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Found 17 papers, 1 papers with code
Instruct-Imagen: Image Generation with Multi-modal Instruction
We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision.
Fine-grained Controllable Video Generation via Object Appearance and Context
To achieve detailed control, we propose a unified framework to jointly inject control signals into the existing text-to-video model.
Alchemist: Parametric Control of Material Properties with Diffusion Models
We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images.
Controllable One-Shot Face Video Synthesis With Semantic Aware Prior
The one-shot talking-head synthesis task aims to animate a source image to another pose and expression, which is dictated by a driving frame.
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
FedBasis learns a set of few shareable ``basis'' models, which can be linearly combined to form personalized models for clients.
Identity Encoder for Personalized Diffusion
Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications.
What's in a Name? Beyond Class Indices for Image Recognition
In this paper, we reconsider the recognition problem and task a vision-language model to assign class names to images given only a large and essentially unconstrained vocabulary of categories as prior information.
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models
This paper proposes a method for generating images of customized objects specified by users.
Rethinking Deep Face Restoration
While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces.
Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data
This paper studies the problem of novel category discovery on single- and multi-modal data with labels from different but relevant categories.
Global Self-Attention Networks
Based on the proposed GSA module, we introduce new standalone global attention-based deep networks that use GSA modules instead of convolutions to model pixel interactions.
A Flexible Framework for Discovering Novel Categories with Contrastive Learning
This paper studies the problem of novel category discovery on single- and multi-modal data with labels from different but relevant categories.
Ranking Neural Checkpoints
This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task.
Ranked #6 on
Transferability
on classification benchmark
Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze
In this work, we focus on the task of image-based mutual gaze detection, and propose a simple and effective approach to boost the performance by using an auxiliary 3D gaze estimation task during the training phase.
Global Self-Attention Networks for Image Recognition
Based on the proposed GSA module, we introduce new standalone global attention-based deep networks that use GSA modules instead of convolutions to model pixel interactions.
Search to Distill: Pearls are Everywhere but not the Eyes
Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture.
An Empirical Study of Recent Face Alignment Methods
In this paper, we carry out a rigorous evaluation of these methods by making the following contributions: 1) we proposes a new evaluation metric for face alignment on a set of images, i. e., area under error distribution curve within a threshold, AUC$_\alpha$, given the fact that the traditional evaluation measure (mean error) is very sensitive to big alignment error.
