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Found 13 papers, 1 papers with code
CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detection
Existing camouflaged object detection~(COD) methods depend heavily on large-scale pixel-level annotations. However, acquiring such annotations is laborious due to the inherent camouflage characteristics of the objects. Semi-supervised learning offers a promising solution to this challenge. Yet, its application in COD is hindered by significant pseudo-label noise, both pixel-level and instance-level. We introduce CamoTeacher, a novel semi-supervised COD framework, utilizing Dual-Rotation Consistency Learning~(DRCL) to effectively address these noise issues. Specifically, DRCL minimizes pseudo-label noise by leveraging rotation views' consistency in pixel-level and instance-level. First, it employs Pixel-wise Consistency Learning~(PCL) to deal with pixel-level noise by reweighting the different parts within the pseudo-label. Second, Instance-wise Consistency Learning~(ICL) is used to adjust weights for pseudo-labels, which handles instance-level noise. Extensive experiments on four COD benchmark datasets demonstrate that the proposed CamoTeacher not only achieves state-of-the-art compared with semi-supervised learning methods, but also rivals established fully-supervised learning methods. Our code will be available soon.
Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble
Thirdly, we propose a probability post-process to increase the predicted value of the rare classes.
Visual Tuning
With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.
AttentionMixer: An Accurate and Interpretable Framework for Process Monitoring
An accurate and explainable automatic monitoring system is critical for the safety of high efficiency energy conversion plants that operate under extreme working condition.
Towards Efficient Visual Adaption via Structural Re-parameterization
Experimental results show the superior performance and efficiency of RepAdapter than the state-of-the-art PETL methods.
RefTeacher: A Strong Baseline for Semi-Supervised Referring Expression Comprehension
In this paper, we present the first attempt of semi-supervised learning for REC and propose a strong baseline method called RefTeacher.
CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises
Enterprise relation extraction aims to detect pairs of enterprise entities and identify the business relations between them from unstructured or semi-structured text data, and it is crucial for several real-world applications such as risk analysis, rating research and supply chain security.
Image restoration quality assessment based on regional differential information entropy
With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation.
UNetGAN: A Robust Speech Enhancement Approach in Time Domain for Extremely Low Signal-to-noise Ratio Condition
This approach consists of a generator network and a discriminator network, which operate directly in the time domain.
SNR-Based Teachers-Student Technique for Speech Enhancement
Specifically, this method consists of multiple teacher models and a student model.
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
3D moving object detection is one of the most critical tasks in dynamic scene analysis.
Saturation problems in the Ramsey theory of graphs, posets and point sets
We prove a saturation version of the Erd\H{o}s-Szekeres theorem about monotone subsequences and saturation versions of some Ramsey-type theorems on graphs and Dilworth-type theorems on posets.
Combinatorics
On a hypergraph probabilistic graphical model
We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs.
