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Found 7 papers, 2 papers with code
A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification
Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles.
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing
More specifically, our efforts consist of three parts: 1) a data-free training strategy to train latent mappers to bridge the latent space of CLIP and StyleGAN; 2) for more precise mapping, temporal relative consistency is proposed to address the knowledge distribution bias problem among different latent spaces; 3) to refine the mapped latent in s space, adaptive style mixing is also proposed.
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training
In addition, we discuss the function of entropy in TRADES, and we find that models with high entropy can be better robustness learners.
MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image
In this work, we propose a framework for single-view hand mesh reconstruction, which can simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal coherence.
Ranked #7 on
3D Hand Pose Estimation
on DexYCB
Improving Robustness for Pose Estimation via Stable Heatmap Regression
Deep learning methods have achieved excellent performance in pose estimation, but the lack of robustness causes the keypoints to change drastically between similar images.
Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration
In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues.
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets
Monocular depth estimation plays a crucial role in 3D recognition and understanding.
