Search Results for author:
Found 8 papers, 7 papers with code
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions.
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection
We introduce a highly performant 3D object detector for point clouds using the DETR framework.
Ranked #2 on
3D Object Detection
on ScanNetV2
Human Pose as Compositional Tokens
Human pose is typically represented by a coordinate vector of body joints or their heatmap embeddings.
Ranked #1 on
Pose Estimation
on MPII Human Pose
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well.
Ranked #14 on
Monocular Depth Estimation
on NYU-Depth V2
Revealing the Dark Secrets of Masked Image Modeling
In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspectives, the visualizations and the experiments, to uncover their key representational differences.
Ranked #3 on
Depth Estimation
on NYU-Depth V2
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions.
Consistent Instance Classification for Unsupervised Representation Learning
The proposed consistent instance classification (ConIC) approach simultaneously optimizes the classification loss and an additional consistency loss explicitly penalizing the feature dissimilarity between the augmented views from the same instance.
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates
The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping.
