However, current 3D semantic segmentation benchmarks contain only a small number of categories -- less than 30 for ScanNet and SemanticKITTI, for instance, which are not enough to reflect the diversity of real environments (e.g., semantic image understanding covers hundreds to thousands of ...
Implementation for ECCV 2022 paper Language-Grounded Indoor 3D Semantic Segmentation in the Wild - GitHub - RozDavid/LanguageGroundedSemseg: Implementation for ECCV 2022 paper Language-Grounded Indoor 3D Semantic Segmentation in the Wild
Building upon recent success of the large language models, our main objective is to improve the state abstraction technique in reinforcement learning by leveraging language for robust action selection. Specifically, we focus on learning language-grounded visual features to enhance the world model ...
[2311.17593] LanGWM: Language Grounded World Model ViT+RNN+Actor-Critic=自主强化学习模型 Part1背景 强化学习在许多领域都取得了成功,比如 DreamerV3 可以自主从 Minecraft 中采集钻石。 然而,这些基于图像的 RL 控制模型在 iGibson OOD 测试中的表现都并不好。 Part2动机 本文认为,基于图像的控制模型都需要看到...
Daniel Honerkamp*Martin Büchner*,Fabien Despinoy,Tim WelscheholdandAbhinav Valada Please cite the paper as follows: @article{honerkamp2024language, title={Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation}, journal={IEEE Robotics and Automation Letters}, auth...
这个任务是学习object-level,语言感知和语义丰富的视觉表示的有效和可扩展的预训练任务,并提出了Grounded Language-Image Pre-training(GLIP)。我们的方法统一了phrase grounding和object detection任务,object detection可以被转换为上下文无关的phrase grounding,而phrase grounding可以被视为置于context背景下的的object ...
意思是让模型去学习图片和句子短语之间更加精细的联系。然后提出了GLIP模型：Grounded Language-Image Pre...
微软的《Grounded Language-Image Pre-training（GLIP）》文章提出了一种结合短语定位与目标检测的预训练方法，显著拓宽了自然语言在目标检测领域的应用。GLIP模型不仅在COCO、LVIS等任务中刷新了历史最好成绩，还展示了卓越的零样本预测能力。GLIP模型通过将目标检测任务转换为短语定位任务，利用语言-图像预...
Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially ...
Object DetectionCOCO test-devGLIP (Swin-L, multi-scale)box mAP61.5# 21 Compare AP5079.5# 4 Compare AP7567.7# 4 Compare APS45.3# 4 Compare APM64.9# 4 Compare APL75.0# 4 Compare Described Object DetectionDescription Detection DatasetGLIP-TIntra-scenario FULL mAP19.1# 4 ...