5/21/2023 0 Comments World 3d map![]() It can retain long-tailed concepts better than supervised approaches and outperform existing SoTA methods by more than 40%. Our zero-shot pixel alignment technique is used to combine the region-specific features with the global feature, resulting in pixel-aligned features.ĬonceptFusion is evaluated on a mixture of real-world and simulated scenarios. Local features are then extracted for each object, and a global feature is computed for the entire input image. ![]() Input images are processed to generate generic object masks that do not belong to any particular class. This means the robot should be able to use more than one “sense.” It should understand text, image, audio, etc., all together.ĬonceptFusion constructs pixel-aligned features. For example, if we ask the robot to bring us a can of soda, it should understand it as “something to drink” and should be able to associate it with a specific brand, flavor, etc. Open-set modeling means they can capture a wide variety of concepts in great detail. The modeling they do should have two main properties being open-set and multimodal. What does it require to understand and model the environment? If we want our robot to have broad applicability in a range of tasks, it should be able to use its environment modeling without the need for retraining for each new task. We need to find a way to bring this ability to robots so that we can say they can actually understand their environment truly. Or take DINO, for example it can understand and draw boundaries around objects it hasn’t seen before. For example, CLIP can be used to caption and explain images that were never seen during the training set, and it produces reliable results. On the other hand, we have new developments in the AI domain that could “understand” concepts in relatively open-end datasets. □ Check Out 100's AI Tools in AI Tools Club
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