TS-VLM: Text-Guided SoftSort Pooling for Vision-Language Models in Multi-View Driving Reasoning

• URL: http://arxiv.org/abs/2505.12670v1
• Date: Mon, 19 May 2025 03:37:15 GMT
• Title: TS-VLM: Text-Guided SoftSort Pooling for Vision-Language Models in Multi-View Driving Reasoning
• Authors: Lihong Chen, Hossein Hassani, Soodeh Nikan,
• Abstract summary: Vision-Language Models (VLMs) have shown potential to enhance scene perception, reasoning, and decision-making.<n>Existing models suffer from computational overhead and inefficient integration of multi-view sensor data.<n>This paper introduces a lightweight VLM called TS-VLM, which incorporates a novel Text-Guided SoftSort Pooling (TGSSP) module.
• Score: 1.4087249809872509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) have shown remarkable potential in advancing autonomous driving by leveraging multi-modal fusion in order to enhance scene perception, reasoning, and decision-making. Despite their potential, existing models suffer from computational overhead and inefficient integration of multi-view sensor data that make them impractical for real-time deployment in safety-critical autonomous driving applications. To address these shortcomings, this paper is devoted to designing a lightweight VLM called TS-VLM, which incorporates a novel Text-Guided SoftSort Pooling (TGSSP) module. By resorting to semantics of the input queries, TGSSP ranks and fuses visual features from multiple views, enabling dynamic and query-aware multi-view aggregation without reliance on costly attention mechanisms. This design ensures the query-adaptive prioritization of semantically related views, which leads to improved contextual accuracy in multi-view reasoning for autonomous driving. Extensive evaluations on the DriveLM benchmark demonstrate that, on the one hand, TS-VLM outperforms state-of-the-art models with a BLEU-4 score of 56.82, METEOR of 41.91, ROUGE-L of 74.64, and CIDEr of 3.39. On the other hand, TS-VLM reduces computational cost by up to 90%, where the smallest version contains only 20.1 million parameters, making it more practical for real-time deployment in autonomous vehicles.

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