Large Language Model
PromptFix: You Prompt and We Fix the Photo Y ongsheng Y u
Next, we propose a high-frequency guidance sampling method to explicitly control the denoising process and preserve high-frequency details in unprocessed areas. Finally, we design an auxiliary prompting adapter, utilizing Vision-Language Models (VLMs) to enhance text prompts and improve the model's task
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge
We explore those different aspects in relation to mutual knowledge, and analyze zero-shot predictions.
Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
Large Language Models (LLMs) have transformed natural language processing and extended their powerful capabilities to multi-modal domains. As LLMs continue to advance, it is crucial to develop diverse and appropriate metrics for their evaluation. In this paper, we introduce a novel rank-based metric, Diff-eRank, grounded in information theory and geometry principles.
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model
However, VL trackers are still inferior to State-of-The-Art (SoT A) visual trackers in terms of tracking performance.
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
Large Language Models that are evaluated on multiple metrics.
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
Pre-trained large language models (LLMs) based on Transformer have demonstrated striking in-context learning (ICL) abilities.
Generative Hierarchical Materials Search Sherry Y ang
GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates
