Generalist models, capable of handling multiple modalities and tasks simultaneously, are currently one of the hottest research topics. However, due to interference between different tasks during the training process, existing generalist models require a very large decoder to achieve good results in various tasks, which makes real-time prediction difficult for current generalist models. This paper introduces Generalist YOLO, which takes a significant step towards real-time prediction systems for visual language generalist models. The proposed Generalist YOLO uses a unified encoder to reduce conflicts between different tasks, thereby decreasing the complexity required by the decoder. It also introduces a primary-secondary co-attention mechanism that allows different tasks to learn together more effectively, achieving high efficiency and high accuracy. We propose a semantically consistent asymmetric training strategy, allowing various tasks to benefit from performance improvements brought by the latest research results in various fields. The proposed Generalist YOLO achieves excellent results on various vision and language tasks based on MS COCO. While maintaining high accuracy across all tasks, it is 135 times faster than existing generalist models. The source code is released on GitHub at https://github.com/WongKinYiu/GeneralistYOLO.

In this paper, we study the subsequence matching problem of near-duplicate video detection. In particular, we address the application of monitoring a continuous stream with a large video dataset. To achieve real-time response and high accuracy, we propose a novel framework containing two characteristics . First, the subsequence matching is transformed to a 2-D Hough space projection of pairwise frame similarities between two subsequences. We present an approximate Hough transform that replaces the 2-D Hough space with a 1-D Hough space. The near-duplicate subsequence detection can be deemed to be the voting and searching in the 1-D Hough space with a lower time complexity. Second, a coarse-to-fine matching strategy is incorporated in the proposed framework. The coarse level matching selects the candidate videos based on the time-decay hit frequency between the query stream and dataset videos. The fine level matching applies the approximate Hough transform to detect the near-duplicate subsequences coexisting within the query stream and candidate videos. Several state-of-the-art methods are implemented for comparison. Experimental results show our framework outperforms in terms of accuracy and efficiency.