Improving vision-language alignment with graph spiking hybrid Networks

• URL: http://arxiv.org/abs/2501.19069v1
• Date: Fri, 31 Jan 2025 11:55:17 GMT
• Title: Improving vision-language alignment with graph spiking hybrid Networks
• Authors: Siyu Zhang, Heming Zheng, Yiming Wu, Yeming Chen,
• Abstract summary: This paper proposes a comprehensive visual semantic representation module, necessitating the utilization of panoptic segmentation to generate fine-grained semantic features. We propose a novel Graph Spiking Hybrid Network (GSHN) that integrates the complementary advantages of Spiking Neural Networks (SNNs) and Graph Attention Networks (GATs) to encode visual semantic information.
• Score: 6.707524980629404
• License:
• Abstract: To bridge the semantic gap between vision and language (VL), it is necessary to develop a good alignment strategy, which includes handling semantic diversity, abstract representation of visual information, and generalization ability of models. Recent works use detector-based bounding boxes or patches with regular partitions to represent visual semantics. While current paradigms have made strides, they are still insufficient for fully capturing the nuanced contextual relations among various objects. This paper proposes a comprehensive visual semantic representation module, necessitating the utilization of panoptic segmentation to generate coherent fine-grained semantic features. Furthermore, we propose a novel Graph Spiking Hybrid Network (GSHN) that integrates the complementary advantages of Spiking Neural Networks (SNNs) and Graph Attention Networks (GATs) to encode visual semantic information. Intriguingly, the model not only encodes the discrete and continuous latent variables of instances but also adeptly captures both local and global contextual features, thereby significantly enhancing the richness and diversity of semantic representations. Leveraging the spatiotemporal properties inherent in SNNs, we employ contrastive learning (CL) to enhance the similarity-based representation of embeddings. This strategy alleviates the computational overhead of the model and enriches meaningful visual representations by constructing positive and negative sample pairs. We design an innovative pre-training method, Spiked Text Learning (STL), which uses text features to improve the encoding ability of discrete semantics. Experiments show that the proposed GSHN exhibits promising results on multiple VL downstream tasks.

Related papers

• Deep Semantic Graph Learning via LLM based Node Enhancement [5.312946761836463]
  Large Language Models (LLMs) have demonstrated superior capabilities in understanding text semantics. This paper proposes a novel framework that combines Graph Transformer architecture with LLM-enhanced node features.
  arXiv  Detail & Related papers  (2025-02-11T21:55:46Z)
• Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation [8.659766913542938]
  We study a united perceptual and semantic token compression for all granular understanding. We propose Feature Pyramid Tokenization (PAT) to cluster and represent multi-resolution feature by learnable codebooks. Our experiments show that PAT enhances the semantic intuition of VLM feature pyramid.
  arXiv  Detail & Related papers  (2024-12-18T18:43:21Z)
• Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process [8.820909397907274]
  We propose a verbalized graph representation learning (VGRL) method which is fully interpretable. In contrast to traditional graph machine learning models, VGRL constrains this parameter space to be text description. We conduct several studies to empirically evaluate the effectiveness of VGRL.
  arXiv  Detail & Related papers  (2024-10-02T12:07:47Z)
• Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning [16.902924543372713]
  State-of-the-art methods achieve impressive performance by pre-training on large-scale datasets. We propose an efficient framework for multimodal alignment by introducing a novel visual semantic module. Experiments show that the proposed ASH-Nets achieve competitive results.
  arXiv  Detail & Related papers  (2023-08-18T10:40:25Z)
• Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning [74.48337375174297]
  Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain. We deploy the dual semantic-visual transformer module (DSVTM) to progressively model the correspondences between prototypes and visual features. DSVTM devises an instance-motivated semantic encoder that learns instance-centric prototypes to adapt to different images, enabling the recast of the unmatched semantic-visual pair into the matched one.
  arXiv  Detail & Related papers  (2023-03-27T15:21:43Z)
• Fine-Grained Semantically Aligned Vision-Language Pre-Training [151.7372197904064]
  Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarity of the global representations of images and texts. We introduce LO, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine-grained semantic alignment from the novel perspective of game-theoretic interactions.
  arXiv  Detail & Related papers  (2022-08-04T07:51:48Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• GINet: Graph Interaction Network for Scene Parsing [58.394591509215005]
  We propose a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss) to promote context reasoning over image regions. The proposed GINet outperforms the state-of-the-art approaches on the popular benchmarks, including Pascal-Context and COCO Stuff.
  arXiv  Detail & Related papers  (2020-09-14T02:52:45Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

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.