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Found 9 papers, 7 papers with code
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly.
Sinkhorn Distance Minimization for Knowledge Distillation
We propose the Sinkhorn Knowledge Distillation (SinKD) that exploits the Sinkhorn distance to ensure a nuanced and precise assessment of the disparity between teacher and student distributions.
MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks.
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images.
Towards Robust Text Retrieval with Progressive Learning
However, existing embedding models for text retrieval usually have three non-negligible limitations.
Multi-dataset Training of Transformers for Robust Action Recognition
We study the task of robust feature representations, aiming to generalize well on multiple datasets for action recognition.
Stargazer: A transformer-based driver action detection system for intelligent transportation
Distracted driver actions can be dangerous and cause severe accidents.
One for More: Selecting Generalizable Samples for Generalizable ReID Model
Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch.
Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN
Real-time multi-target path planning is a key issue in the field of autonomous driving.
