arXiv Computer Vision
VANTAGE-Bench: Evaluating the Infrastructure AI Gap in Vision-Language Models
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arXiv AI
Jul 3
AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models
arXiv:2607. 02269v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG).
arXiv Computer Vision
Sep 4
ORMOT: A Dataset and Framework for Omnidirectional Referring Multi-Object Tracking
The paper introduces ORMOT, a new task that extends Referring Multi‑Object Tracking to omnidirectional 360° imagery, ensuring full scene context for language‑guided tracking. It presents ORSet, a dataset of 27 omnidirectional scenes with 848 language descriptions and 3,401 annotated objects, and introduces ORTrack, an LVLM‑driven framework that performs zero‑shot detection and robust cross‑frame association. Experiments on ORSet show that ORTrack achieves state‑of‑the‑art performance, establishing a strong baseline for future research.
arXiv Computer Vision
Sep 3
TempoGround: State-Aware Streaming Visual Grounding with Vision-Language Models
TempoGround is a vision‑language model–native framework for streaming visual grounding that detects cross‑frame object correspondence and explicitly models object presence states. It uses a curriculum prediction mechanism to resolve 2D instance association, predict object entry, continuation, or exit, decode 2D boxes, and lift them to 3D camera‑frame boxes. The approach is further refined with Streaming Grounding Reinforcement, which optimizes grounding, identity, and consistency rewards, and achieves significant improvements on multiple streaming visual grounding benchmarks.
arXiv AI
Jun 2
From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model
arXiv:2512. 05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.
arXiv AI
Aug 12
PinpointQA: A Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos
arXiv:2604. 08991v3 Announce Type: replace-cross Abstract: Reliable embodied interaction in indoor environments requires agents to precisely localize small everyday objects from visual observations.
arXiv AI
Jun 4
From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models
arXiv:2512. 05277v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.