103 citations · 339 across the 19 of their papers we have counts for
36 papers · 1 filter
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
Lewei Yao, Jianhua Han, Youpeng Wen +6
Open-world object detection, as a more general and challenging goal, aims to recognize and localize objects described by arbitrary category names. The recent work GLIP formulates t…
Depth-Aware Generative Adversarial Network for Talking Head Video Generation
Fa-Ting Hong, Longhao Zhang, Li Shen +1
Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving vide…
Multi-class Token Transformer for Weakly Supervised Semantic Segmentation
Lian Xu, Wanli Ouyang, Mohammed Bennamoun +2
This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired…
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks wh…
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved perform…
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
Yongtuo Liu, Sucheng Ren, Liangyu Chai +4
Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to red…