  Video-Language Models (VLMs), powered by the advancements in Large Language
Models (LLMs), are charting new frontiers in video understanding. A pivotal
challenge is the development of an efficient method to encapsulate video
content into a set of representative tokens to align with LLMs. In this work,
we introduce Slot-VLM, a novel framework designed to generate semantically
decomposed video tokens, in terms of object-wise and event-wise visual
representations, to facilitate LLM inference. Particularly, we design a
SlowFast Slots module, i.e., SF-Slots, that adaptively aggregates the dense
video tokens from the CLIP vision encoder to a set of representative slots. In
order to take into account both the spatial object details and the varied
temporal dynamics, SF-Slots is built with a dual-branch structure. The
Slow-Slots branch focuses on extracting object-centric slots from features at
high spatial resolution but low (slow) frame sample rate, emphasizing detailed
object information. Conversely, Fast-Slots branch is engineered to learn
event-centric slots from high temporal sample rate but low spatial resolution
features. These complementary slots are combined to form the vision context,
serving as the input to the LLM for efficient question answering. Our
experimental results demonstrate the effectiveness of our Slot-VLM, which
achieves the state-of-the-art performance on video question-answering.
