Flat Multi-modal Interaction Transformer for Named Entity Recognition

• URL: http://arxiv.org/abs/2208.11039v1
• Date: Tue, 23 Aug 2022 15:25:44 GMT
• Title: Flat Multi-modal Interaction Transformer for Named Entity Recognition
• Authors: Junyu Lu, Dixiang Zhang, Pingjian Zhang
• Abstract summary: Multi-modal named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images. We propose a Flat Multi-modal Interaction Transformer (FMIT) for MNER. We transform the fine-grained semantic representation of the vision and text into a unified lattice structure and design a novel relative position encoding to match different modalities in Transformer.
• Score: 1.7605709999848573
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-modal named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images. However, in dominant MNER approaches, the interaction of different modalities is usually carried out through the alternation of self-attention and cross-attention or over-reliance on the gating machine, which results in imprecise and biased correspondence between fine-grained semantic units of text and image. To address this issue, we propose a Flat Multi-modal Interaction Transformer (FMIT) for MNER. Specifically, we first utilize noun phrases in sentences and general domain words to obtain visual cues. Then, we transform the fine-grained semantic representation of the vision and text into a unified lattice structure and design a novel relative position encoding to match different modalities in Transformer. Meanwhile, we propose to leverage entity boundary detection as an auxiliary task to alleviate visual bias. Experiments show that our methods achieve the new state-of-the-art performance on two benchmark datasets.

Related papers

• Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation [109.1912721224697]
  We present the Unified Frequency-Assisted transFormer framework, named UFAFormer, to address the DGM4 problem. By leveraging the discrete wavelet transform, we decompose images into several frequency sub-bands, capturing rich face forgery artifacts. Our proposed frequency encoder, incorporating intra-band and inter-band self-attentions, explicitly aggregates forgery features within and across diverse sub-bands.
  arXiv  Detail & Related papers  (2023-09-18T11:06:42Z)
• FER-former: Multi-modal Transformer for Facial Expression Recognition [14.219492977523682]
  A novel multifarious supervision-steering Transformer for Facial Expression Recognition is proposed in this paper. Our approach features multi-granularity embedding integration, hybrid self-attention scheme, and heterogeneous domain-steering supervision. Experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-arts.
  arXiv  Detail & Related papers  (2023-03-23T02:29:53Z)
• Learning to Model Multimodal Semantic Alignment for Story Visualization [58.16484259508973]
  Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story. Current works face the problem of semantic misalignment because of their fixed architecture and diversity of input modalities. We explore the semantic alignment between text and image representations by learning to match their semantic levels in the GAN-based generative model.
  arXiv  Detail & Related papers  (2022-11-14T11:41:44Z)
• Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media [11.235498285650142]
  Named Entity Recognition (NER) on social media refers to discovering and classifying entities from unstructured free-form content. This work introduces the multi-granularity cross-modality representation learning. Experiments show that our proposed approach can achieve the SOTA or approximate SOTA performance on two benchmark datasets of tweets.
  arXiv  Detail & Related papers  (2022-10-19T15:14:55Z)
• Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval [8.855547063009828]
  We propose a Cross-modal Semantic Enhanced Interaction method, termed CMSEI for image-sentence retrieval. We first design the intra- and inter-modal spatial and semantic graphs based reasoning to enhance the semantic representations of objects. To correlate the context of objects with the textual context, we further refine the visual semantic representation via the cross-level object-sentence and word-image based interactive attention.
  arXiv  Detail & Related papers  (2022-10-17T10:01:16Z)
• Hierarchical Local-Global Transformer for Temporal Sentence Grounding [58.247592985849124]
  This paper studies the multimedia problem of temporal sentence grounding. It aims to accurately determine the specific video segment in an untrimmed video according to a given sentence query.
  arXiv  Detail & Related papers  (2022-08-31T14:16:56Z)
• ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition [38.08486689940946]
  Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. It is difficult to model such interactions as image and text representations are trained separately on the data of their respective modality. In this paper, we propose bf Image-bf text bf Alignments (ITA) to align image features into the textual space.
  arXiv  Detail & Related papers  (2021-12-13T08:29:43Z)
• Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentation [87.01669173673288]
  We propose an encoder fusion network (EFN), which transforms the visual encoder into a multi-modal feature learning network. A co-attention mechanism is embedded in the EFN to realize the parallel update of multi-modal features. The experiment results on four benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-05-05T02:27:25Z)
• Enhanced Modality Transition for Image Captioning [51.72997126838352]
  We build a Modality Transition Module (MTM) to transfer visual features into semantic representations before forwarding them to the language model. During the training phase, the modality transition network is optimised by the proposed modality loss. Experiments have been conducted on the MS-COCO dataset demonstrating the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2021-02-23T07:20:12Z)
• Referring Image Segmentation via Cross-Modal Progressive Comprehension [94.70482302324704]
  Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities. We propose a Cross-Modal Progressive (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task.
  arXiv  Detail & Related papers  (2020-10-01T16:02:30Z)

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.