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Found 11 papers, 6 papers with code
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection
Trained on a set of anomaly-free hyperspectral images with random masks, our network can learn the spatial context characteristics between anomalies and background in an unsupervised way.
Micro Expression Generation with Thin-plate Spline Motion Model and Face Parsing
We (Team: USTC-IAT-United) also compare our method with other competitors' in MEGC2022, and the expert evaluation results show that our method performs best, which verifies the effectiveness of our method.
Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection
To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images.
A viable framework for semi-supervised learning on realistic dataset
Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
Ranking-Based Siamese Visual Tracking
Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization.
Multi-model Ensemble Learning Method for Human Expression Recognition
Moreover, we introduce the multi-fold ensemble method to train and ensemble several models with the same architecture but different data distributions to enhance the performance of our solution.
Cognitive Diagnosis with Explicit Student Vector Estimation and Unsupervised Question Matrix Learning
Then, based on the estimated student vectors, the probabilistic part of DINA can be modified to a student dependent model that the slip and guess rates are related to student vectors.
BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition
Semi-supervised Fine-Grained Recognition is a challenge task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch.
Adaptively Meshed Video Stabilization
Moreover, foreground and background feature trajectories are no longer distinguished and both contribute to the estimation of the camera motion in the proposed optimization problem, which yields better estimation performance than previous works, particularly in challenging videos with large foreground objects or strong parallax.
Bicycle Detection Based On Multi-feature and Multi-frame Fusion in low-resolution traffic videos
So bicycle detection is one major task of traffic video surveillance systems in China.
