# PerspectiveNet: Multi-View Perception for Dynamic Scene Understanding

arXiv: https://arxiv.org/abs/2410.16824 · HTML: https://mlsanity.com/paper/2410.16824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections](https://mlsanity.com/paper/2205.12005/) (2022)
- [CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario](https://mlsanity.com/paper/2405.03194/) (2024)
- [WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding](https://mlsanity.com/paper/2407.15350/) (2024)
- [Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis](https://mlsanity.com/paper/2404.08229/) (2024)

## Abstract (arXiv)

Generating detailed descriptions from multiple cameras and viewpoints is challenging due to the complex and inconsistent nature of visual data. In this paper, we introduce PerspectiveNet, a lightweight yet efficient model for generating long descriptions across multiple camera views. Our approach utilizes a vision encoder, a compact connector module to convert visual features into a fixed-size tensor, and large language models (LLMs) to harness the strong natural language generation capabilities of LLMs. The connector module is designed with three main goals: mapping visual features onto LLM embeddings, emphasizing key information needed for description generation, and producing a fixed-size feature matrix. Additionally, we augment our solution with a secondary task, the correct frame sequence detection, enabling the model to search for the correct sequence of frames to generate descriptions. Finally, we integrate the connector module, the secondary task, the LLM, and a visual feature extraction model into a single architecture, which is trained for the Traffic Safety Description and Analysis task. This task requires generating detailed, fine-grained descriptions of events from multiple cameras and viewpoints. The resulting model is lightweight, ensuring efficient training and inference, while remaining highly effective.