arXiv Computer Vision
RTAformer: Relation-Transition-Aware Transformer for Video Scene Graph Generation
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arXiv Computer Vision
1d ago
A Closer Look at Dynamic Scene Graph Generation In the Era of Multimodal Large Language Models
arXiv:2503.15846v4 Announce Type: replace Abstract: Dynamic Scene Graph Generation (DSGG) aims to capture objects and their evolving relations in videos. Despite recent progress, the practicality and...
arXiv AI
Oct 15
SceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning
arXiv:2610.14439v1 Announce Type: cross Abstract: Large multimodal models (LMMs) need to understand how entities move and interact as a scene develops. This requires following the same entities over...
arXiv Computer Vision
Sep 2
CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modeling
CameraEditor is a new framework that transforms camera-controlled image editing into a temporal sequence prediction problem. By using video diffusion models, it incorporates a geometric perception module and dynamic reference routing to create precise visual references through dynamic panorama cropping. The method also inserts intermediate transition frames to handle large perspective shifts, maintaining content identity and spatial coherence, and is evaluated on a dataset of 5,760 instances with a benchmark of 462 test cases, achieving state‑of‑the‑art performance.
arXiv Machine Learning
4d ago
GROME: GROunding Factorized MEmory into Video World Models
GROME introduces a factorized memory framework for video world models that separates historical evidence into static and dynamic components. It uses local memory retrieval to align static history spatially and injects the factorized memory into models without extra training. Experiments show improved long‑horizon scene persistence and dynamic entity fidelity while maintaining visual quality and controllability.
arXiv AI
Aug 20
Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios
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 AI
Jun 9
Video Understanding by Design: How Datasets Shape Video Models
arXiv:2509. 09151v2 Announce Type: replace-cross Abstract: Research in video understanding has advanced rapidly, driven by increasingly diverse datasets and more powerful model architectures.