PerspectiveNet: Multi-View Perception for Dynamic Scene Understanding

• URL: http://arxiv.org/abs/2410.16824v1
• Date: Tue, 22 Oct 2024 08:57:17 GMT
• Title: PerspectiveNet: Multi-View Perception for Dynamic Scene Understanding
• Authors: Vinh Nguyen,
• Abstract summary: PerspectiveNet is a lightweight model for generating long descriptions across multiple camera views. Our approach utilizes a vision encoder, a compact connector module, and large language models. The resulting model is lightweight, ensuring efficient training and inference, while remaining highly effective.
• Score: 1.2781698000674653
• License:
• Abstract: 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.

Related papers

• VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos [58.765796160750504]
  VideoGLaMM is a new model for fine-grained pixel-level grounding in videos based on user-provided textual inputs. The architecture is trained to synchronize both spatial and temporal elements of video content with textual instructions. Experimental results show that our model consistently outperforms existing approaches across all three tasks.
  arXiv  Detail & Related papers  (2024-11-07T17:59:27Z)
• MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding [39.68348330596116]
  We propose modelname, a simple yet effective multi-layer feature fuser that efficiently integrates deep and shallow features from Vision Transformers (ViTs) Specifically, it leverages semantically aligned deep features as queries to dynamically extract missing details from shallow features. modelnameachieves significant improvements in visual representation and benchmark performance.
  arXiv  Detail & Related papers  (2024-10-15T17:55:22Z)
• Response Wide Shut: Surprising Observations in Basic Vision Language Model Capabilities [30.176918208200604]
  Vision-Language Models (VLMs) have emerged as general purpose tools for addressing a variety of complex computer vision problems. These models have been shown to be highly capable, but also lacking some basic visual understanding skills. This paper sets out to understand the limitations of SoTA VLMs on fundamental visual tasks.
  arXiv  Detail & Related papers  (2024-08-13T08:26:32Z)
• DocKylin: A Large Multimodal Model for Visual Document Understanding with Efficient Visual Slimming [33.40963475653868]
  DocKylin is a document-centric MLLM that performs visual content slimming at both the pixel and token levels. We introduce an Adaptive Pixel Slimming (APS) preprocessing module to perform pixel-level slimming. We also propose a novel Dynamic Token Slimming (DTS) module to conduct token-level slimming.
  arXiv  Detail & Related papers  (2024-06-27T11:28:36Z)
• HRVDA: High-Resolution Visual Document Assistant [32.51417315241559]
  We propose a High-Resolution Visual Document Assistant (HRVDA) to bridge the gap between MLLMs and visual document understanding. HRVDA employs a content filtering mechanism and an instruction filtering module to filter out the content-agnostic visual tokens and instruction-agnostic visual tokens. Our model achieves state-of-the-art performance across multiple document understanding datasets.
  arXiv  Detail & Related papers  (2024-04-10T11:10:50Z)
• Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models [81.71651422951074]
  Chain-of-Spot (CoS) method is a novel approach that enhances feature extraction by focusing on key regions of interest. This technique allows LVLMs to access more detailed visual information without altering the original image resolution. Our empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content.
  arXiv  Detail & Related papers  (2024-03-19T17:59:52Z)
• Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model [83.85856356798531]
  VistaLLM is a visual system that addresses coarse- and fine-grained vision-language tasks. It employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences. We also introduce a novel task, AttCoSeg, which boosts the model's reasoning and grounding capability over multiple input images.
  arXiv  Detail & Related papers  (2023-12-19T18:53:01Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• Dense Video Object Captioning from Disjoint Supervision [77.47084982558101]
  We propose a new task and model for dense video object captioning. This task unifies spatial and temporal localization in video. We show how our model improves upon a number of strong baselines for this new task.
  arXiv  Detail & Related papers  (2023-06-20T17:57:23Z)
• MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks [59.09343552273045]
  We propose a decoder-only model for multimodal tasks, which is surprisingly effective in jointly learning of these disparate vision-language tasks. We demonstrate that joint learning of these diverse objectives is simple, effective, and maximizes the weight-sharing of the model across these tasks. Our model achieves the state of the art on image-text and text-image retrieval, video question answering and open-vocabulary detection tasks, outperforming much larger and more extensively trained foundational models.
  arXiv  Detail & Related papers  (2023-03-29T16:42:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.