Search Results for author:
Found 29 papers, 12 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.
DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models
Our extensive experiments demonstrate the superior performance of our method in terms of visual quality, identity preservation, and text control, showcasing its effectiveness in the context of text-guided subject-driven image inpainting.
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.
Learning to Adapt to Online Streams with Distribution Shifts
Test-time adaptation (TTA) is a technique used to reduce distribution gaps between the training and testing sets by leveraging unlabeled test data during inference.
Train-Once-for-All Personalization
We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e. g., for accurately classifying different subsets of objects.
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i. e., skeleton and mesh structure).
Ranked #5 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Multi-Person Human Pose Estimation
3D Multi-Person Pose Estimation
Dir-MUSIC Algorithm for DOA Estimation of Partial Discharge Based on Signal Strength represented by Antenna Gain Array Manifold
The experimental results show that the PD direction-finding error is 3. 39{\deg}, which can meet the need for Partial discharge DOA estimation using inspection robots in substations.
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.
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation
We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size.
MoViNets: Mobile Video Networks for Efficient Video Recognition
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.
Ranked #3 on
Action Classification
on Charades
MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
Many objects do not appear frequently enough in complex scenes (e. g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e. g., in product images).
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
Improving Object Detection with Selective Self-supervised Self-training
They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets.
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model
The other is that the number of images used for the knowledge distillation should be small; otherwise, it violates our expectation of reducing the dependence on large-scale datasets.
BachGAN: High-Resolution Image Synthesis from Salient Object Layout
We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout.
Attacking Lifelong Learning Models with Gradient Reversion
We evaluate the effectiveness of traditional attack methods such as FGSM and PGD. The results show that A-GEM still possesses strong continual learning ability in the presence of adversarial examples in the memory and simple defense techniques such as label smoothing can further alleviate the adversarial effects.
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.
Ranked #25 on
Node Classification
on Pubmed
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques.
NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK
In other words, there is a population of adversarial examples, instead of only one, for any input to a DNN.
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems.
Depthwise Convolution is All You Need for Learning Multiple Visual Domains
A model aware of the relationships between different domains can also be trained to work on new domains with less resources.
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (StNet) architecture for both local and global spatial-temporal modeling in videos.
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
The large volume of video content and high viewing frequency demand automatic video summarization algorithms, of which a key property is the capability of modeling diversity.
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Many seemingly distant annotations (e. g., semantic segmentation and visual question answering (VQA)) are inherently connected in that they reveal different levels and perspectives of human understandings about the same visual scenes --- and even the same set of images (e. g., of COCO).
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification
Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks.
Ranked #163 on
Action Classification
on Kinetics-400
Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place.
