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Found 29 papers, 15 papers with code
Are Pose Estimators Ready for the Open World? STAGE: Synthetic Data Generation Toolkit for Auditing 3D Human Pose Estimators
The estimation of 3D human poses from images has progressed tremendously over the last few years as measured on standard benchmarks.
Domain-Aware Fine-Tuning of Foundation Models
Foundation models (FMs) have revolutionized computer vision, enabling effective learning across different domains.
Label-free Neural Semantic Image Synthesis
Neural layouts are advantageous as they provide rich descriptions of the desired image, containing both semantics and detailed geometry of the scene.
ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections
Parameter-efficient finetuning (PEFT) has become ubiquitous to adapt foundation models to downstream task requirements while retaining their generalization ability.
VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis
Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content.
Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive
Current L2I models either suffer from poor editability via text or weak alignment between the generated image and the input layout.
Divide & Bind Your Attention for Improved Generative Semantic Nursing
To address the challenges posed by complex prompts or scenarios involving multiple entities and to achieve improved attribute binding, we propose Divide & Bind.
Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization
Using the proposed masked noise encoder to randomize style and content combinations in the training set, i. e., intra-source style augmentation (ISSA) effectively increases the diversity of training data and reduces spurious correlation.
Discovering Class-Specific GAN Controls for Semantic Image Synthesis
Prior work has extensively studied the latent space structure of GANs for unconditional image synthesis, enabling global editing of generated images by the unsupervised discovery of interpretable latent directions.
Intra-Source Style Augmentation for Improved Domain Generalization
Using the proposed masked noise encoder to randomize style and content combinations in the training set, ISSA effectively increases the diversity of training data and reduces spurious correlation.
One-Shot Synthesis of Images and Segmentation Masks
To this end, inspired by the recent architectural developments of single-image GANs, we introduce our OSMIS model which enables the synthesis of segmentation masks that are precisely aligned to the generated images in the one-shot regime.
Learning to Generate Novel Scene Compositions from Single Images and Videos
Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence.
Generating Novel Scene Compositions from Single Images and Videos
In this work, we introduce SIV-GAN, an unconditional generative model that can generate new scene compositions from a single training image or a single video clip.
You Only Need Adversarial Supervision for Semantic Image Synthesis
By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous.
Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis
We therefore propose to improve the established semantic image synthesis evaluation scheme by analyzing separately the performance of generated images on the biased and unbiased classes for the given segmentation network.
Ranked #5 on
Image-to-Image Translation
on ADE20K Labels-to-Photos
(Accuracy metric)
A U-Net Based Discriminator for Generative Adversarial Networks
The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism.
Ranked #1 on
Image Generation
on CelebA 128x128
Grid Saliency for Context Explanations of Semantic Segmentation
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions.
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
An environment representation (ER) is a substantial part of every autonomous system.
Progressive Augmentation of GANs
Training of Generative Adversarial Networks (GANs) is notoriously fragile, requiring to maintain a careful balance between the generator and the discriminator in order to perform well.
Ranked #3 on
Image Generation
on CelebA-HQ 128x128
PA-GAN: Improving GAN Training by Progressive Augmentation
Despite recent progress, Generative Adversarial Networks (GANs) still suffer from training instability, requiring careful consideration of architecture design choices and hyper-parameter tuning.
Learning to Refine Human Pose Estimation
Multi-person pose estimation in images and videos is an important yet challenging task with many applications.
Video Object Segmentation with Language Referring Expressions
We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on DAVIS'16 and is competitive to methods using scribbles on the challenging DAVIS'17 dataset.
Ranked #1 on
Video Object Segmentation
on DAVIS 2017
(mIoU metric)
Lucid Data Dreaming for Video Object Segmentation
Our approach is suitable for both single and multiple object segmentation.
Exploiting saliency for object segmentation from image level labels
We show how to combine both information sources in order to recover 80% of the fully supervised performance - which is the new state of the art in weakly supervised training for pixel-wise semantic labelling.
Ranked #26 on
Semantic Segmentation
on PASCAL VOC 2012 val
Learning Video Object Segmentation from Static Images
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation.
Ranked #6 on
Semi-Supervised Video Object Segmentation
on YouTube
Improved Image Boundaries for Better Video Segmentation
Graph-based video segmentation methods rely on superpixels as starting point.
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2012 val
(Mean IoU metric)
Weakly Supervised Object Boundaries
State-of-the-art learning based boundary detection methods require extensive training data.
Ranked #2 on
Edge Detection
on SBD
Classifier Based Graph Construction for Video Segmentation
Video segmentation has become an important and active research area with a large diversity of proposed approaches.
