Although current text-guided music generation technology can cope with simple creative scenarios, achieving finegrained control over individual text-modality conditions remains challenging as user demands become more intricate. Accordingly, we introduce the TEAcher Adapter (TEAdapter), a compact plugin designed to guide the generation process with diverse control information provided by users. In addition, we explore the controllable generation of extended music by leveraging TEAdapter control groups trained on data of distinct structural functionalities. In general, we consider controls over global, elemental, and structural levels. Experimental results demonstrate that the proposed TEAdapter enables multiple precise controls and ensures high-quality music generation. Our module is also lightweight and transferable to any diffusion model architecture. Available code and demos will be found soon at https://github.com/Ashley1101/TEAdapter.

Trajectory analysis in crowded video scenes is challenging as trajectories obtained by existing tracking algorithms are often fragmented. In this paper, we propose a new approach to do trajectory inference and clustering on fragmented trajectories, by exploring a cluster specific Latent Dirichlet Allocation(CLDA) model. LDA models are widely used to learn middle level trajectory features and perform trajectory inference. However, they often require scene priors in the learning or inference process. Our cluster specific LDA model addresses this issue by using manifold based clustering as initialization and iterative statistical inference as optimization. The output middle level features of CLDA are input to a clustering algorithm to obtain trajectory clusters. Experiments on a public dataset show the effectiveness of our approach.

Trajectory clustering in crowded video scenes is very challenging. In this paper, we propose to use a belief based correlated topic model (BCTM) to learn discriminative middle level features for trajectory clustering. By constructing a scene prior based joint Gaussian distribution, the BCTM can uncover relations between trajectory clusters and the middle level features using a parameter estimation procedure. The method has distinct advantages over Correlated Topic Model (CTM) and Random Field Topic (RFT) model previously proposed. The inputs to the BCTM are either full trajectories or trajectory fragments obtained with an existing tracking algorithm. The output BCTM features are input to a hierarchical clustering algorithm to obtain trajectory clusters. Experiments on three benchmark datasets show that the proposed BCTM and trajectory clustering approach improves the state of the art.