Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Video concept learning often requires a large set oftraining samples. In practice, hover, acquiring noise-free training labels with sufficient positive examples is very expensive. A plausible solution for training data collection is by sampling from the vast quantities of images and videos on the b. Such a solution is motivated by the assumption that the retrieved images or videos are highly correlated with the query. Still, a number ofchallenges remain. First, b videos are often untrimmed. Thus, only parts of the videos are relevant to the query. Second, the retrieved b images are always highly relevant to the issued query. Hover, thoughtlessly utilizing the images in the video domain may even hurt the performance due to the ll-known semantic drift and domain gap problems. As a result, a valid question is how b images and videos interact for video concept learning. ,  propose a Lead-Exceed Neural Network (LENN), which reinforces the training on b images and videos in a curriculum manner. Specifically, the training proceeds by inputting frames of b videos to obtain a network. The b images are then filtered by the learnt network and the selected images are additionally fed into the network to enhance the architecture and further trim the videos. In addition, Long Short-Term Memory (LSTM) can be applied on the trimmed videos to explore temporal information. Encouraging results are reported on UCFIOl, TRECVID 2013 and 2014 MEDTest in the context ofboth action recognition and event detection. Without using human annotated exemplars, our proposed LENN can achieve 74.4% accuracy on UCFIOI dataset.
['Chuang Gan', 'Ting Yao', 'Kuiyuan Yang', 'Yi Yang', 'Tao Mei']
I am writing a research paper on a idea You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images
[{"from": "human", "value": "I am writing a research paper on a idea You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Video concept learning often requires a large set oftraining samples. In practice, hover, acquiring noise-free training labels with sufficient positive examples is very expensive. A plausible solution for training data collection is by sampling from the vast quantities of images and videos on the b. Such a solution is motivated by the assumption that the retrieved images or videos are highly correlated with the query. Still, a number ofchallenges remain. First, b videos are often untrimmed. Thus, only parts of the videos are relevant to the query. Second, the retrieved b images are always highly relevant to the issued query. Hover, thoughtlessly utilizing the images in the video domain may even hurt the performance due to the ll-known semantic drift and domain gap problems. As a result, a valid question is how b images and videos interact for video concept learning. ,  propose a Lead-Exceed Neural Network (LENN), which reinforces the training on b images and videos in a curriculum manner. Specifically, the training proceeds by inputting frames of b videos to obtain a network. The b images are then filtered by the learnt network and the selected images are additionally fed into the network to enhance the architecture and further trim the videos. In addition, Long Short-Term Memory (LSTM) can be applied on the trimmed videos to explore temporal information. Encouraging results are reported on UCFIOl, TRECVID 2013 and 2014 MEDTest in the context ofboth action recognition and event detection. Without using human annotated exemplars, our proposed LENN can achieve 74.4% accuracy on UCFIOI dataset."}]