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Found 12 papers, 7 papers with code
PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustment
Camera pose estimation is a long-standing computer vision problem that to date often relies on classical methods, such as handcrafted keypoint matching, RANSAC and bundle adjustment.
Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning
Based on this observation, we propose a new learning strategy named False Negative Aware Contrastive (FNAC) to mitigate the problem of misleading the training with such false negative samples.
Audio-Visual Segmentation with Semantics
To deal with these problems, we propose a new baseline method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process.
Spatial Steerability of GANs via Self-Supervision from Discriminator
Generative models make huge progress to the photorealistic image synthesis in recent years.
Linear Video Transformer with Feature Fixation
Therefore, we propose a feature fixation module to reweight the feature importance of the query and key before computing linear attention.
Audio-Visual Segmentation
To deal with the AVS problem, we propose a novel method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process.
Vicinity Vision Transformer
Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity.
Improving GAN Equilibrium by Raising Spatial Awareness
We further propose to align the spatial awareness of G with the attention map induced from D. Through this way we effectively lessen the information gap between D and G. Extensive results show that our method pushes the two-player game in GANs closer to the equilibrium, leading to a better synthesis performance.
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation
The task of semi-supervised video object segmentation (VOS) has been greatly advanced and state-of-the-art performance has been made by dense matching-based methods.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Deep Two-View Structure-from-Motion Revisited
Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM.
Ranked #16 on
Monocular Depth Estimation
on KITTI Eigen split
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
Learning matching costs has been shown to be critical to the success of the state-of-the-art deep stereo matching methods, in which 3D convolutions are applied on a 4D feature volume to learn a 3D cost volume.
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
In this paper, we propose Deep Epipolar Flow, an unsupervised optical flow method which incorporates global geometric constraints into network learning.
