Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training

• URL: http://arxiv.org/abs/2108.09479v1
• Date: Sat, 21 Aug 2021 09:57:21 GMT
• Title: Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training
• Authors: Ming Yan, Haiyang Xu, Chenliang Li, Bin Bi, Junfeng Tian, Min Gui and Wei Wang
• Abstract summary: Existing approaches to vision-language pre-training rely on an object detector based on bounding boxes (regions) In this paper, we revisit grid-based convolutional features for vision-language pre-training, skipping the expensive region-related steps.
• Score: 27.103514548337404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images and then a Transformer-based model is used for cross-modal fusion. Despite their superior performance, these approaches are bounded by the capability of the object detector in terms of both effectiveness and efficiency. Besides, the presence of object detection imposes unnecessary constraints on model designs and makes it difficult to support end-to-end training. In this paper, we revisit grid-based convolutional features for vision-language pre-training, skipping the expensive region-related steps. We propose a simple yet effective grid-based VLP method that works surprisingly well with the grid features. By pre-training only with in-domain datasets, the proposed Grid-VLP method can outperform most competitive region-based VLP methods on three examined vision-language understanding tasks. We hope that our findings help to further advance the state of the art of vision-language pre-training, and provide a new direction towards effective and efficient VLP.

Related papers

• Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation [105.23631749213729]
  We propose a novel method for unsupervised pre-training in low-data regimes. Inspired by the recently successful prompting technique, we introduce a new method, Unsupervised Pre-training with Language-Vision Prompts. We show that our method can converge faster and perform better than CNN-based models in low-data regimes.
  arXiv  Detail & Related papers  (2024-05-22T06:48:43Z)
• Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation [19.20874993309959]
  vision-language foundation models, such as CLIP, have showcased remarkable effectiveness in numerous zero-shot image-level tasks. We propose a baseline for training-free OVSS, termed Neighbour-Aware CLIP (NACLIP) Our method enforces localization of patches in the self-attention of CLIP's vision transformer which, despite being crucial for dense prediction tasks, has been overlooked in the OVSS literature.
  arXiv  Detail & Related papers  (2024-04-12T01:08:04Z)
• Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition [72.35438297011176]
  We propose a novel method to realize seamless adaptation of pre-trained models for visual place recognition (VPR) Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time.
  arXiv  Detail & Related papers  (2024-02-22T12:55:01Z)
• APoLLo: Unified Adapter and Prompt Learning for Vision Language Models [58.9772868980283]
  We present APoLLo, a unified multi-modal approach that combines Adapter and Prompt learning for Vision-Language models. APoLLo achieves a relative gain up to 6.03% over MaPLe (SOTA) on novel classes for 10 diverse image recognition datasets.
  arXiv  Detail & Related papers  (2023-12-04T01:42:09Z)
• Exploiting the Textual Potential from Vision-Language Pre-training for Text-based Person Search [17.360982091304137]
  Text-based Person Search (TPS) is targeted on retrieving pedestrians to match text descriptions instead of query images. Recent Vision-Language Pre-training models can bring transferable knowledge to downstream TPS tasks, resulting in more efficient performance gains. However, existing TPS methods only utilize pre-trained visual encoders, neglecting the corresponding textual representation.
  arXiv  Detail & Related papers  (2023-03-08T10:41:22Z)
• Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection [87.39089806069707]
  We propose a fine-grained Visual-Text Prompt-driven self-training paradigm for Open-Vocabulary Detection (VTP-OVD) During the adapting stage, we enable VLM to obtain fine-grained alignment by using learnable text prompts to resolve an auxiliary dense pixel-wise prediction task. Experiments show that our method achieves the state-of-the-art performance for open-vocabulary object detection, e.g., 31.5% mAP on unseen classes of COCO.
  arXiv  Detail & Related papers  (2022-11-02T03:38:02Z)
• VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment [52.489874804051304]
  VoLTA is a new vision-language pre-training paradigm that only utilizes image-caption data but fine-grained region-level image understanding. VoLTA pushes multi-modal fusion deep into the uni-modal backbones during pre-training. Experiments on a wide range of vision- and vision-language downstream tasks demonstrate the effectiveness of VoLTA.
  arXiv  Detail & Related papers  (2022-10-09T01:49:58Z)
• PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models [127.17675443137064]
  We introduce PEVL, which enhances the pre-training and prompt tuning of vision-language models with explicit object position modeling. PEVL reformulates discretized object positions and language in a unified language modeling framework. We show that PEVL enables state-of-the-art performance on position-sensitive tasks such as referring expression comprehension and phrase grounding.
  arXiv  Detail & Related papers  (2022-05-23T10:17:53Z)
• KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation [42.01427946204401]
  Self-supervised vision-and-language pretraining aims to learn transferable multi-modal representations from large-scale image-text data. We propose an object-aware end-to-end QF framework, which directly feeds image grid features from CNNs into the Transformer and learns the multi-modal representations jointly. To achieve that, we design two novel pretext tasks by taking object features and their semantic labels from external detectors as supervision.
  arXiv  Detail & Related papers  (2021-09-22T03:38:05Z)
• E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning [31.622393984150314]
  We propose the first end-to-end vision-language pre-trained model for both V+L understanding and generation. We build a unified Transformer framework to jointly learn visual representation, and semantic alignments between image and text.
  arXiv  Detail & Related papers  (2021-06-03T12:50:26Z)

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.