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Works66 from public data
- AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer456
A novel attention and normalization module, named Adaptive Attention Normalization (AdaAttN), to adaptively perform attentive normalization on per-point basis is proposed, which achieves state-of-the-art arbitrary image/video style transfer.
- Multi-Label Classification with Label Graph Superimposing206
A label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects and to leverage embedding of the whole label system for better representation learning.
- Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos173
A reinforcement learning based framework improved by multi-task learning is proposed which achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset and Charades-STA dataset while observing only 10 or less clips per video.
- DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features148
This paper proposes a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval that achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.
- StNet: Local and Global Spatial-Temporal Modeling for Action Recognition146
A novel spatial temporal network (StNet) architecture for both local and global spatial-temporal modeling in videos that outperforms several state-of-the-art approaches in action recognition and can strike a satisfying trade-off between recognition accuracy and model complexity is explored.
- RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning143
It is observed that the relative playback speed is more consistent with motion patterns and thus provides more effective and stable supervision for representation learning, and a new way to perceive the playback speed and exploit the relative speed between two video clips as labels is proposed.
- Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition139
This work focuses on improving untrimmed video classification via developing a learning-based frame sampling strategy and proposes to solve the problems with multi-agent reinforcement learning (MARL), a novel RNN-based context-aware observation network which jointly models context information among nearby agents.
- Real-Time Neural Radiance Talking Portrait Synthesis via Audio-Spatial Decomposition137
An efficient NeRF-based framework is proposed, which enables faster convergence and real-time synthesizing of stable talking portraits, by utilizing the recent success of grid-based NeRF.
- StyleSync: High-Fidelity Generalized and Personalized Lip Sync in Style-Based Generator125
This paper designs a mask-guided spatial information encoding module that preserves the details of the given face and enables personalized lip-sync by introducing style space and generator refinement on only limited frames, so the identity and talking style of a target person could be accurately preserved.
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- Paint Transformer: Feed Forward Neural Painting with Stroke Prediction112
This paper formulate the task as a set prediction problem and proposes a novel Transformer-based framework, dubbed Paint Transformer, to predict the parameters of a stroke set with a feed forward network, which achieves better painting performance than previous ones with cheaper training and inference costs.
- Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer111
Inspired by the common painting process of drawing a draft and revising the details, a novel feed-forward method named Laplacian Pyramid Network (LapStyle) is introduced that can synthesize high quality stylized images in real time, where holistic style patterns are properly transferred.
- MVFNet: Multi-View Fusion Network for Efficient Video Recognition94
A novel multi-view fusion (MVF) module is introduced to exploit video dynamics using separable convolution for efficiency and the proposed MVFNet can achieve state-of-the-art performance with 2D CNN's complexity.
- Learning Semantic Person Image Generation by Region-Adaptive Normalization88
This work proposes a new two-stage framework to handle the pose and appearance translation and suggests a new person image generation method by incorporating the region-adaptive normalization, in which it takes the per-region styles to guide the target appearance generation.
- ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency56
It is observed that the consistency between positive samples is the key to learn robust video representations and two tasks to learn appearance and speed consistency are proposed, separately.
- NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results54
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single image super- resolution.
- Multi-camera vehicle tracking and re-identification based on visual and spatial-temporal features54
A novel framework for multi-camera tracking, which integrates visual features, orientation prediction and temporal-spatial information of the trajectories for optimization is proposed, which achieves the first place in vehicle re-identification at the NVIDIA AI City Challenge 2019.
- A CNN-Based Approach for Automatic License Plate Recognition in the Wild54
This paper addresses automatic license plate recognition (ALPR) in the wild with a cascade structure comprising of a fast region proposal network and a R-CNN network, and proposes an innovative structure composed of parallel spatial transform networks and shared-weight recognizers.
- Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model49
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 ma-nipulations.
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- NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results43
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results, and gauge the state of the art of video quality enhancement.
- Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence42
The proposed method is the first to treat arbitrary rescaling, both upscaling and downscaling, as one unified process, and is able to learn up Scaling and Downscaling simultaneously and achieve bidirectional arbitrary image rescaling.
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- Dynamic Inference: A New Approach Toward Efficient Video Action Recognition37
This paper innovatively proposes a general dynamic inference idea to improve inference efficiency by leveraging the variation in the distinguishability of different videos to alleviate the conflict between progressive computation and video temporal relation modeling by introducing the online temporal shift module.
- NTIRE 2021 Challenge on Video Super-Resolution36
This paper presents evaluation results from two competition tracks as well as the proposed solutions to the NTIRE 2021 Challenge on Video Super-Resolution, and develops conventional video SR methods focusing on the restoration quality.
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- LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution32
A new baseline method for multi-reference super-resolution is proposed: MRefSR, including a Multi-Reference Attention Module (MAM) for feature fusion of an arbitrary number of reference images, and a Spatial Aware Filtering Module (SAFM) for the fused feature selection.
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- Adversarial Dual-Student With Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation30
A context friendly differentiable geometric warping to conduct unsupervised data augmentation and a novel adversarial dual-student framework is proposed to improve the Mean-Teacher from the following two aspects.
- Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer29
A novel Transformer model termed as Master specifically for style transfer is devised so that it can not only work in the typical setting of arbitrary style transfer, but also adaptable to the few-shot setting, by only fine-tuning the Transformer encoder layer in the many-shot stage for one specific style.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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