions.
ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection
This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO).
Senti-Attend: Image Captioning using Sentiment and Attention
However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption.
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data.
Face-Cap: Image Captioning using Facial Expression Analysis
In this work, we present two variants of our Face-Cap model, which embed facial expression features in different ways, to generate image captions.
Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)
Premature convergence in particle swarm optimization (PSO) algorithm usually leads to gaining local optimum and preventing from surveying those regions of solution space which have optimal points in.
