arXiv Computer Vision
Efficient Language-to-Vision Feature Injection for Referring Single-Object Tracking
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Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking
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Referring Multi-Object Tracking with Diverse Dynamic Expressions
The paper introduces Referring Multi-Object Tracking (RMOT), a task that uses natural language expressions to guide the prediction of multiple objects over time, addressing limitations in previous work that lacked dynamic language expressiveness. It presents the Refer-KITTI-V2 benchmark, which includes 9,758 semi-automatically generated language prompts for diverse object dynamics. The authors also propose TempRMOT, a Transformer-based framework with a Temporal Enhancement Module that achieves state‑of‑the‑art results on both Refer‑KITTI and Refer‑KITTI‑V2.
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An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation
arXiv:2606. 00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains underexplored.
arXiv Computer Vision
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Motion-Aware Vision-Reference Alignment for Referring Multi-Object Tracking
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CaptionFormer: Unified Segmentation, Tracking, and Captioning for Spatio-Temporal Objects
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arXiv AI
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Steerable Visual Representations
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