Multi-group adaptation for event recognition from videos

Recognizing events in consumer videos is becoming increasingly important because of the enormous growth of consumer videos in recent years. Current researches mainly focus on learning from numerous labeled videos, which is time consuming and labor expensive due to labeling the consumer videos. To alleviate the labeling process, we utilize a large number of loosely labeled Web videos (e.g., from YouTube) for visual event recognition in consumer videos. Web videos are noisy and diverse, so brute force transfer of Web videos to consumer videos may hurt the performance. To address such a negative transfer problem, we propose a novel Multi-Group Adaptation (MGA) framework to divide the training Web videos into several semantic groups and seek the optimal weight of each group. Each weight represents how relative the corresponding group is to the consumer domain. The final classifier for event recognition is learned using the weighted combination of classifiers learned from Web videos and enforced to be smooth on the consumer domain. Comprehensive experiments on three real-world consumer video datasets demonstrate the effectiveness of MGA for event recognition in consumer videos.