SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval
  and Routing in Long-Form Video Analysis
Authors: name: Junho Kim, name: Hyunjun Kim, name: Hosu Lee, name: Yong Man Ro

Despite advances in Large Multi-modal Models, applying them to long and
untrimmed video content remains challenging due to limitations in context
length and substantial memory overhead. These constraints often lead to
significant information loss and reduced relevance in the model responses. With
the exponential growth of video data across web platforms, understanding
long-form video is crucial for advancing generalized intelligence. In this
paper, we introduce SALOVA: Segment-Augmented LOng Video Assistant, a novel
video-LLM framework designed to enhance the comprehension of lengthy video
content through targeted retrieval process. We address two main challenges to
achieve it: (i) We present the SceneWalk dataset, a high-quality collection of
87.8K long videos, each densely captioned at the segment level to enable models
to capture scene continuity and maintain rich descriptive context. (ii) We
develop robust architectural designs integrating dynamic routing mechanism and
spatio-temporal projector to efficiently retrieve and process relevant video
segments based on user queries. Our framework mitigates the limitations of
current video-LMMs by allowing for precise identification and retrieval of
relevant video segments in response to queries, thereby improving the
contextual relevance of the generated responses. Through extensive experiments,
SALOVA demonstrates enhanced capability in processing complex long-form videos,
showing significant capability to maintain contextual integrity across extended
sequences.