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Found 9 papers, 3 papers with code
Retrieval-Augmented Generation for Large Language Models: A Survey
Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.
Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection
Out-of-distribution (OOD) detection is essential for reliable and trustworthy machine learning.
Long-Tailed Question Answering in an Open World
A retrieve-then-rerank frame is further introduced to select in-context examples, which guild the LM to generate text that express knowledge for QA tasks.
Domain Incremental Lifelong Learning in an Open World
Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across input samples to improve the model's generalization performance.
VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection
For this reason, we propose to advance research areas of video understanding, with a shift from traditional action recognition to industrial anomaly analysis.
Continual Relation Learning via Episodic Memory Activation and Reconsolidation
Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations.
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process
The proposed method consists of three elemental operators, that are dynamic texture model based motion segmentation, feature extraction and Gaussian process (GP) regression.
Robust video object tracking via Bayesian model averaging based feature fusion
In this article, we are concerned with tracking an object of interest in video stream.
Robust video object tracking using particle filter with likelihood based feature fusion and adaptive template updating
In this solution, the bootstrap particle filter (PF) is initialized by an object detector, which models the time-evolving background of the video signal by an adaptive Gaussian mixture.
