arXiv Computer Vision
Learning Compositional Spatio-Temporal Video Grounding with Synthetic Curriculum
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Aug 25
OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation
arXiv:2608.14835v2 Announce Type: replace Abstract: Dynamic scene graphs (DSGs) capture spatio-temporal interactions across videos as $\langle$subject, predicate, object$\rangle$ triplets, and underp...
arXiv Computation and Language
Sep 25
STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models
STRAND is a new benchmark that tests multimodal large language models’ ability to track objects, their states, and relationships over time in videos. It evaluates intermediate reasoning by breaking queries into sub‑questions and uses Faithful Accuracy to ensure all parts of an answer are correct. The authors also propose an object‑centric framework that builds structured trajectories and shows reduced hallucinations and better temporal consistency compared to existing models.
arXiv Machine Learning
Jul 7
Incentivizing Vision Language Models to Search for Long Video Question Answering
arXiv:2607. 02959v1 Announce Type: cross Abstract: We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process.
arXiv AI
Aug 5
CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning
arXiv:2608. 02833v1 Announce Type: cross Abstract: Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate visual grounding and coherent reasoning chains.
arXiv AI
5d ago
EventLens: Event-Structure Reinforcement Learning for Video Understanding
arXiv:2610.16172v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure...
arXiv AI
Jun 12
ReFoCUS: Reinforcement-guided Frame Optimization for Contextual Understanding
arXiv:2506. 01274v2 Announce Type: replace-cross Abstract: Recent progress in Large Multi-modal Models (LMMs) has enabled effective vision-language reasoning, yet the ability to video understanding remains constrained by suboptimal frame selection strategies, albeit with the rapid development of video-specialized LMMs.