Search Results for author:
Found 42 papers, 24 papers with code
DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation
Our key idea is to investigate and identify a space, namely delta image and text space that has well-aligned distribution between CLIP visual feature differences of two images and CLIP textual embedding differences of source and target texts.
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution
To this end, we construct a large-scale, multi-reference super-resolution dataset, named LMR.
Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation
Creating the photo-realistic version of people sketched portraits is useful to various entertainment purposes.
AdaCM: Adaptive ColorMLP for Real-Time Universal Photo-realistic Style Transfer
Then, in \textbf{AdaCM}, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair.
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage.
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection
While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as.
It Takes Two: Masked Appearance-Motion Modeling for Self-supervised Video Transformer Pre-training
In order to guide the encoder to fully excavate spatial-temporal features, two separate decoders are used for two pretext tasks of disentangled appearance and motion prediction.
Effective Invertible Arbitrary Image Rescaling
To increase its real world applicability, numerous models have also been proposed to restore SR images with arbitrary scale factors, including asymmetric ones where images are resized to different scales along horizontal and vertical directions.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval
We introduce dynamic dictionaries for both modalities to enlarge the scale of image-text pairs, and diversity-sensitiveness is achieved by adaptive negative pair weighting.
Boosting Video-Text Retrieval with Explicit High-Level Semantics
Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video).
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition
On the video level, a temporal attention module is learned under dual video-level supervisions on both the salient and the non-salient representations.
Ranked #3 on
Action Recognition
on ActivityNet
Neural Color Operators for Sequential Image Retouching
The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar.
DALG: Deep Attentive Local and Global Modeling for Image Retrieval
Deeply learned representations have achieved superior image retrieval performance in a retrieve-then-rerank manner.
Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation
The perturbations for unlabeled data enable the consistency training loss, which benefits semi-supervised semantic segmentation.
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence
Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and downscaling degradation kernel.
Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model
We propose a novel framework, i. e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image manipulation that requires little manual annotation while being applicable to a wide variety of manipulations.
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks.
Ranked #1 on
Object Detection
on A2D
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics.
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
Components orthogonal to the global image representation are then extracted from the local information.
Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings
In this paper, we present a fast exemplar-based image colorization approach using color embeddings named Color2Embed.
ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency
Specifically, we propose two tasks to learn the appearance and speed consistency, respectively.
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition.
Ranked #5 on
Action Recognition
on ActivityNet
Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness
Inpainting arbitrary missing regions is challenging because learning valid features for various masked regions is nontrivial.
Ranked #3 on
Image Inpainting
on CelebA-HQ
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Learning Semantic Person Image Generation by Region-Adaptive Normalization
In the first stage, we predict the target semantic parsing maps to eliminate the difficulties of pose transfer and further benefit the latter translation of per-region appearance style.
Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer
Inspired by the common painting process of drawing a draft and revising the details, we introduce a novel feed-forward method named Laplacian Pyramid Network (LapStyle).
Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones
Recently, research efforts have been concentrated on revealing how pre-trained model makes a difference in neural network performance.
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in performance.
Ranked #27 on
Action Recognition
on Something-Something V1
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition.
Ranked #7 on
Self-Supervised Action Recognition
on UCF101
HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network
Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications.
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition
In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module.
Multi-Label Classification with Label Graph Superimposing
In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects.
Ranked #24 on
Multi-Label Classification
on MS-COCO
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation
In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story.
Deep Concept-wise Temporal Convolutional Networks for Action Localization
In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution.
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition
Video Recognition has drawn great research interest and great progress has been made.
Ranked #6 on
Action Recognition
on ActivityNet
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks.
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos.
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.
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition
In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding.
