Streaming dense video captioning requires real-time processing of continuous visual input while determining precisely when and what to caption. Current approaches primarily focus on designing complex external memory mechanisms, failing to leverage Large Multimodal Models' (LMMs) inherent long-context capabilities. Moreover, existing methods employing threshold-based caption triggering face a severe Threshold-Gated Discrepancy (TGD) problem, a training-inference mismatch arising from data imbalance, where models predominantly predict silence tokens, requiring thresholds that vary drastically across videos with extremely narrow effective ranges. We introduce Takusen, an asynchronous temporal modeling two-agent framework comprising a Small Multimodal Model (SMM) as an Oracle agent and an LMM as a Listener agent. The Oracle agent processes sparse video inputs at an accelerated rate to detect event boundaries, while the Listener agent processes dense inputs to generate accurate captions when prompted by the Oracle's signals. This architecture eliminates threshold dependencies by fundamentally changing how silence/generation decisions are made, resolving the TGD problem. To enhance robustness against boundary prediction instabilities, we integrate uniformly distributed fixed decoding points with Oracle-predicted boundaries. Experiments on ActivityNet Captions and YouCook2 datasets demonstrate that Takusen achieves state-of-the-art performance with a simpler and more efficient design that balances temporal sensitivity with descriptive accuracy.
尚无 AI 解读，展示原文摘要
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Test-time "thinking" (i.e., generating explicit intermediate reasoning chains) is known to boost performance in large language models and has recently shown strong gains for large vision-language models (LVLMs). However, despite these promising results, there is still no systematic analysis of how thinking actually affects visual reasoning. We provide the first such analysis with a large-scale, controlled comparison of thinking for LVLMs, evaluating 10 variants from the InternVL3.5 and Qwen3-VL families on MMMUval under generous token budgets and multi-pass decoding. We show that more thinking is not always better: long chains often yield long-wrong trajectories that ignore the image and underperform the same models run in standard instruct mode. A deeper analysis reveals that certain short "lookback" phrases, which explicitly refer back to the image, are strongly enriched in successful trajectories and correlate with better visual grounding. Building on this insight, we propose uncertainty-guided lookback, a training-free decoding strategy that combines an uncertainty signal with adaptive lookback prompts and breadth search. Our method improves overall MMMU performance, delivers the largest gains in categories where standard thinking is weak, and outperforms several strong decoding baselines, setting a new state of the art under fixed model families and token budgets. We further show that this decoding strategy generalizes, yielding consistent improvements on five additional benchmarks, including two broad multimodal suites and math-focused visual reasoning datasets.
尚无 AI 解读，展示原文摘要
cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表