EVC-MF: End-to-end Video Captioning Network with Multi-scale Features

• URL: http://arxiv.org/abs/2410.16624v1
• Date: Tue, 22 Oct 2024 02:16:02 GMT
• Title: EVC-MF: End-to-end Video Captioning Network with Multi-scale Features
• Authors: Tian-Zi Niu, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu,
• Abstract summary: We propose an end-to-end encoder-decoder-based network (EVC-MF) for video captioning. It efficiently utilizes multi-scale visual and textual features to generate video descriptions. The results demonstrate that EVC-MF yields competitive performance compared with the state-of-theart methods.
• Score: 13.85795110061781
• License:
• Abstract: Conventional approaches for video captioning leverage a variety of offline-extracted features to generate captions. Despite the availability of various offline-feature-extractors that offer diverse information from different perspectives, they have several limitations due to fixed parameters. Concretely, these extractors are solely pre-trained on image/video comprehension tasks, making them less adaptable to video caption datasets. Additionally, most of these extractors only capture features prior to the classifier of the pre-training task, ignoring a significant amount of valuable shallow information. Furthermore, employing multiple offline-features may introduce redundant information. To address these issues, we propose an end-to-end encoder-decoder-based network (EVC-MF) for video captioning, which efficiently utilizes multi-scale visual and textual features to generate video descriptions. Specifically, EVC-MF consists of three modules. Firstly, instead of relying on multiple feature extractors, we directly feed video frames into a transformer-based network to obtain multi-scale visual features and update feature extractor parameters. Secondly, we fuse the multi-scale features and input them into a masked encoder to reduce redundancy and encourage learning useful features. Finally, we utilize an enhanced transformer-based decoder, which can efficiently leverage shallow textual information, to generate video descriptions. To evaluate our proposed model, we conduct extensive experiments on benchmark datasets. The results demonstrate that EVC-MF yields competitive performance compared with the state-of-theart methods.

Related papers

• VIMI: Grounding Video Generation through Multi-modal Instruction [89.90065445082442]
  Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. We construct a large-scale multimodal prompt dataset by employing retrieval methods to pair in-context examples with the given text prompts. We finetune the model from the first stage on three video generation tasks, incorporating multi-modal instructions.
  arXiv  Detail & Related papers  (2024-07-08T18:12:49Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• 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 Infringement Detection via Feature Disentanglement and Mutual Information Maximization [51.206398602941405]
  We propose to disentangle an original high-dimensional feature into multiple sub-features. On top of the disentangled sub-features, we learn an auxiliary feature to enhance the sub-features. Our method achieves 90.1% TOP-100 mAP on the large-scale SVD dataset and also sets the new state-of-the-art on the VCSL benchmark dataset.
  arXiv  Detail & Related papers  (2023-09-13T10:53:12Z)
• 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)
• VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending [78.1399386935455]
  Large-scale image-text contrastive pre-training models, such as CLIP, have been demonstrated to effectively learn high-quality multimodal representations. We propose a novel video-text pre-training method dubbed VLAB: Video Language pre-training by feature generativearity and Blending. VLAB transfers CLIP representations to video pre-training tasks and develops unified video multimodal models for a wide range of video-text tasks.
  arXiv  Detail & Related papers  (2023-05-22T15:54:22Z)
• MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer [12.544216587327387]
  We present an end-to-end trainable unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in video. The presented Multiscale-Decoder Video (MED-VT) uses multiscale representation throughout and employs an optional input beyond video. We present a transductive learning scheme through many-to-many label propagation to provide temporally consistent video predictions.
  arXiv  Detail & Related papers  (2023-04-12T15:50:19Z)
• Multimodal Frame-Scoring Transformer for Video Summarization [4.266320191208304]
  Multimodal Frame-Scoring Transformer (MFST) framework exploiting visual, text and audio features and scoring a video with respect to frames. MFST framework first extracts each modality features (visual-text-audio) using pretrained encoders. MFST trains the multimodal frame-scoring transformer that uses video-text-audio representations as inputs and predicts frame-level scores.
  arXiv  Detail & Related papers  (2022-07-05T05:14:15Z)
• Variational Stacked Local Attention Networks for Diverse Video Captioning [2.492343817244558]
  Variational Stacked Local Attention Network exploits low-rank bilinear pooling for self-attentive feature interaction. We evaluate VSLAN on MSVD and MSR-VTT datasets in terms of syntax and diversity.
  arXiv  Detail & Related papers  (2022-01-04T05:14:34Z)
• Self-Supervised MultiModal Versatile Networks [76.19886740072808]
  We learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. We demonstrate how such networks trained on large collections of unlabelled video data can be applied on video, video-text, image and audio tasks.
  arXiv  Detail & Related papers  (2020-06-29T17:50:23Z)

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.