Beyond Bounding Box: Multimodal Knowledge Learning for Object Detection

• URL: http://arxiv.org/abs/2205.04072v1
• Date: Mon, 9 May 2022 07:03:30 GMT
• Title: Beyond Bounding Box: Multimodal Knowledge Learning for Object Detection
• Authors: Weixin Feng, Xingyuan Bu, Chenchen Zhang, Xubin Li
• Abstract summary: We take advantage of language prompt to introduce effective and unbiased linguistic supervision into object detection. We propose a new mechanism called multimodal knowledge learning (textbfMKL), which is required to learn knowledge from language supervision.
• Score: 3.785123406103386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal supervision has achieved promising results in many visual language understanding tasks, where the language plays an essential role as a hint or context for recognizing and locating instances. However, due to the defects of the human-annotated language corpus, multimodal supervision remains unexplored in fully supervised object detection scenarios. In this paper, we take advantage of language prompt to introduce effective and unbiased linguistic supervision into object detection, and propose a new mechanism called multimodal knowledge learning (\textbf{MKL}), which is required to learn knowledge from language supervision. Specifically, we design prompts and fill them with the bounding box annotations to generate descriptions containing extensive hints and context for instances recognition and localization. The knowledge from language is then distilled into the detection model via maximizing cross-modal mutual information in both image- and object-level. Moreover, the generated descriptions are manipulated to produce hard negatives to further boost the detector performance. Extensive experiments demonstrate that the proposed method yields a consistent performance gain by 1.6\% $\sim$ 2.1\% and achieves state-of-the-art on MS-COCO and OpenImages datasets.

Related papers

• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization [49.992614129625274]
  ForgeryGPT is a novel framework that advances the Image Forgery Detection and localization task. It captures high-order correlations of forged images from diverse linguistic feature spaces. It enables explainable generation and interactive dialogue through a newly customized Large Language Model (LLM) architecture.
  arXiv  Detail & Related papers  (2024-10-14T07:56:51Z)
• Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception [63.03288425612792]
  We propose bfAnyRef, a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references. Our model achieves state-of-the-art results across multiple benchmarks, including diverse modality referring segmentation and region-level referring expression generation.
  arXiv  Detail & Related papers  (2024-03-05T13:45:46Z)
• Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects [11.117055725415446]
  Large Vision Language Models (LVLMs) have demonstrated impressive zero-shot capabilities in various vision-language dialogue scenarios. The absence of fine-grained visual object detection hinders the model from understanding the details of images, leading to irreparable visual hallucinations and factual errors. We propose Lyrics, a novel multi-modal pre-training and instruction fine-tuning paradigm that bootstraps vision-language alignment from fine-grained cross-modal collaboration.
  arXiv  Detail & Related papers  (2023-12-08T09:02:45Z)
• LanGWM: Language Grounded World Model [24.86620763902546]
  We focus on learning language-grounded visual features to enhance the world model learning. Our proposed technique of explicit language-grounded visual representation learning has the potential to improve models for human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-29T12:41:55Z)
• Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object Detection [72.36017150922504]
  We propose a multi-modal contextual knowledge distillation framework, MMC-Det, to transfer the learned contextual knowledge from a teacher fusion transformer to a student detector. The diverse multi-modal masked language modeling is realized by an object divergence constraint upon traditional multi-modal masked language modeling (MLM)
  arXiv  Detail & Related papers  (2023-08-30T08:33:13Z)
• Contextual Object Detection with Multimodal Large Language Models [66.15566719178327]
  We introduce a novel research problem of contextual object detection. Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering. We present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts.
  arXiv  Detail & Related papers  (2023-05-29T17:50:33Z)
• Position-Aware Contrastive Alignment for Referring Image Segmentation [65.16214741785633]
  We present a position-aware contrastive alignment network (PCAN) to enhance the alignment of multi-modal features. Our PCAN consists of two modules: 1) Position Aware Module (PAM), which provides position information of all objects related to natural language descriptions, and 2) Contrastive Language Understanding Module (CLUM), which enhances multi-modal alignment.
  arXiv  Detail & Related papers  (2022-12-27T09:13:19Z)
• OmDet: Large-scale vision-language multi-dataset pre-training with multimodal detection network [17.980765138522322]
  This work introduces OmDet, a novel language-aware object detection architecture. Leveraging natural language as a universal knowledge representation, OmDet accumulates a "visual vocabulary" from diverse datasets. We demonstrate superior performance of OmDet over strong baselines in object detection in the wild, open-vocabulary detection, and phrase grounding.
  arXiv  Detail & Related papers  (2022-09-10T14:25:14Z)

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.