Year: 2025
Type: conference-paper
Abstract: Language has become a prominent modality in computer vision with the rise of LLMs.Despite supporting long context-lengths, their effectiveness in handling long-term information gradually declines with input length.This becomes critical, especially in applications such as longform video understanding.In this paper, we introduce a Language Repository (LangRepo) for LLMs, that maintains concise and structured information as an interpretable (i.e., alltextual) representation.Our repository is updated iteratively based on multi-scale video chunks.We introduce write and read operations that focus on pruning redundancies in text, and extracting information at various temporal scales.The proposed framework is evaluated on zero-shot visual question-answering benchmarks, showing state-of-the-art performance at its scale.Our code is available at github.com/kkahatapitiya/LangRepo.
Institution: Stony Brook University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G4545485320
OpenAlex ID: W4412888622