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Video Saliency Detection via Dynamic Consistent Spatio-Temporal Attention Modelling

AAAI Conferences

Human vision system actively seeks salient regions and movements in video sequences to reduce the search effort. Modeling computational visual saliency map provides im-portant information for semantic understanding in many real world applications. In this paper, we propose a novel video saliency detection model for detecting the attended regions that correspond to both interesting objects and dominant motions in video sequences. In spatial saliency map, we in-herit the classical bottom-up spatial saliency map. In tem-poral saliency map, a novel optical flow model is proposed based on the dynamic consistency of motion. The spatial and the temporal saliency maps are constructed and further fused together to create a novel attention model. The pro-posed attention model is evaluated on three video datasets. Empirical validations demonstrate the salient regions de-tected by our dynamic consistent saliency map highlight the interesting objects effectively and efficiency. More im-portantly, the automatically video attended regions detected by proposed attention model are consistent with the ground truth saliency maps of eye movement data.


Zhong

AAAI Conferences

Human vision system actively seeks salient regions and movements in video sequences to reduce the search effort. Modeling computational visual saliency map provides im-portant information for semantic understanding in many real world applications. In this paper, we propose a novel video saliency detection model for detecting the attended regions that correspond to both interesting objects and dominant motions in video sequences.


OpenCV Saliency Detection - PyImageSearch

#artificialintelligence

Today's tutorial is on saliency detection, the process of applying image processing and computer vision algorithms to automatically locate the most "salient" regions of an image. In essence, saliency is what "stands out" in a photo or scene, enabling your eye-brain connection to quickly (and essentially unconsciously) focus on the most important regions. For example -- consider the figure at the top of this blog post where you see a soccer field with players on it. When looking at the photo, your eyes automatically focus on the players themselves as they are the most important areas of the photo. This automatic process of locating the important parts of an image or scene is called saliency detection.


Saliency, Scale and Information: Towards a Unifying Theory

Neural Information Processing Systems

In this paper we present a definition for visual saliency grounded in information theory. This proposal is shown to relate to a variety of classic research contributions in scale-space theory, interest point detection, bilateral filtering, and to existing models of visual saliency. Based on the proposed definition of visual saliency, we demonstrate results competitive with the state-of-the art for both prediction of human fixations, and segmentation of salient objects. We also characterize different properties of this model including robustness to image transformations, and extension to a wide range of other data types with 3D mesh models serving as an example. Finally, we relate this proposal more generally to the role of saliency computation in visual information processing and draw connections to putative mechanisms for saliency computation in human vision.


Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference

arXiv.org Artificial Intelligence

Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often unclear how and why they actually work. In this paper, we take a step toward explaining such deep learning based models through a case study on a popular neural model for NLI. In particular, we propose to interpret the intermediate layers of NLI models by visualizing the saliency of attention and LSTM gating signals. We present several examples for which our methods are able to reveal interesting insights and identify the critical information contributing to the model decisions.