Ultrafast Video Attention Prediction with Coupled Knowledge Distillation

Large convolutional neural network models have recently demonstrated impressive performance on video attention prediction. Conventionally, these models are with intensive computation and large memory. To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net. The network is constructed based on depth-wise convolutions and takes low-resolution images as input. However, this straight-forward acceleration method will decrease performance dramatically. To this end, we propose a coupled knowledge distillation strategy to augment and train the network effectively. With this strategy, the model can further automatically discover and emphasize implicit useful cues contained in the data. Both spatial and temporal knowledge learned by the high-resolution complex teacher networks also can be distilled and transferred into the proposed low-resolution light-weight spatiotemporal network. Experimental results show that the performance of our model is comparable to ten state-of-the-art models in video attention prediction, while it costs only 0.68 MB memory footprint, runs about 10,106 FPS on GPU and 404 FPS on CPU, which is 206 times faster than previous models

Glimpse: A gaze-based measure of temporal salience

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed GLIMPSE, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. GLIMPSE could serve as the basis for several downstream tasks such as segmentation or summarization of videos. GLIMPSE’s software and data are publicly available

Human-machine cooperation in large-scale multimedia retrieval : a survey

Large-Scale Multimedia Retrieval(LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more interdisciplinary approach is necessary to develop an LSMR system that is really meaningful for humans. To this end, this paper aims to stimulate attention to the LSMR problem from diverse research fields. By explaining basic terminologies in LSMR, we first survey several representative methods in chronological order. This reveals that due to prioritizing the generality and scalability for large-scale data, recent methods interpret semantic meanings with a completely different mechanism from humans, though such humanlike mechanisms were used in classical heuristic-based methods. Based on this, we discuss human-machine cooperation, which incorporates knowledge about human interpretation into LSMR without sacrificing the generality and scalability. In particular, we present three approaches to human-machine cooperation (cognitive, ontological, and adaptive), which are attributed to cognitive science, ontology engineering, and metacognition, respectively. We hope that this paper will create a bridge to enable researchers in different fields to communicate about the LSMR problem and lead to a ground-breaking next generation of LSMR systems

Human-Machine Cooperation in Large-Scale Multimedia Retrieval: A Survey

Large-Scale Multimedia Retrieval(LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more interdisciplinary approach is necessary to develop an LSMR system that is really meaningful for humans. To this end, this paper aims to stimulate attention to the LSMR problem from diverse research fields. By explaining basic terminologies in LSMR, we first survey several representative methods in chronological order. This reveals that due to prioritizing the generality and scalability for large-scale data, recent methods interpret semantic meanings with a completely different mechanism from humans, though such humanlike mechanisms were used in classical heuristic-based methods. Based on this, we discuss human-machine cooperation, which incorporates knowledge about human interpretation into LSMR without sacrificing the generality and scalability. In particular, we present three approaches to human-machine cooperation (cognitive, ontological, and adaptive), which are attributed to cognitive science, ontology engineering, and metacognition, respectively. We hope that this paper will create a bridge to enable researchers in different fields to communicate about the LSMR problem and lead to a ground-breaking next generation of LSMR systems