Despite the significant advancements of Large Vision-Language Models (LVLMs) on established benchmarks, there remains a notable gap in suitable evaluation regarding their applicability in the emerging domain of long-context streaming video understanding. Current benchmarks for video understanding typically emphasize isolated single-instance text inputs and fail to evaluate the capacity to sustain temporal reasoning throughout the entire duration of video streams. To address these limitations, we introduce SVBench, a pioneering benchmark with temporal multi-turn question-answering chains specifically designed to thoroughly assess the capabilities of streaming video understanding of current LVLMs. We design a semi-automated annotation pipeline to obtain 49,979 Question-Answer (QA) pairs of 1,353 streaming videos, which includes generating QA chains that represent a series of consecutive multi-turn dialogues over video segments and constructing temporal linkages between successive QA chains. Our experimental results, obtained from 14 models in dialogue and streaming evaluations, reveal that while the closed-source GPT-4o outperforms others, most open-source LVLMs struggle with long-context streaming video understanding. We also construct a StreamingChat model, which significantly outperforms open-source LVLMs on our SVBench and achieves comparable performance on diverse vision-language benchmarks. We expect SVBench to advance the research of streaming video understanding by providing a comprehensive and in-depth analysis of current LVLMs. Our benchmark and model can be accessed at https://yzy-bupt.github.io/SVBench.

Panoptic Scene Graph Generation (PSG) presents pixel-wise instance detection and localization, leading to comprehensive and precise scene graphs. Current methods employ conventional Scene Graph Generation (SGG) frameworks to solve the PSG problem, neglecting the fundamental differences between bounding boxes and masks, i.e., bounding boxes are allowed overlap but masks are not. Since segmentation from the panoptic head has deviations, non-overlapping masks may not afford complete instance information. Subsequently, in the training phase, incomplete segmented instances may not be well-aligned to annotated ones, causing mismatched relations and insufficient training. During the inference phase, incomplete segmentation leads to incomplete scene graph prediction. To alleviate these problems, we construct a novel two-stage framework for the PSG problem. In the training phase, we design a proposal matching strategy, which replaces deterministic segmentation results with proposals extracted from the off-the-shelf panoptic head for label alignment, thereby ensuring the all-matching of training samples. In the inference phase, we present an innovative concept of employing relation predictions to constrain segmentation and design a relation-constrained segmentation algorithm. By reconstructing the process of generating segmentation results from proposals using predicted relation results, the algorithm recovers more valid instances and predicts more complete scene graphs. The experimental results show overall superiority, effectiveness, and robustness against adversarial attacks.