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Found 30 papers, 11 papers with code
$\texttt{Complex-Edit}$: CoT-Like Instruction Generation for Complexity-Controllable Image Editing Benchmark
We introduce $\texttt{Complex-Edit}$, a comprehensive benchmark designed to systematically evaluate instruction-based image editing models across instructions of varying complexity.
D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition
We demonstrate that our proposed use of diffusion-based features results in models that are more robust to partial object occlusions for both Transformers and ConvNets on ImageNet with simulated occlusions.
M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation
In this paper, we show that this scale-wise autoregressive framework can be effectively decoupled into \textit{intra-scale modeling}, which captures local spatial dependencies within each scale, and \textit{inter-scale modeling}, which models cross-scale relationships progressively from coarse-to-fine scales.
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
In this paper, we present ReCapture, a method for generating new videos with novel camera trajectories from a single user-provided video.
Unbounded: A Generative Infinite Game of Character Life Simulation
We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models.
Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
Although Diffusion Models (DMs) have recently dominated the field of generative modeling for images, their inversion presents faithfulness and editability challenges due to nonlinearities in drift and diffusion.
Magic Insert: Style-Aware Drag-and-Drop
This work formalizes the problem of style-aware drag-and-drop and presents a method for tackling it by addressing two sub-problems: style-aware personalization and realistic object insertion in stylized images.
RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content.
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize.
RealFill: Reference-Driven Generation for Authentic Image Completion
Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene.
Platypus: Quick, Cheap, and Powerful Refinement of LLMs
We present $\textbf{Platypus}$, a family of fine-tuned and merged Large Language Models (LLMs) that achieves the strongest performance and currently stands at first place in HuggingFace's Open LLM Leaderboard as of the release date of this work.
HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications.
Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
We hypothesize that this power to ignore out-of-context information (which we name $\textit{patch selectivity}$), while integrating in-context information in a non-local manner in early layers, allows ViTs to more easily handle occlusion.
StyleDrop: Text-to-Image Generation in Any Style
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.
DreamBooth3D: Subject-Driven Text-to-3D Generation
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject.
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness issues with respect to specific scene variations.
Human Body Measurement Estimation with Adversarial Augmentation
Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes.
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Once the subject is embedded in the output domain of the model, the unique identifier can be used to synthesize novel photorealistic images of the subject contextualized in different scenes.
Ranked #1 on
Personalized Image Generation
on DreamBooth
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
We also show how the $\ell_2$ norm and other metrics do not correlate with human perceptibility in a linear fashion, thus making these norms suboptimal at measuring adversarial attack perceptibility.
Simulated Adversarial Testing of Face Recognition Models
In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios.
MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias
Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression.
Protecting Against Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks
In this work, we develop efficient disruptions of black-box image translation deepfake generation systems.
Detecting Attended Visual Targets in Video
We address the problem of detecting attention targets in video.
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems
This type of manipulated images and video have been coined Deepfakes.
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
Learning To Simulate
Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire.
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions
Automatic generation of textual video descriptions that are time-aligned with video content is a long-standing goal in computer vision.
Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency
This paper addresses the challenging problem of estimating the general visual attention of people in images.
Fine-Grained Head Pose Estimation Without Keypoints
Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.
Ranked #5 on
Head Pose Estimation
on AFLW
Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
Face detection is a very important task and a necessary pre-processing step for many applications such as facial landmark detection, pose estimation, sentiment analysis and face recognition.
