HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions

• URL: http://arxiv.org/abs/2409.10641v1
• Date: Mon, 16 Sep 2024 18:15:38 GMT
• Title: HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions
• Authors: Alexandru Bobe, Jan C. van Gemert,
• Abstract summary: This paper presents a novel annotation pipeline that uses pre-extracted features and dimensionality reduction to accelerate the temporal video annotation process. We demonstrate significant improvements in annotation effort compared to traditional linear methods, achieving more than a 10x reduction in clicks required for annotating over 12 hours of video.
• Score: 59.71751978599567
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video annotation is a critical and time-consuming task in computer vision research and applications. This paper presents a novel annotation pipeline that uses pre-extracted features and dimensionality reduction to accelerate the temporal video annotation process. Our approach uses Hierarchical Stochastic Neighbor Embedding (HSNE) to create a multi-scale representation of video features, allowing annotators to efficiently explore and label large video datasets. We demonstrate significant improvements in annotation effort compared to traditional linear methods, achieving more than a 10x reduction in clicks required for annotating over 12 hours of video. Our experiments on multiple datasets show the effectiveness and robustness of our pipeline across various scenarios. Moreover, we investigate the optimal configuration of HSNE parameters for different datasets. Our work provides a promising direction for scaling up video annotation efforts in the era of video understanding.

Related papers

• Scaling Up Video Summarization Pretraining with Large Language Models [73.74662411006426]
  We introduce an automated and scalable pipeline for generating a large-scale video summarization dataset. We analyze the limitations of existing approaches and propose a new video summarization model that effectively addresses them. Our work also presents a new benchmark dataset that contains 1200 long videos each with high-quality summaries annotated by professionals.
  arXiv  Detail & Related papers  (2024-04-04T11:59:06Z)
• TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking [33.75267864844047]
  Video Object (VOS) has emerged as an increasingly important problem with availability of larger datasets and more complex and realistic settings. We propose a novel, clip-based DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges. Specifically, we propose a novel transformation-aware loss that focuses learning on portions of the video where an object undergoes significant deformations.
  arXiv  Detail & Related papers  (2023-12-13T21:02:03Z)
• Video-based Person Re-identification with Long Short-Term Representation Learning [101.62570747820541]
  Video-based person Re-Identification (V-ReID) aims to retrieve specific persons from raw videos captured by non-overlapped cameras. We propose a novel deep learning framework named Long Short-Term Representation Learning (LSTRL) for effective V-ReID.
  arXiv  Detail & Related papers  (2023-08-07T16:22:47Z)
• PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking [90.29143475328506]
  We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. We animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos.
  arXiv  Detail & Related papers  (2023-07-27T17:58:11Z)
• In Defense of Clip-based Video Relation Detection [32.05021939177942]
  Video Visual Relation Detection (VidVRD) aims to detect visual relationship triplets in videos using spatial bounding boxes and temporal boundaries. We propose a Hierarchical Context Model (HCM) that enriches the object-based spatial context and relation-based temporal context based on clips. Our HCM achieves a new state-of-the-art performance, highlighting the effectiveness of incorporating advanced spatial and temporal context modeling within the clip-based paradigm.
  arXiv  Detail & Related papers  (2023-07-18T05:42:01Z)
• Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition [25.888314212797436]
  We propose a novel video frame sampler for few-shot action recognition. Task-specific spatial-temporal frame sampling is achieved via a temporal selector (TS) and a spatial amplifier (SA) Experiments show a significant boost on various benchmarks including long-term videos.
  arXiv  Detail & Related papers  (2022-07-20T09:04:12Z)
• VRAG: Region Attention Graphs for Content-Based Video Retrieval [85.54923500208041]
  Region Attention Graph Networks (VRAG) improves the state-of-the-art video-level methods. VRAG represents videos at a finer granularity via region-level features and encodes video-temporal dynamics through region-level relations. We show that the performance gap between video-level and frame-level methods can be reduced by segmenting videos into shots and using shot embeddings for video retrieval.
  arXiv  Detail & Related papers  (2022-05-18T16:50:45Z)
• Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning [44.412145665354736]
  We introduce a novel contrastive action representation learning framework to learn frame-wise action representations. Inspired by the recent progress of self-supervised learning, we present a novel sequence contrastive loss (SCL) applied on two correlated views. Our approach also shows outstanding performance on video alignment and fine-grained frame retrieval tasks.
  arXiv  Detail & Related papers  (2022-03-28T17:59:54Z)
• t-EVA: Time-Efficient t-SNE Video Annotation [16.02592287695421]
  t-EVA can outperform other video annotation tools while maintaining test accuracy on video classification. We show that t-EVA can outperform other video annotation tools while maintaining test accuracy on video classification.
  arXiv  Detail & Related papers  (2020-11-26T09:56:54Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)

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.