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Found 6 papers, 1 papers with code
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.
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.
GAN-Based Facial Attractiveness Enhancement
We propose a generative framework based on generative adversarial network (GAN) to enhance facial attractiveness while preserving facial identity and high-fidelity.
