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Found 10 papers, 4 papers with code
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation
With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth.
Peer Learning for Unbiased Scene Graph Generation
Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image.
Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification
Then, to build multi-scale hierarchical information of input features, we utilize an attention fusion mechanism to aggregate features from multiple layers of the backbone network.
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation
Specifically, we propose to integrate the mixture of experts with a divide and ensemble strategy to remedy the severely long-tailed distribution of predicate classes, which is applicable to the majority of unbiased scene graph generators.
View Blind-spot as Inpainting: Self-Supervised Denoising with Mask Guided Residual Convolution
Our MGRConv can be regarded as soft partial convolution and find a trade-off among partial convolution, learnable attention maps, and gated convolution.
Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition
The final results in this work show that OTS successfully extracts object features and learns object relations from the segmentation network.
BORM: Bayesian Object Relation Model for Indoor Scene Recognition
First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene.
Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention
This paper presents a semantic-guided automatic natural image matting pipeline with Trimap Generation Network and light-weight non-local attention, which does not need trimap and background as input.
Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training
Each of these small networks has a fraction of the original one's parameters.
Ranked #31 on
Image Classification
on CIFAR-100
(using extra training data)
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
