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Found 9 papers, 3 papers with code
When SAM Meets Sonar Images
Notably, there is a lack of research on the application of SAM to sonar imaging.
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs).
Cheap-fake Detection with LLM using Prompt Engineering
We enhance the baseline algorithm by incorporating a Large Language Model (LLM), GPT3. 5, as a feature extractor.
A Lightweight Reconstruction Network for Surface Defect Inspection
Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics.
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection
Firstly, to enhance the model's processing ability for different scale targets, a multi-scale perception module based on dilated convolution is designed, and the depth features containing multi-scale information are re-refined from both spatial and channel directions considering the inconsistency between feature maps of different scales.
Data Augmentation Vision Transformer for Fine-grained Image Classification
Recently, the vision transformer (ViT) has made breakthroughs in image recognition.
Spatio-Temporal-based Context Fusion for Video Anomaly Detection
Motion features are used multiple targets in the video frame to construct spatial context simultaneously, re-encoding the target appearance and motion features, and finally reconstructing the above features through the spatio-temporal dual-stream network, and using the reconstruction error to represent the abnormal score.
Normal Learning in Videos with Attention Prototype Network
With models trained on the normal data, the reconstruction errors of anomalous scenes are usually much larger than those of normal ones.
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods
To determine the convergence rate of these methods, we give an explicit estimate of the exponent in a Lojasiewicz inequality for the (non-convex) set of critical points of the aforementioned class of problems.
