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Found 6 papers, 2 papers with code
AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks
Large language models (LLMs) have demonstrated impressive results on natural language tasks, and security researchers are beginning to employ them in both offensive and defensive systems.
TranSalNet: Towards perceptually relevant visual saliency prediction
Due to the inherent inductive biases of CNN architectures, there is a lack of sufficient long-range contextual encoding capacity.
Ranked #1 on
Saliency Prediction
on SALICON
Gradient Weighted Superpixels for Interpretability in CNNs
As Convolutional Neural Networks embed themselves into our everyday lives, the need for them to be interpretable increases.
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition
However, by exploiting a simple technique that removes motion information, we show that it is not the case that this technique is effective as-is for representing relevance in non-image tasks.
Weakly-Supervised Temporal Localization via Occurrence Count Learning
We propose a novel model for temporal detection and localization which allows the training of deep neural networks using only counts of event occurrences as training labels.
Detecting Violent and Abnormal Crowd activity using Temporal Analysis of Grey Level Co-occurrence Matrix (GLCM) Based Texture Measures
We utilise computer vision techniques to develop an automated method of abnormal crowd detection that can aid a human operator in the detection of violent behaviour.
