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These methods, while effective, often involve manually intensive prompt engineering.

While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g.

Conditioning on the current game context, the generation model predicts the distribution of the currently acting player.

Meanwhile, a Neural V ariational Inference (NVI) approach is proposed to learn our model with graph neural networks to encode the document graphs. Besides, we theoretically demonstrate that Latent Dirichlet Allocation (LDA) can be derived from GNTM as a special case with similar objective functions.

Fleming went on to win the gold medal. Breakthroughs, discoveries, and DIY tips sent six days a week. From figure skating to ice hockey, many of the most popular winter sports stem from a long history of people simply playing around on ice skates . Part of what makes a good skater so fun to watch is the juxtaposition of their clear technical skill and the seeming effortlessness with which they glide across the ice. They make it seem so natural.

Accurate estimation of human pose and the pose of interacting objects, like a hockey stick, is crucial for action recognition and performance analysis, particularly in sports. Existing methods capture the object along with the human in the bounding boxes, assuming all keypoints are visible within the bounding box. This necessitates larger bounding boxes to capture the object, introducing unnecessary visual features and hindering performance in real-world cluttered environments. We propose a simple image and text-based multimodal solution TokenCLIPose that addresses this limitation. Our approach focuses solely on human keypoints within the bounding box, treating objects as unseen. TokenCLIPose leverages the rich semantic representations endowed by language for inducing keypoint-specific context, even for occluded keypoints. We evaluate the performance of TokenCLIPose on a real-world Ice-Hockey dataset, and demonstrate its generalizability through zero-shot transfer to a smaller Lacrosse dataset.