MemoryCard is a video-memory-based augmentation framework designed to improve long-video question answering for Vision‑Language Models. It segments lengthy videos into semantically coherent units—each representing a distinct topic or event—by performing a self‑reading process over the video and aligned utterances. For each unit, the framework generates an event‑level video gist and selects representative visual moments, which are compiled into unified Memory Cards that are used for retrieval and answering questions, yielding up to a 21.8% relative accuracy improvement under comparable visual‑token budgets.
arXiv:2610.08674v1 Announce Type: new Abstract: Current large video-language models (LVLMs) still face challenges when dealing with long videos, mainly because frames are often processed independentl...
arXiv:2607. 24794v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding.
arXiv:2609.23601v1 Announce Type: new Abstract: Long-video understanding must capture transient visual evidence under strict token budgets, yet existing methods compress frames, append memory tokens,...
arXiv:2604.17422v2 Announce Type: replace Abstract: Long video understanding remains a formidable challenge for Multimodal Large Language Models (MLLMs) due to the prohibitive cost of processing dens...
FocusGraph is a framework for selecting keyframes in egocentric long‑video question answering. It uses a lightweight Scene‑Graph LLM Selector to identify query‑relevant clips from graph‑based captions, then extracts keyframes with Patch‑wise Sparse‑Flow Retention (PSFR) before feeding them to a multimodal large language model for answer generation. The method achieves state‑of‑the‑art performance on FindingDory and HourVideo while reducing question‑time inference cost.
arXiv:2610.00757v1 Announce Type: new Abstract: Long-video question answering is limited by the high cost of visual tokens and by the fixed context width of current VLMs. A long-video question may re...
arXiv:2608.30294v1 Announce Type: new Abstract: Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to co...
arXiv:2609.16722v1 Announce Type: new Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form video understanding is bottlenecked by the explosion of visual tokens, which saturates co...
Event-Causal RAG (EC‑RAG) is a lightweight retrieval‑augmented framework designed for reasoning over ultra‑long and streaming videos. It segments video streams into semantically complete events using a dual visual‑audio sentinel mechanism, representing each event as a State‑Event‑State (SES) structure that captures pre‑event, event, and post‑event states. During question answering, bidirectional graph retrieval accesses relevant predecessor and successor events from a dual vector‑graph memory, and answers are generated using both this structured memory and the corresponding video evidence. The authors also introduce ECV‑1H, an hour‑scale long‑video QA benchmark with over 150 hours of untrimmed video and 1,251 human‑annotated QA pairs, where EC‑RAG achieves significant accuracy gains across multiple video foundation models while maintaining efficient streaming memory usage on a single RTX 5090 GPU.
arXiv:2609.38900v1 Announce Type: new Abstract: Streaming video understanding requires models to process unbounded visual streams while preserving rich visual semantics across vast temporal horizons,...
The paper introduces Caption‑once, Frames‑on‑Demand (CFD), a budget‑aware edge‑cloud framework for long‑video understanding. CFD first runs a single offline captioning pass on the edge to build a dual‑track narrative index—an event‑level story skeleton and a clip‑level micro‑log—that is cached for future queries. At query time, a cloud‑side MLLM uses a Visual‑Need Router to decide whether to retrieve keyframes for perceptual questions, thereby limiting visual processing while preserving temporal structure in language space.
arXiv:2609.00551v1 Announce Type: cross Abstract: Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, s...