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Found 14 papers, 5 papers with code
Latent Semantic Consensus For Deterministic Geometric Model Fitting
The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses.
BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning
In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses.
Graph Context Transformation Learning for Progressive Correspondence Pruning
To further apply the recalibrated graph contexts to the global domain, we propose the Graph Context Guidance Transformer.
VSFormer: Visual-Spatial Fusion Transformer for Correspondence Pruning
Then, we model these visual cues and correspondences by a joint visual-spatial fusion module, simultaneously embedding visual cues into correspondences for pruning.
Video Polyp Segmentation: A Deep Learning Perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era.
Ranked #2 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
T-Net: Effective Permutation-Equivariant Network for Two-View Correspondence Learning
We develop a conceptually simple, flexible, and effective framework (named T-Net) for two-view correspondence learning.
Hierarchical Representation via Message Propagation for Robust Model Fitting
In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting.
Hypergraph Optimization for Multi-structural Geometric Model Fitting
Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points.
Learning a Layout Transfer Network for Context Aware Object Detection
We present a context aware object detection method based on a retrieve-and-transform scene layout model.
Superpixel-guided Two-view Deterministic Geometric Model Fitting
Specifically, SDF includes three main parts: a deterministic sampling algorithm, a model hypothesis updating strategy and a novel model selection algorithm.
Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting
We cast the task of geometric model fitting as a representative mode-seeking problem on hypergraphs.
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods.
Hypergraph Modelling for Geometric Model Fitting
The hypergraph, with large and "data-determined" degrees of hyperedges, can express the complex relationships between model hypotheses and data points.
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a weight-aware sampling technique.
