activity
20232026
most citedRTQ: Rethinking Video-language Understanding Based on Image-text Model

14 citations · 20 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2026

COMBINER: Composed Image Retrieval Guided by Attribute-based Neighbor Relations

Zixu Li, Yupeng Hu, Zhiwei Chen +3

Composed Image Retrieval (CIR) represents a challenging retrieval task that targets locating specific images through multimodal inputs. Despite recent progress in CIR techniques, p…

cs.IR2026

STABLE: Efficient Hybrid Nearest Neighbor Search via Magnitude-Uniformity and Cardinality-Robustness

Qianyun Yang, Zhiwei Chen, Yupeng Hu +3

Hybrid Approximate Nearest Neighbor Search (Hybrid ANNS) is a foundational search technology for large-scale heterogeneous data and has gained significant attention in both academi…

cs.CV2025

Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation

Qiaohui Chu, Haoyu Zhang, Meng Liu +3

Long-term action anticipation from egocentric video is critical for applications such as human-computer interaction and assistive technologies, where anticipating user intent enabl…

cs.CV2025

TextSplat: Text-Guided Semantic Fusion for Generalizable Gaussian Splatting

Zhicong Wu, Hongbin Xu, Gang Xu +6

Recent advancements in Generalizable Gaussian Splatting have enabled robust 3D reconstruction from sparse input views by utilizing feed-forward Gaussian Splatting models, achieving…

cs.CV2024

Do Vision-Language Transformers Exhibit Visual Commonsense? An Empirical Study of VCR

Zhenyang Li, Yangyang Guo, Kejie Wang +3

Visual Commonsense Reasoning (VCR) calls for explanatory reasoning behind question answering over visual scenes. To achieve this goal, a model is required to provide an acceptable…

cs.MM2024

Dual Dynamic Threshold Adjustment Strategy for Deep Metric Learning

Xiruo Jiang, Yazhou Yao, Sheng Liu +3

Loss functions and sample mining strategies are essential components in deep metric learning algorithms. However, the existing loss function or mining strategy often necessitate th…

cs.CV2024★ 1 cited

Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation

Gensheng Pei, Yazhou Yao, Jianbo Jiao +3

Conventional video object segmentation (VOS) methods usually necessitate a substantial volume of pixel-level annotated video data for fully supervised learning. In this paper, we p…

cs.CV2024★ 1 cited

MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training

Jiayang Li, Junjun Jiang, Pengwei Liang +2

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on trainin…

cs.CV2024

Spatial Structure Constraints for Weakly Supervised Semantic Segmentation

Tao Chen, Yazhou Yao, Xingguo Huang +3

The image-level label has prevailed in weakly supervised semantic segmentation tasks due to its easy availability. Since image-level labels can only indicate the existence or absen…

cs.CV2023★ 14 cited

RTQ: Rethinking Video-language Understanding Based on Image-text Model

Xiao Wang, Yaoyu Li, Tian Gan +3

Recent advancements in video-language understanding have been established on the foundation of image-text models, resulting in promising outcomes due to the shared knowledge betwee…

cs.CV2023

Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog

Haoyu Zhang, Meng Liu, Yisen Feng +3

In contrast to conventional visual question answering, video-grounded dialog necessitates a profound understanding of both dialog history and video content for accurate response ge…

cs.CV2023★ 1 cited

ELIP: Efficient Discriminative Language-Image Pre-training with Fewer Vision Tokens

Yangyang Guo, Haoyu Zhang, Yongkang Wong +2

Learning a versatile language-image model is computationally prohibitive under a limited computing budget. This paper delves into the \emph{efficient language-image pre-training},…

cs.IR2023★ 3 cited

OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification

Lin Dengtian, Ma Yang, Li Yuhong +3

Illegal live-streaming identification, which aims to help live-streaming platforms immediately recognize the illegal behaviors in the live-streaming, such as selling precious and e…