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Found 19 papers, 12 papers with code
TiNO-Edit: Timestep and Noise Optimization for Robust Diffusion-Based Image Editing
Previous approaches have focused on either fine-tuning pre-trained T2I models on specific datasets to generate certain kinds of images (e. g., with a specific object or person), or on optimizing the weights, text prompts, and/or learning features for each input image in an attempt to coax the image generator to produce the desired result.
Deep Appearance Prefiltering
The goal of an ideal level of detail (LoD) method is to make rendering costs independent of the 3D scene complexity, while preserving the appearance of the scene.
Interactive Segmentation and Visualization for Tiny Objects in Multi-megapixel Images
We introduce an interactive image segmentation and visualization framework for identifying, inspecting, and editing tiny objects (just a few pixels wide) in large multi-megapixel high-dynamic-range (HDR) images.
3DVNet: Multi-View Depth Prediction and Volumetric Refinement
Furthermore, unlike existing volumetric MVS techniques, our 3D CNN operates on a feature-augmented point cloud, allowing for effective aggregation of multi-view information and flexible iterative refinement of depth maps.
Ranked #5 on
3D Action Recognition
on NTU RGB+D
VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and Fusion
To this end, we introduce VoRTX, an end-to-end volumetric 3D reconstruction network using transformers for wide-baseline, multi-view feature fusion.
Sparse Fusion for Multimodal Transformers
Multimodal classification is a core task in human-centric machine learning.
Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit
2) We propose a novel loss function and a neural network optimized for telescope imaging data to train generic CR detection models.
Noise-Aware Video Saliency Prediction
We note that the accuracy of the maps reconstructed from the gaze data of a fixed number of observers varies with the frame, as it depends on the content of the scene.
Binary TTC: A Temporal Geofence for Autonomous Navigation
Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene -- even for humans.
Bi3D: Stereo Depth Estimation via Binary Classifications
Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels.
Meshlet Priors for 3D Mesh Reconstruction
Meshlets act as a dictionary of local features and thus allow to use learned priors to reconstruct object meshes in any pose and from unseen classes, even when the noise is large and the samples sparse.
PieAPP: Perceptual Image-Error Assessment through Pairwise Preference
Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels.
Ranked #1 on
Video Quality Assessment
on MSU SR-QA Dataset
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images
Image hallucination and super-resolution have been studied for decades, and many approaches have been proposed to upsample low-resolution images using information from the images themselves, multiple example images, or large image databases.
Improving the Resolution of CNN Feature Maps Efficiently with Multisampling
We describe a new class of subsampling techniques for CNNs, termed multisampling, that significantly increases the amount of information kept by feature maps through subsampling layers.
Localization-Aware Active Learning for Object Detection
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification.
GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion
We present GraphMatch, an approximate yet efficient method for building the matching graph for large-scale structure-from-motion (SfM) pipelines.
ANSAC: Adaptive Non-minimal Sample and Consensus
While RANSAC-based methods are robust to incorrect image correspondences (outliers), their hypothesis generators are not robust to correct image correspondences (inliers) with positional error (noise).
Kernel-predicting convolutional networks for denoising monte carlo renderings.
In a second approach, we introduce a novel, kernel-prediction network which uses the CNN to estimate the local weighting kernels used to compute each denoised pixel from its neighbors.
Removing Shadows from Images of Documents
In this work, we automatically detect and remove distracting shadows from photographs of documents and other text-based items.
