Search Results for author:
Found 9 papers, 6 papers with code
EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment
Second, we propose a novel image-text alignment module for improved feature extraction of the Stable Diffusion backbone.
Ranked #1 on
Referring Expression Segmentation
on RefCOCO testB
Monocular Depth Estimation
Referring Expression Segmentation
LooseControl: Lifting ControlNet for Generalized Depth Conditioning
We present LooseControl to allow generalized depth conditioning for diffusion-based image generation.
PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation
Single image depth estimation is a foundational task in computer vision and generative modeling.
LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts
Diffusion-based generative models have significantly advanced text-to-image generation but encounter challenges when processing lengthy and intricate text prompts describing complex scenes with multiple objects.
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.
Ranked #13 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
LocalBins: Improving Depth Estimation by Learning Local Distributions
We build on AdaBins which estimates a global distribution of depth values for the input image and evolve the architecture in two ways.
Ranked #35 on
Monocular Depth Estimation
on NYU-Depth V2
SketchGen: Generating Constrained CAD Sketches
Sketches can be represented as graphs, with the primitives as nodes and the constraints as edges.
Self-Supervised Learning of Domain Invariant Features for Depth Estimation
Specifically, we extend self-supervised learning from traditional representation learning, which works on images from a single domain, to domain invariant representation learning, which works on images from two different domains by utilizing an image-to-image translation network.
AdaBins: Depth Estimation using Adaptive Bins
We address the problem of estimating a high quality dense depth map from a single RGB input image.
Ranked #7 on
Depth Estimation
on NYU-Depth V2
