arXiv AI
Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches
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arXiv AI
Aug 28
Temporally-Grounded Language Generation: Towards Real-Time Vision-Language Models
The paper introduces Temporally-Grounded Language Generation (TGLG), a benchmark that tests vision‑language models on their ability to produce semantically accurate and temporally precise utterances in real‑time settings. It identifies perceptual updating and contingency awareness as key capabilities, curates datasets from sports broadcasting and egocentric interactions, and proposes the TRACE metric to jointly evaluate semantic similarity and temporal alignment. The authors also present VLM‑TSI, a model that interleaves visual and linguistic tokens in a time‑synchronized manner, achieving better performance than a strong baseline yet still showing modest overall results, underscoring the challenge of real‑time VLMs.
arXiv AI
Aug 17
Content Based Video Narration of Gameplay with Vision Language Models
arXiv:2608. 14016v1 Announce Type: cross Abstract: Live game commentary is scarce: it exists for professional esports broadcasts and almost nowhere else.
arXiv Computer Vision
Sep 23
NBA_Streaming: A Large-Scale Benchmark for Fine-Grained Basketball Commentary Generation in Continuous Streams
arXiv:2608.09200v3 Announce Type: replace Abstract: Live basketball commentary generation requires determining when an event is sufficiently observable and describing it before subsequent events unfo...
arXiv Computation and Language
Aug 21
StreamSoccer: Event-Driven Memory for Streaming Soccer Commentary
arXiv:2608. 19723v1 Announce Type: cross Abstract: Streaming video understanding requires models to causally update state as video arrives and organize growing history into semantic units that can evolve, persist, and be recalled under bounded computation and memory.
arXiv AI
Jul 20
LVSum: A Benchmark for Timestamp-Aware Long Video Summarization
arXiv:2604. 10024v2 Announce Type: replace-cross Abstract: Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded.
arXiv Computer Vision
Sep 28
Where to Focus: Query-Modulated Multimodal Keyframe Selection for Long Video Understanding
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...