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Found 9 papers, 1 papers with code
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs.
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications.
Balancing Logit Variation for Long-tailed Semantic Segmentation
In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation.
ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes
In this paper, we introduce ZeroPose, a novel zero-shot framework that performs pose estimation following a Discovery-Orientation-Registration (DOR) inference pipeline.
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
To this end, we propose SeqCo-DETR, a novel Sequence Consistency-based self-supervised method for object DEtection with TRansformers.
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working.
Geo6D: Geometric Constraints Learning for 6D Pose Estimation
Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters.
Uni6Dv2: Noise Elimination for 6D Pose Estimation
Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images.
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization
To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage.
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Anomaly Detection
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