212 citations · 838 across the 55 of their papers we have counts for
11 papers · 2 filters
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…