212 citations · 838 across the 55 of their papers we have counts for
56 papers · 1 filter
Few-shot Action Recognition with Captioning Foundation Models
Xiang Wang, Shiwei Zhang, Hangjie Yuan +4
Transferring vision-language knowledge from pretrained multimodal foundation models to various downstream tasks is a promising direction. However, most current few-shot action reco…
Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
Feng Zhang, Ming Tian, Zhiqiang Li +4
Tone mapping aims to convert high dynamic range (HDR) images to low dynamic range (LDR) representations, a critical task in the camera imaging pipeline. In recent years, 3-Dimensio…
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +4
Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to…
Towards General Low-Light Raw Noise Synthesis and Modeling
Feng Zhang, Bin Xu, Zhiqiang Li +4
Modeling and synthesizing low-light raw noise is a fundamental problem for computational photography and image processing applications. Although most recent works have adopted phys…
HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation
Huaxin Zhang, Xiang Wang, Xiaohao Xu +3
Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the networ…
PLIP: Language-Image Pre-training for Person Representation Learning
Jialong Zuo, Jiahao Hong, Feng Zhang +5
Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, th…
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Zhiwu Qing +4
Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they gener…
CLIP-guided Prototype Modulating for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Jun Cen +4
Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explore…
Semantic Segmentation via Pixel-to-Center Similarity Calculation
Dongyue Wu, Zilin Guo, Aoyan Li +3
Since the fully convolutional network has achieved great success in semantic segmentation, lots of works have been proposed focusing on extracting discriminative pixel feature repr…
Parallel Reasoning Network for Human-Object Interaction Detection
Huan Peng, Fenggang Liu, Yangguang Li +4
Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and thei…
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Zhiwu Qing +4
Recent attempts mainly focus on learning deep representations for each video individually under the episodic meta-learning regime and then performing temporal alignment to match qu…
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
Yixuan Pei, Zhiwu Qing, Jun Cen +6
Recent incremental learning for action recognition usually stores representative videos to mitigate catastrophic forgetting. However, only a few bulky videos can be stored due to t…
MAR: Masked Autoencoders for Efficient Action Recognition
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +5
Standard approaches for video recognition usually operate on the full input videos, which is inefficient due to the widely present spatio-temporal redundancy in videos. Recent prog…
Context-aware Proposal Network for Temporal Action Detection
Xiang Wang, Huaxin Zhang, Shiwei Zhang +3
This technical report presents our first place winning solution for temporal action detection task in CVPR-2022 AcitivityNet Challenge. The task aims to localize temporal boundarie…
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
Xiang Wang, Shiwei Zhang, Zhiwu Qing +5
Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal ali…
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency
Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +6
Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos,…
Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting
Fusen Wang, Kai Liu, Fei Long +3
Currently, for crowd counting, the fully supervised methods via density map estimation are the mainstream research directions. However, such methods need location-level annotation…
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID
Yuhang Wu, Tengteng Huang, Haotian Yao +5
Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a u…
Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval
Zongheng Huang, YiFan Sun, Chuchu Han +2
This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. This task has two characteristics: 1) the zero…
Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior
Feng Zhang, Yuanjie Shao, Yishi Sun +3
Deep learning-based methods for low-light image enhancement typically require enormous paired training data, which are impractical to capture in real-world scenarios. Recently, uns…
Implicit Neural Deformation for Sparse-View Face Reconstruction
Moran Li, Haibin Huang, Yi Zheng +3
In this work, we present a new method for 3D face reconstruction from sparse-view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited…
Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation
Rui Wang, Jian Chen, Gang Yu +4
Image manipulation with StyleGAN has been an increasing concern in recent years.Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit th…
CondNet: Conditional Classifier for Scene Segmentation
Changqian Yu, Yuanjie Shao, Changxin Gao +1
The fully convolutional network (FCN) has achieved tremendous success in dense visual recognition tasks, such as scene segmentation. The last layer of FCN is typically a global cla…
Weakly Supervised Person Search with Region Siamese Networks
Chuchu Han, Kai Su, Dongdong Yu +5
Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. Large-scale labeled training data is often difficult to colle…