arXiv Computer Vision
SpaTime: Streaming Vision-Language Models for Spatio-temporal Reasoning
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arXiv Computer Vision
Sep 1
Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting
The paper introduces structured video prompting, a training‑free inference‑time technique that augments input videos with lightweight spatial and temporal structure to provide explicit anchors for evidence organization. By applying this method to two video benchmarks and two open video‑language models, the authors demonstrate performance improvements across several tasks, with gains varying by model and task. The study suggests that failures in video‑language models stem not only from reasoning capacity but also from how video evidence is presented during inference.
arXiv AI
Jun 30
Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task
arXiv:2512. 10359v1 Announce Type: cross Abstract: Video Question Answering (VideoQA) task serves as a critical playground for evaluating whether foundation models can effectively perceive, understand, and reason about dynamic real-world scenarios.
arXiv Machine Learning
Sep 10
Contextual Observer Grounding: Evaluating Situated Spatial Reasoning in Vision-Language Models
arXiv:2609.06880v1 Announce Type: cross Abstract: Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated pers...
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 AI
Aug 18
Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models
arXiv:2605. 08974v2 Announce Type: replace-cross Abstract: While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes.
arXiv Machine Learning
Aug 4
SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
arXiv:2608. 01899v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning.