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Found 6 papers, 4 papers with code
Semantic Diffusion Network for Semantic Segmentation
We then propose a novel learnable approach called semantic diffusion network (SDN) to approximate the diffusion process, which contains a parameterized semantic difference convolution operator followed by a feature fusion module.
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation
Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance.
Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation
Relying on the model's performance, iMAS employs a class-weighted symmetric intersection-over-union to evaluate quantitative hardness of each unlabeled instance and supervises the training on unlabeled data in a model-adaptive manner.
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers
In this paper, we emphasize the cruciality of diverse global semantics and propose an efficient token decoupling and merging method that can jointly consider the token importance and diversity for token pruning.
Ranked #4 on
Efficient ViTs
on ImageNet-1K (with DeiT-T)
CAE v2: Context Autoencoder with CLIP Target
That is to say, the smaller the model, the lower the mask ratio needs to be.
Multimodal Utterance-level Affect Analysis using Visual, Audio and Text Features
The integration of information across multiple modalities and across time is a promising way to enhance the emotion recognition performance of affective systems.
