Search Results for author:
Found 42 papers, 30 papers with code
SDM: Spatial Diffusion Model for Large Hole Image Inpainting
Generative adversarial networks (GANs) have made great success in image inpainting yet still have difficulties tackling large missing regions.
Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space Viewpoint
In this work, we propose to first obtain the precise latent code in foundation latent space $\mathcal{W}$.
DiffusionDet: Diffusion Model for Object Detection
In inference, the model refines a set of randomly generated boxes to the output results in a progressive way.
One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations
Based on the visual latent space of StyleGAN[21] and text embedding space of CLIP[34], studies focus on how to map these two latent spaces for text-driven attribute manipulations.
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
To address this challenge, we propose an effective adaptation approach for Transformer, namely AdaptFormer, which can adapt the pre-trained ViTs into many different image and video tasks efficiently.
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets.
Ranked #3 on
Action Recognition
on AVA v2.2
(using extra training data)
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Following this principle, we propose to enrich the "diversity" of forgeries by synthesizing augmented forgeries with a pool of forgery configurations and strengthen the "sensitivity" to the forgeries by enforcing the model to predict the forgery configurations.
Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
Second, by maintaining the same computational cost, our method empowers ViTs to take more image tokens as input for recognition accuracy improvement, where the image tokens are from higher resolution images.
DynaMixer: A Vision MLP Architecture with Dynamic Mixing
In contrast with vision transformers and CNNs, the success of MLP-like models shows that simple information fusion operations among tokens and channels can yield a good representation power for deep recognition models.
MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning
Dancing video retargeting aims to synthesize a video that transfers the dance movements from a source video to a target person.
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning
Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning
Motivated by the transformers that explore visual attention effectively in recognition scenarios, we propose a CNN Attention REvitalization (CARE) framework to train attentive CNN encoders guided by transformers in SSL.
Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learning
Motivated by the transformers that explore visual attention effectively in recognition scenarios, we propose a CNN Attention REvitalization (CARE) framework to train attentive CNN encoders guided by transformers in SSL.
EViT: Expediting Vision Transformers via Token Reorganizations
Second, by maintaining the same computational cost, our method empowers ViTs to take more image tokens as input for recognition accuracy improvement, where the image tokens are from higher resolution images.
PD-GAN: Probabilistic Diverse GAN for Image Inpainting
To this end, we propose spatially probabilistic diversity normalization (SPDNorm) inside the modulation to model the probability of generating a pixel conditioned on the context information.
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
The forward inference projects input images into deep features, while the backward inference remaps deep features back to input images in a lossless and unbiased way.
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking
Recently, adversarial attack has been applied to visual object tracking to evaluate the robustness of deep trackers.
DeFLOCNet: Deep Image Editing via Flexible Low-level Controls
While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content.
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On
To this end, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning.
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples
By empowering the temporal robustness of the encoder and modeling the temporal decay of the keys, our VideoMoCo improves MoCo temporally based on contrastive learning.
Ranked #72 on
Action Recognition
on HMDB-51
Stabilized Medical Image Attacks
However, a threat to these systems arises that adversarial attacks make CNNs vulnerable.
Parser-Free Virtual Try-on via Distilling Appearance Flows
A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model.
Stabilized Medical Attacks
We further analyze the KL-divergence of the proposed loss function and find that the loss stabilization term makes the perturbations updated towards a fixed objective spot while deviating from the ground truth.
Rethinking Image Deraining via Rain Streaks and Vapors
Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light.
Unsupervised Deep Representation Learning for Real-Time Tracking
The advancement of visual tracking has continuously been brought by deep learning models.
Robust Tracking against Adversarial Attacks
On one hand, we add the temporal perturbations into the original video sequences as adversarial examples to greatly degrade the tracking performance.
Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations
We use CNN features from the deep and shallow layers of the encoder to represent structures and textures of an input image, respectively.
Self-supervised Learning of Detailed 3D Face Reconstruction
The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage.
Real-Time Correlation Tracking via Joint Model Compression and Transfer
In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network.
MVF-Net: Multi-View 3D Face Morphable Model Regression
The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred.
Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement
We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image.
Deep Attentive Tracking via Reciprocative Learning
Visual attention, derived from cognitive neuroscience, facilitates human perception on the most pertinent subset of the sensory data.
Deformable Object Tracking with Gated Fusion
In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss
Monocular depth estimation benefits greatly from learning based techniques.
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks
The proposed network is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN).
Ranked #7 on
Deblurring
on RealBlur-R (trained on GoPro)
(SSIM (sRGB) metric)
VITAL: VIsual Tracking via Adversarial Learning
To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.
Image Correction via Deep Reciprocating HDR Transformation
Given an input LDR image, we first reconstruct the missing details in the HDR domain.
Stylizing Face Images via Multiple Exemplars
We address the problem of transferring the style of a headshot photo to face images.
CREST: Convolutional Residual Learning for Visual Tracking
Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training.
Learning to Hallucinate Face Images via Component Generation and Enhancement
We propose a two-stage method for face hallucination.
Fast Preprocessing for Robust Face Sketch Synthesis
Exemplar-based face sketch synthesis methods usually meet the challenging problem that input photos are captured in different lighting conditions from training photos.
