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Found 19 papers, 13 papers with code
Can multiple-choice questions really be useful in detecting the abilities of LLMs?
Additionally, we propose two methods to quantify the consistency and confidence of LLMs' output, which can be generalized to other QA evaluation benchmarks.
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering
Medical Visual Question Answering (Med-VQA) is a very important task in healthcare industry, which answers a natural language question with a medical image.
Towards Robust and Accurate Visual Prompting
Visual prompting, an efficient method for transfer learning, has shown its potential in vision tasks.
Instruct Me More! Random Prompting for Visual In-Context Learning
Our findings suggest that InMeMo offers a versatile and efficient way to enhance the performance of visual ICL with lightweight training.
Concatenated Masked Autoencoders as Spatial-Temporal Learner
Learning representations from videos requires understanding continuous motion and visual correspondences between frames.
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
The large language models have achieved superior performance on various natural language tasks.
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Our summarization compression can reduce 65% of the retrieval token size with further 0. 3% improvement on the accuracy; semantic compression provides a more flexible way to trade-off the token size with performance, for which we can reduce the token size by 20% with only 1. 6% of accuracy drop.
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems
Efficient knowledge retrieval plays a pivotal role in ensuring the success of end-to-end task-oriented dialogue systems by facilitating the selection of relevant information necessary to fulfill user requests.
Improving Facade Parsing with Vision Transformers and Line Integration
We introduce a new pipeline known as Revision-based Transformer Facade Parsing (RTFP).
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
This approach is different from the pruning method as it is not limited by the initial number of training parameters, and each parameter matrix has a higher rank upper bound for the same training overhead.
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
In an effort to minimize the reliance on human resources for performance evaluation, we offer off-the-shelf judgment models for automatically assessing the LF output of LLMs given benchmark questions.
Learning Bottleneck Concepts in Image Classification
Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability.
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging.
Match Them Up: Visually Explainable Few-shot Image Classification
Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories.
Ranked #34 on
Few-Shot Image Classification
on CIFAR-FS 5-way (5-shot)
Automated Grading System of Retinal Arterio-venous Crossing Patterns: A Deep Learning Approach Replicating Ophthalmologist's Diagnostic Process of Arteriolosclerosis
Our severity grading method was able to validate crossing points with precision and recall of 96. 3% and 96. 3%, respectively.
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Semantic video segmentation is a key challenge for various applications.
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Explainable artificial intelligence has been gaining attention in the past few years.
Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
Retinal imaging serves as a valuable tool for diagnosis of various diseases.
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases.
Ranked #5 on
Retinal Vessel Segmentation
on CHASE_DB1
