Semantic Grouping Network for Video Captioning

• URL: http://arxiv.org/abs/2102.00831v2
• Date: Wed, 3 Feb 2021 11:53:17 GMT
• Title: Semantic Grouping Network for Video Captioning
• Authors: Hobin Ryu, Sunghun Kang, Haeyong Kang, and Chang D. Yoo
• Abstract summary: The SGN learns an algorithm to capture the most discriminating word phrases of the partially decoded caption. The continuous feedback from decoded words enables the SGN to dynamically update the video representation that adapts to the partially decoded caption. The SGN achieves state-of-the-art performances by outperforming runner-up methods by a margin of 2.1%p and 2.4%p in a CIDEr-D score on MSVD and MSR-VTT datasets.
• Score: 11.777063873936598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) to group video frames with discriminating word phrases of partially decoded caption and then (2) to decode those semantically aligned groups in predicting the next word. As consecutive frames are not likely to provide unique information, prior methods have focused on discarding or merging repetitive information based only on the input video. The SGN learns an algorithm to capture the most discriminating word phrases of the partially decoded caption and a mapping that associates each phrase to the relevant video frames - establishing this mapping allows semantically related frames to be clustered, which reduces redundancy. In contrast to the prior methods, the continuous feedback from decoded words enables the SGN to dynamically update the video representation that adapts to the partially decoded caption. Furthermore, a contrastive attention loss is proposed to facilitate accurate alignment between a word phrase and video frames without manual annotations. The SGN achieves state-of-the-art performances by outperforming runner-up methods by a margin of 2.1%p and 2.4%p in a CIDEr-D score on MSVD and MSR-VTT datasets, respectively. Extensive experiments demonstrate the effectiveness and interpretability of the SGN.

Related papers

• Learning text-to-video retrieval from image captioning [59.81537951811595]
  We describe a protocol to study text-to-video retrieval training with unlabeled videos. We assume (i) no access to labels for any videos, and (ii) access to labeled images in the form of text. We show that automatically labeling video frames with image captioning allows text-to-video retrieval training.
  arXiv  Detail & Related papers  (2024-04-26T15:56:08Z)
• Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrieval [24.691270610091554]
  In this paper, we aim to learn semantically-enhanced representations purely from the video, so that the video representations can be computed offline and reused for different texts. We obtain state-of-the-art performances on three benchmark datasets, i.e., MSR-VTT, MSVD, and LSMDC.
  arXiv  Detail & Related papers  (2023-08-15T08:54:25Z)
• Exploiting Auxiliary Caption for Video Grounding [66.77519356911051]
  Video grounding aims to locate a moment of interest matching a given query sentence from an untrimmed video. Previous works ignore the sparsity dilemma in video annotations, which fails to provide the context information between potential events and query sentences in the dataset. We propose an Auxiliary Caption Network (ACNet) for video grounding. Specifically, we first introduce dense video captioning to generate dense captions and then obtain auxiliary captions by Non-Auxiliary Caption Suppression (NACS) To capture the potential information in auxiliary captions, we propose Caption Guided Attention (CGA) project the semantic relations between auxiliary captions and
  arXiv  Detail & Related papers  (2023-01-15T02:04:02Z)
• RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval [61.77760317554826]
  We propose Redundancy-aware Video-language Pre-training. We design a redundancy measurement of video patches and text tokens by calculating the cross-modal minimum dis-similarity. We evaluate our method on four benchmark datasets, MSRVTT, MSVD, DiDeMo, and LSMDC.
  arXiv  Detail & Related papers  (2022-10-13T10:11:41Z)
• Contrastive Video-Language Learning with Fine-grained Frame Sampling [54.542962813921214]
  FineCo is an approach to better learn video and language representations with a fine-grained contrastive objective operating on video frames. It helps distil a video by selecting the frames that are semantically equivalent to the text, improving cross-modal correspondence.
  arXiv  Detail & Related papers  (2022-10-10T22:48:08Z)
• Controllable Video Captioning with an Exemplar Sentence [89.78812365216983]
  We propose a novel Syntax Modulated Caption Generator (SMCG) incorporated in an encoder-decoder-reconstructor architecture. SMCG takes video semantic representation as an input, and conditionally modulates the gates and cells of long short-term memory network. We conduct experiments by collecting auxiliary exemplar sentences for two public video captioning datasets.
  arXiv  Detail & Related papers  (2021-12-02T09:24:45Z)
• End-to-End Dense Video Captioning with Parallel Decoding [53.34238344647624]
  We propose a simple yet effective framework for end-to-end dense video captioning with parallel decoding (PDVC) PDVC precisely segments the video into a number of event pieces under the holistic understanding of the video content. experiments on ActivityNet Captions and YouCook2 show that PDVC is capable of producing high-quality captioning results.
  arXiv  Detail & Related papers  (2021-08-17T17:39:15Z)
• Domain Adaptive Video Segmentation via Temporal Consistency Regularization [32.77436219094282]
  This paper presents DA-VSN, a domain adaptive video segmentation network that addresses domain gaps in videos by temporal consistency regularization (TCR) The first is cross-domain TCR that guides the prediction of target frames to have similar temporal consistency as that of source frames (learnt from annotated source data) via adversarial learning. The second is intra-domain TCR that guides unconfident predictions of target frames to have similar temporal consistency as confident predictions of target frames.
  arXiv  Detail & Related papers  (2021-07-23T02:50:42Z)

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.