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Found 5 papers, 4 papers with code
Prompt Learning via Meta-Regularization
Recently, prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of downstream tasks.
Ranked #2 on
Prompt Engineering
on Stanford Cars
Stochastic Conditional Diffusion Models for Semantic Image Synthesis
This facilitates the generation of an image close to a clean image, enabling robust generation.
Semantic-Aware Implicit Template Learning via Part Deformation Consistency
In this paper, we highlight the importance of part deformation consistency and propose a semantic-aware implicit template learning framework to enable semantically plausible deformation.
Relation-Aware Language-Graph Transformer for Question Answering
To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner.
Video-Text Representation Learning via Differentiable Weak Temporal Alignment
In this paper, we propose a novel multi-modal self-supervised framework Video-Text Temporally Weak Alignment-based Contrastive Learning (VT-TWINS) to capture significant information from noisy and weakly correlated data using a variant of Dynamic Time Warping (DTW).
