17 citations · 68 across the 25 of their papers we have counts for
19 papers · 1 filter
Thinking in Streaming Video
Zikang Liu, Longteng Guo, Handong Li +7
Real-time understanding of continuous video streams is essential for interactive assistants and multimodal agents operating in dynamic environments. However, most existing video re…
Towards Unified Referring Expression Segmentation Across Omni-Level Visual Target Granularities
Jing Liu, Wenxuan Wang, Yisi Zhang +5
Referring expression segmentation (RES) aims at segmenting the entities' masks that match the descriptive language expression. While traditional RES methods primarily address objec…
MiniVLN: Efficient Vision-and-Language Navigation by Progressive Knowledge Distillation
Junyou Zhu, Yanyuan Qiao, Siqi Zhang +3
In recent years, Embodied Artificial Intelligence (Embodied AI) has advanced rapidly, yet the increasing size of models conflicts with the limited computational capabilities of Emb…
The Instance-centric Transformer for the RVOS Track of LSVOS Challenge: 3rd Place Solution
Bin Cao, Yisi Zhang, Hanyi Wang +2
Referring Video Object Segmentation is an emerging multi-modal task that aims to segment objects in the video given a natural language expression. In this work, we build two instan…
2nd Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation
Bin Cao, Yisi Zhang, Xuanxu Lin +3
Motion Expression guided Video Segmentation is a challenging task that aims at segmenting objects in the video based on natural language expressions with motion descriptions. Unlik…
Fuse & Calibrate: A bi-directional Vision-Language Guided Framework for Referring Image Segmentation
Yichen Yan, Xingjian He, Sihan Chen +2
Referring Image Segmentation (RIS) aims to segment an object described in natural language from an image, with the main challenge being a text-to-pixel correlation. Previous method…
Calibration & Reconstruction: Deep Integrated Language for Referring Image Segmentation
Yichen Yan, Xingjian He, Sihan Chen +1
Referring image segmentation aims to segment an object referred to by natural language expression from an image. The primary challenge lies in the efficient propagation of fine-gra…
SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models
Tongtian Yue, Jie Cheng, Longteng Guo +6
Recent trends in Large Vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level re…
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions
Wenxuan Wang, Yisi Zhang, Xingjian He +4
Visual grounding (VG) aims at locating the foreground entities that match the given natural language expressions. Previous datasets and methods for classic VG task mainly rely on t…
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…
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning
Zijia Zhao, Longteng Guo, Xingjian He +3
Multimodal representation learning has shown promising improvements on various vision-language tasks. Most existing methods excel at building global-level alignment between vision…
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing
Xingjian He, Weining Wang, Zhiyong Xu +3
Compared with image scene parsing, video scene parsing introduces temporal information, which can effectively improve the consistency and accuracy of prediction. In this paper, we…
Global-Local Propagation Network for RGB-D Semantic Segmentation
Sihan Chen, Xinxin Zhu, Wei Liu +2
Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images. Most existing methods exploit a multi-stage fusion str…