The classes on the horizontal axis are sorted based on the performance of the task model without DTL. Dashed line shows the median performance of all classes. The implementation for MSTCN [2] and ASFormer [6] are from existing opensource code provided by corresponding authors. The result is shown in Fig.A1 and Fig.A2. Weanticipatemoreperformance improvement with more general constraints that go beyond knowledge in the annotations in future works.

The dataset provides synchronized labels as ground truth data, spanning 20 unique activities. Of thetime spent performing activities, 64.9% ofthedatahasground-truth labels entered inrealtime during the experiment.

Would my dog or cat really eat me if I died alone? As grim as it sounds, it's often expected--and biology explains why. Is man's best friend also a dead man's best friend? Case studies say maybe not. Breakthroughs, discoveries, and DIY tips sent every weekday.

The table below shows the notations grouped by the modules. This work was done when Ziwei Xu was visiting the Center for Research in Computer Vision. The classes on the horizontal axis are sorted based on the performance of the task model without DTL. Dashed line shows the median performance of all classes. The annotation above (below) the line indicates the averaged improvement for classes ranked at top (bottom) 50% in the baseline performance.

ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment

We present GeoManip, a framework to enable generalist robots to leverage essential conditions derived from object and part relationships, as geometric constraints, for robot manipulation. For example, cutting the carrot requires adhering to a geometric constraint: the blade of the knife should be perpendicular to the carrot's direction. By interpreting these constraints through symbolic language representations and translating them into low-level actions, GeoManip bridges the gap between natural language and robotic execution, enabling greater generalizability across diverse even unseen tasks, objects, and scenarios. Unlike vision-language-action models that require extensive training, operates training-free by utilizing large foundational models: a constraint generation module that predicts stage-specific geometric constraints and a geometry parser that identifies object parts involved in these constraints. A solver then optimizes trajectories to satisfy inferred constraints from task descriptions and the scene. Furthermore, GeoManip learns in-context and provides five appealing human-robot interaction features: on-the-fly policy adaptation, learning from human demonstrations, learning from failure cases, long-horizon action planning, and efficient data collection for imitation learning. Extensive evaluations on both simulations and real-world scenarios demonstrate GeoManip's state-of-the-art performance, with superior out-of-distribution generalization while avoiding costly model training.

This is what I messaged you about!

We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.

Everybody knows that large language models like GPT-3 and LaMDA have made tremendous strides, at least in some respects, and powered past many benchmarks, and Cosmo recently described DALL-E but most in the field also agree that something is still missing. A growing body of evidence shows that state-of-the-art models learn to exploit spurious statistical patterns in datasets... instead of learning meaning in the flexible and generalizable way that humans do." Since then, the results on benchmarks have gotten better, but there's still something missing. Reference: Words and sentence don't exist in isolation. Language is about a connection between words (or sentence) and the world; the sequences of words that large language models utter lack connection to the external world.

Rohana Meade is the President and CEO at Synergy Technical, a leading-edge technology services and solutions company. As we kick off 2021, business technology leaders around the globe are kicking off their 2021 IT strategic plans. If 2020 taught us anything in the technology world, it was that if you weren't refining your business processes using advances in technology, you should be. Rather than just thinking outside the box, technology leaders should be assuming that there is no box at all. In 2021, artificial intelligence and machine learning technologies will continue to become more mainstream.