Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolution

• URL: http://arxiv.org/abs/2205.12089v1
• Date: Tue, 24 May 2022 14:12:32 GMT
• Title: Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolution
• Authors: Georgios Tziafas, Hamidreza Kasaei
• Abstract summary: We consider the task of visual grounding, where the agent segments an object from a crowded scene given a natural language description. Modern holistic approaches to visual grounding usually ignore language structure and struggle to cover generic domains. We introduce a fully decoupled modular framework for compositional visual grounding of entities, attributes, and spatial relations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Service robots should be able to interact naturally with non-expert human users, not only to help them in various tasks but also to receive guidance in order to resolve ambiguities that might be present in the instruction. We consider the task of visual grounding, where the agent segments an object from a crowded scene given a natural language description. Modern holistic approaches to visual grounding usually ignore language structure and struggle to cover generic domains, therefore relying heavily on large datasets. Additionally, their transfer performance in RGB-D datasets suffers due to high visual discrepancy between the benchmark and the target domains. Modular approaches marry learning with domain modeling and exploit the compositional nature of language to decouple visual representation from language parsing, but either rely on external parsers or are trained in an end-to-end fashion due to the lack of strong supervision. In this work, we seek to tackle these limitations by introducing a fully decoupled modular framework for compositional visual grounding of entities, attributes, and spatial relations. We exploit rich scene graph annotations generated in a synthetic domain and train each module independently. Our approach is evaluated both in simulation and in two real RGB-D scene datasets. Experimental results show that the decoupled nature of our framework allows for easy integration with domain adaptation approaches for Sim-To-Real visual recognition, offering a data-efficient, robust, and interpretable solution to visual grounding in robotic applications.

Related papers

• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Learning to Ground VLMs without Forgetting [54.033346088090674]
  We introduce LynX, a framework that equips pretrained Visual Language Models with visual grounding ability without forgetting their existing image and language understanding skills. To train the model effectively, we generate a high-quality synthetic dataset we call SCouT, which mimics human reasoning in visual grounding. We evaluate LynX on several object detection and visual grounding datasets, demonstrating strong performance in object detection, zero-shot localization and grounded reasoning.
  arXiv  Detail & Related papers  (2024-10-14T13:35:47Z)
• Pushing the Limits of Vision-Language Models in Remote Sensing without Human Annotations [5.065947993017157]
  This study introduces an approach to curate vision-language datasets by employing an image decoding machine learning model. We amassed approximately 9.6 million vision-language paired datasets in VHR imagery. The resultant model outperformed counterparts that did not leverage publicly available vision-language datasets.
  arXiv  Detail & Related papers  (2024-09-11T06:36:08Z)
• Learning Visual Grounding from Generative Vision and Language Model [29.2712567454021]
  Visual grounding tasks aim to localize image regions based on natural language references. We find that grounding knowledge already exists in generative VLM and can be elicited by proper prompting. Our results demonstrate the promise of generative VLM to scale up visual grounding in the real world.
  arXiv  Detail & Related papers  (2024-07-18T20:29:49Z)
• 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)
• Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter [14.489086924126253]
  This work focuses on the task of referring grasp synthesis, which predicts a grasp pose for an object referred through natural language in cluttered scenes. Existing approaches often employ multi-stage pipelines that first segment the referred object and then propose a suitable grasp, and are evaluated in private datasets or simulators that do not capture the complexity of natural indoor scenes. We propose a novel end-to-end model (CROG) that leverages the visual grounding capabilities of CLIP to learn synthesis grasp directly from image-text pairs.
  arXiv  Detail & Related papers  (2023-11-09T22:55:10Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• Structure-Guided Image Completion with Image-level and Object-level Semantic Discriminators [97.12135238534628]
  We propose a learning paradigm that consists of semantic discriminators and object-level discriminators for improving the generation of complex semantics and objects. Specifically, the semantic discriminators leverage pretrained visual features to improve the realism of the generated visual concepts. Our proposed scheme significantly improves the generation quality and achieves state-of-the-art results on various tasks.
  arXiv  Detail & Related papers  (2022-12-13T01:36:56Z)
• CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations [98.30038910061894]
  Vision-and-Language Navigation (VLN) tasks require an agent to navigate through the environment based on language instructions. We propose CLEAR: Cross-Lingual and Environment-Agnostic Representations. Our language and visual representations can be successfully transferred to the Room-to-Room and Cooperative Vision-and-Dialogue Navigation task.
  arXiv  Detail & Related papers  (2022-07-05T17:38:59Z)
• Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction [4.137464623395377]
  We show how a grounding domain, a denotation function and a composition function are learned from user data only. We benchmark our grounded semantics on compositionality and zero-shot inference tasks.
  arXiv  Detail & Related papers  (2021-04-18T15:03:16Z)
• Language and Visual Entity Relationship Graph for Agent Navigation [54.059606864535304]
  Vision-and-Language Navigation (VLN) requires an agent to navigate in a real-world environment following natural language instructions. We propose a novel Language and Visual Entity Relationship Graph for modelling the inter-modal relationships between text and vision. Experiments show that by taking advantage of the relationships we are able to improve over state-of-the-art.
  arXiv  Detail & Related papers  (2020-10-19T08:25:55Z)

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.