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Found 11 papers, 5 papers with code
Exploring Interpretability for Visual Prompt Tuning with Hierarchical Concepts
Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks.
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions.
Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection
We present Uni$^2$Det, a brand new framework for unified and universal multi-dataset training on 3D detection, enabling robust performance across diverse domains and generalization to unseen domains.
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling
Specifically, we introduce a relationship-guided attention module to capture pair-wise associations among entities and attributes for low-level prompt learning.
Ranked #1 on
Prompt Engineering
on ImageNet V2
ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding
Video temporal grounding is an emerging topic aiming to identify specific clips within videos.
The Application of ChatGPT in Responding to Questions Related to the Boston Bowel Preparation Scale
This study aims to assess the accuracy and consistency of ChatGPT in using the Boston Bowel Preparation Scale (BBPS) for colonoscopy assessment.
Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
To address this limitation and prioritize harnessing structured knowledge, this paper advocates for leveraging LLMs to build a graph for each description to model the entities and attributes describing the category, as well as their correlations.
Ranked #2 on
Prompt Engineering
on ImageNet V2
Exploring Shape Embedding for Cloth-Changing Person Re-Identification via 2D-3D Correspondences
CSCL establishes continuous correspondences between a 2D image plane and a canonical 3D body surface via pixel-to-vertex classification, which naturally aligns a person image to the surface of a 3D human model and simultaneously obtains pixel-wise surface embeddings.
Learning Domain Invariant Prompt for Vision-Language Models
Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vectors with very few samples.
Ranked #5 on
Prompt Engineering
on Food-101
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Event detection tends to struggle when it needs to recognize novel event types with a few samples.
Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model.
Ranked #1 on
Relation Extraction
on NYT-single
