Video Large Language Models (VideoLLMs), which adopt large language models for video understanding, have been demonstrated for single-shot videos. However, they usually struggle in multi-shot videos with frequent shot changes, varying camera angles, etc., which makes VideoLLMs hardly answer questions about multiple instances or shots over the whole video. We attribute this challenge to two issues: 1) the lack of multi-shot multi-instance annotations of existing datasets, and 2) the negligence of instance-aware modeling of current VideoLLMs. Therefore, we first introduce a new dataset termed MultiClip-Bench, featuring dense descriptions and question-answering pairs tailored for multi-shot and multi-instance scenarios. Moreover, since the existing VideoLLMs neglect the explicit modeling of instance-related features, we propose a novel Instance Prompt-guided Transformer, named IPFormer, to achieve instance-aware videounderstanding. In the IPFormer, we design a simple but effective instance-aware feature injection module, which encodes instance features as instance prompts via an attention-based connector. By this means, IPFormer can aggregate instance-specific information across multiple shots. Extensive experiments not only show that our dataset and model significantly improve multi-shot video understanding. but also show that our MultiClip-Bench can provide valuable training data and benchmarks for various video understanding tasks.

Automatic and interactive matting largely improve image matting by respectively alleviating the need for auxil-iary input and enabling object selection. Due to different settings on whether prompts exist, they either suffer from weakness in instance completeness or region details. Also, when dealing with different scenarios, directly switching between the two matting models introduces inconvenience and higher workload. Therefore, we wonder whether we can al-leviate the limitations of both settings while achieving unification to facilitate more convenient use. Our key idea is to offer saliency guidance for automatic mode to enable its attention to detailed regions, and also refine the instance completeness in interactive mode by replacing the binary mask guidance with a more probabilistic form. With different guidance for each mode, we can achieve unification through adaptable guidance, defined as saliency information in automatic mode and user cue for interactive one. It is instantiated as candidate feature in our method, an automatic switch for class token in pretrained ViTs and average feature of user prompts, controlled by the existence of user prompts. Then we use the candidate feature to generate a probabilistic similarity map as the guidance to alleviate the over-reliance on binary mask. Extensive experiments show that our method can adapt well to both automatic and inter-active scenarios with more light-weight framework. Code available at github.com/coconut/SMat.