17 citations · 68 across the 25 of their papers we have counts for
8 papers · 1 filter
Unveiling Parts Beyond Objects:Towards Finer-Granularity Referring Expression Segmentation
Wenxuan Wang, Tongtian Yue, Yisi Zhang +4
Referring expression segmentation (RES) aims at segmenting the foreground masks of the entities that match the descriptive natural language expression. Previous datasets and method…
EAVL: Explicitly Align Vision and Language for Referring Image Segmentation
Yichen Yan, Xingjian He, Wenxuan Wang +2
Referring image segmentation (RIS) aims to segment an object mentioned in natural language from an image. The main challenge is text-to-pixel fine-grained correlation. In the previ…
COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
Sihan Chen, Xingjian He, Handong Li +3
Due to the limited scale and quality of video-text training corpus, most vision-language foundation models employ image-text datasets for pretraining and primarily focus on modelin…
MMNet: Multi-Mask Network for Referring Image Segmentation
Yichen Yan, Xingjian He, Wenxuan Wan +1
Referring image segmentation aims to segment an object referred to by natural language expression from an image. However, this task is challenging due to the distinct data properti…
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Xingjian He, Sihan Chen, Fan Ma +7
Large-scale image-text contrastive pre-training models, such as CLIP, have been demonstrated to effectively learn high-quality multimodal representations. However, there is limited…
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner
Zikang Liu, Sihan Chen, Longteng Guo +3
Large pre-trained multimodal models have demonstrated significant success in a range of downstream tasks, including image captioning, image-text retrieval, visual question answerin…
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation
Wenxuan Wang, Jing Liu, Xingjian He +5
Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression. Due to…
VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset
Jing Liu, Sihan Chen, Xingjian He +4
In this paper, we propose a Vision-Audio-Language Omni-peRception pretraining model (VALOR) for multi-modal understanding and generation. Different from widely-studied vision-langu…