MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding

• URL: http://arxiv.org/abs/2306.04593v1
• Date: Wed, 7 Jun 2023 16:46:44 GMT
• Title: MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding
• Authors: Tan-Sang Ha, Hai Nguyen-Truong, Tuan-Anh Vu, Sai-Kit Yeung
• Abstract summary: MarineVRS is a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species.
• Score: 11.878077736295863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries. To address these challenges, we present MarineVRS, a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. In addition, unlike the conventional video retrieval system, which only permits users to index a collection of images or videos and search using a free-form natural language sentence, our retrieval system includes an additional Explainability module that outputs the segmentation masks of the objects that the input query referred to. This feature allows users to identify and isolate specific objects in the video footage, leading to more detailed analysis and understanding of their behavior and movements. Finally, with its adaptability, explainability, accuracy, and scalability, MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species.

Related papers

• VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs [64.60035916955837]
  VANE-Bench is a benchmark designed to assess the proficiency of Video-LMMs in detecting anomalies and inconsistencies in videos. Our dataset comprises an array of videos synthetically generated using existing state-of-the-art text-to-video generation models. We evaluate nine existing Video-LMMs, both open and closed sources, on this benchmarking task and find that most of the models encounter difficulties in effectively identifying the subtle anomalies.
  arXiv  Detail & Related papers  (2024-06-14T17:59:01Z)
• DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries [60.09774333024783]
  We introduce Dynamic Anchor Queries (DAQ) to shorten the transition gap between the anchor and target queries. We also introduce a query-level object Emergence and Disappearance Simulation (EDS) strategy, which unleashes DAQ's potential without any additional cost. Experiments demonstrate that DVIS-DAQ achieves a new state-of-the-art (SOTA) performance on five mainstream video segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-03-29T17:58:50Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles [3.2716252389196288]
  This paper outlines our approach to detect dynamic scene changes in Unmanned Surface Vehicles (USVs) Our objective is to identify significant changes in the dynamic scenes of maritime video data, particularly those scenes that exhibit a high degree of resemblance. In our system for dynamic scene change detection, we propose completely unsupervised learning method.
  arXiv  Detail & Related papers  (2023-11-20T07:34:01Z)
• Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways [12.7548343467665]
  The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. We propose a deep learning-enabled asynchronous trajectory matching method (named DeepSORVF) to fuse the AIS-based vessel information with the corresponding visual targets. In addition, by combining the AIS- and video-based movement features, we also present a prior knowledge-driven anti-occlusion method to yield accurate and robust vessel tracking results.
  arXiv  Detail & Related papers  (2023-02-22T11:00:34Z)
• Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval [10.526705651297146]
  In this paper, we focus on single-shot videos taken from moving cameras in underwater environments. The first shard of a new Marine Video Kit is presented to serve for video retrieval and other computer vision challenges.
  arXiv  Detail & Related papers  (2022-09-23T10:57:50Z)
• Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge [133.80567761430584]
  We collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario. OVIS consists of 296k high-quality instance masks and 901 occluded scenes. All baseline methods encounter a significant performance degradation of about 80% in the heavily occluded object group.
  arXiv  Detail & Related papers  (2021-11-15T17:59:03Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• FathomNet: A global underwater image training set for enabling artificial intelligence in the ocean [0.0]
  Ocean-going platforms are integrating high-resolution camera feeds for observation and navigation, producing a deluge of visual data. Recent advances in machine learning enable fast, sophisticated analysis of visual data, but have had limited success in the oceanographic world. We will demonstrate how machine learning models trained on FathomNet data can be applied across different institutional video data.
  arXiv  Detail & Related papers  (2021-09-29T18:08:42Z)
• Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis [1.6181085766811525]
  We propose a multimodal structure to obtain state-of-the-art performance in this field. We select several benchmarks for multimodal video understanding and apply the most suitable model to find the best performance.
  arXiv  Detail & Related papers  (2021-05-28T08:39:19Z)
• Movement Tracks for the Automatic Detection of Fish Behavior in Videos [63.85815474157357]
  We offer a dataset of sablefish (Anoplopoma fimbria) startle behaviors in underwater videos, and investigate the use of deep learning (DL) methods for behavior detection on it. Our proposed detection system identifies fish instances using DL-based frameworks, determines trajectory tracks, derives novel behavior-specific features, and employs Long Short-Term Memory (LSTM) networks to identify startle behavior in sablefish.
  arXiv  Detail & Related papers  (2020-11-28T05:51:19Z)

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.