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Found 10 papers, 1 papers with code
LLMs are Good Action Recognizers
Motivated by this, we propose a novel LLM-AR framework, in which we investigate treating the Large Language Model as an Action Recognizer.
GPT-Connect: Interaction between Text-Driven Human Motion Generator and 3D Scenes in a Training-free Manner
Recently, while text-driven human motion generation has received massive research attention, most existing text-driven motion generators are generally only designed to generate motion sequences in a blank background.
Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning
Human-centered dynamic scene understanding plays a pivotal role in enhancing the capability of robotic and autonomous systems, in which Video-based Human-Object Interaction (V-HOI) detection is a crucial task in semantic scene understanding, aimed at comprehensively understanding HOI relationships within a video to benefit the behavioral decisions of mobile robots and autonomous driving systems.
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation
Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds.
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition
Open-set object recognition aims to identify if an object is from a class that has been encountered during training or not.
A Characteristic Function-Based Method for Bottom-Up Human Pose Estimation
Therefore, via minimizing the distance between the two characteristic functions, we can optimize the model to provide a more accurate localization result for the body joints in different sub-regions of the predicted heatmap.
Improving the Reliability for Confidence Estimation
Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models.
Heatmap Distribution Matching for Human Pose Estimation
In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction.
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation
Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video.
Recent Advances of Continual Learning in Computer Vision: An Overview
In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order.
