Augmented Commonsense Knowledge for Remote Object Grounding

• URL: http://arxiv.org/abs/2406.01256v1
• Date: Mon, 3 Jun 2024 12:12:33 GMT
• Title: Augmented Commonsense Knowledge for Remote Object Grounding
• Authors: Bahram Mohammadi, Yicong Hong, Yuankai Qi, Qi Wu, Shirui Pan, Javen Qinfeng Shi,
• Abstract summary: We propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as atemporal knowledge graph for improving agent navigation. ACK consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment. We add a new pipeline for the commonsense-based decision-making process which leads to more accurate local action prediction.
• Score: 67.30864498454805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object features to represent navigable viewpoints. However, these representations are insufficient for proper action prediction, especially for the REVERIE task, which uses concise high-level instructions, such as ''Bring me the blue cushion in the master bedroom''. To address enhancing representation, we propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as a spatio-temporal knowledge graph for improving agent navigation. Specifically, the proposed approach involves constructing a knowledge base by retrieving commonsense information from ConceptNet, followed by a refinement module to remove noisy and irrelevant knowledge. We further present ACK which consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment by integrating visible objects, commonsense knowledge, and concept history, which includes object and knowledge temporal information. Moreover, we add a new pipeline for the commonsense-based decision-making process which leads to more accurate local action prediction. Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark.

Related papers

• Towards Zero-shot Human-Object Interaction Detection via Vision-Language Integration [14.678931157058363]
  We propose a novel framework, termed Knowledge Integration to HOI (KI2HOI), that effectively integrates the knowledge of visual-language model to improve zero-shot HOI detection. We develop an effective additive self-attention mechanism to generate more comprehensive visual representations. Our model outperforms the previous methods in various zero-shot and full-supervised settings.
  arXiv  Detail & Related papers  (2024-03-12T02:07:23Z)
• Aligning Knowledge Graph with Visual Perception for Object-goal Navigation [16.32780793344835]
  We propose the Aligning Knowledge Graph with Visual Perception (AKGVP) method for object-goal navigation. Our approach introduces continuous modeling of the hierarchical scene architecture and leverages visual-language pre-training to align natural language description with visual perception. The integration of a continuous knowledge graph architecture and multimodal feature alignment empowers the navigator with a remarkable zero-shot navigation capability.
  arXiv  Detail & Related papers  (2024-02-29T06:31:18Z)
• Learning Navigational Visual Representations with Semantic Map Supervision [85.91625020847358]
  We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps. Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
  arXiv  Detail & Related papers  (2023-07-23T14:01:05Z)
• Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation [68.13453771001522]
  We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings. We conduct extensive experiments and evaluate our model on large-scale real-world data.
  arXiv  Detail & Related papers  (2023-06-14T13:07:48Z)
• KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation [61.08389704326803]
  Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. We propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability.
  arXiv  Detail & Related papers  (2023-03-28T08:00:46Z)
• Embodied Learning for Lifelong Visual Perception [33.02424587900808]
  We study lifelong visual perception in an embodied setup, where we develop new models and compare various agents that navigate in buildings. The purpose of the agents is to recognize objects and other semantic classes in the whole building at the end of a process that combines exploration and active visual learning.
  arXiv  Detail & Related papers  (2021-12-28T10:47:13Z)
• Embodied Visual Active Learning for Semantic Segmentation [33.02424587900808]
  We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding. We develop a battery of agents - both learnt and pre-specified - and with different levels of knowledge of the environment. We extensively evaluate the proposed models using the Matterport3D simulator and show that a fully learnt method outperforms comparable pre-specified counterparts.
  arXiv  Detail & Related papers  (2020-12-17T11:02:34Z)
• Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations [103.00383924074585]
  Visual relationship detection aims to reason over relationships among salient objects in images. We propose a novel approach named Visual-Linguistic Representations from Transformers (RVL-BERT) RVL-BERT performs spatial reasoning with both visual and language commonsense knowledge learned via self-supervised pre-training.
  arXiv  Detail & Related papers  (2020-09-10T16:15:09Z)
• Spatio-Temporal Graph for Video Captioning with Knowledge Distillation [50.034189314258356]
  We propose a graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid correlations caused by the variable number of objects, we propose an object-aware knowledge distillation mechanism.
  arXiv  Detail & Related papers  (2020-03-31T03:58:11Z)

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.