Search Results for author:
Found 7 papers, 4 papers with code
Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases
Recent studies have demonstrated the capabilities of LLMs to automatically conduct prompt engineering by employing a meta-prompt that incorporates the outcomes of the last trials and proposes an improved prompt.
Stay on topic with Classifier-Free Guidance
Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations.
Ranked #1 on
Text Generation
on SciQ
DLT: Conditioned layout generation with Joint Discrete-Continuous Diffusion Layout Transformer
The ability to condition layout generation on a partial subset of component attributes is critical to real-world applications that involve user interaction.
Reducing Class Collapse in Metric Learning with Easy Positive Sampling
We theoretically prove and empirically show that under reasonable noise assumptions, prevalent embedding losses in metric learning, e. g., triplet loss, tend to project all samples of a class with various modes onto a single point in the embedding space, resulting in a class collapse that usually renders the space ill-sorted for classification or retrieval.
Rethinking preventing class-collapsing in metric learning with margin-based losses
We theoretically prove and empirically show that under reasonable noise assumptions, margin-based losses tend to project all samples of a class with various modes onto a single point in the embedding space, resulting in a class collapse that usually renders the space ill-sorted for classification or retrieval.
Accurate Visual Localization for Automotive Applications
We propose a hybrid coarse-to-fine approach that leverages visual and GPS location cues.
Spatio-Temporal Action Graph Networks
Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep model to generalize to future object appearance.
