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Found 13 papers, 5 papers with code
Ensemble Quadratic Assignment Network for Graph Matching
In this paper, we propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension
To this end, we propose to extract features corresponding to regional objects as soft prompts for LLM, which provides a straightforward and scalable approach and eliminates the need for LLM fine-tuning.
AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities.
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation
We expect that our PRSeg can promote the development of MLP-based decoder in semantic segmentation.
UniNeXt: Exploring A Unified Architecture for Vision Recognition
Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone.
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.
Demystify Transformers & Convolutions in Modern Image Deep Networks
Our experiments on various tasks and an analysis of inductive bias show a significant performance boost due to advanced network-level and block-level designs, but performance differences persist among different STMs.
CATrans: Context and Affinity Transformer for Few-Shot Segmentation
In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture.
Feature Selective Transformer for Semantic Image Segmentation
In this work, we focus on fusing multi-scale features from Transformer-based backbones for semantic segmentation, and propose a Feature Selective Transformer (FeSeFormer), which aggregates features from all scales (or levels) for each query feature.
StructToken : Rethinking Semantic Segmentation with Structural Prior
In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, \textit{i. e.,} classify each pixel representation to a specific category.
Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention
To reduce the quadratic computation complexity caused by the global self-attention, various methods constrain the range of attention within a local region to improve its efficiency.
Proxy Graph Matching with Proximal Matching Networks
It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization.
Fully Transformer Networks for Semantic Image Segmentation
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies.
