6 citations · 9 across the 12 of their papers we have counts for
15 papers
SeaVIS: Sound-Enhanced Association for Online Audio-Visual Instance Segmentation
Yingjian Zhu, Ying Wang, Yuyang Hong +5
Recently, an audio-visual instance segmentation (AVIS) task has been introduced, aiming to identify, segment and track individual sounding instances in videos. However, prevailing…
Towards Long-Form Spatio-Temporal Video Grounding
Xin Gu, Bing Fan, Jiali Yao +5
In real scenarios, videos can span several minutes or even hours. However, existing research on spatio-temporal video grounding (STVG), given a textual query, mainly focuses on loc…
Structured Context Learning for Generic Event Boundary Detection
Xin Gu, Congcong Li, Xinyao Wang +5
Generic Event Boundary Detection (GEBD) aims to identify moments in videos that humans perceive as event boundaries. This paper proposes a novel method for addressing this task, ca…
Thinking With Bounding Boxes: Enhancing Spatio-Temporal Video Grounding via Reinforcement Fine-Tuning
Xin Gu, Haoji Zhang, Qihang Fan +7
Spatio-temporal video grounding (STVG) requires localizing a target object in untrimmed videos both temporally and spatially from natural language descriptions. Despite their stron…
Robust Ego-Exo Correspondence with Long-Term Memory
Yijun Hu, Bing Fan, Xin Gu +4
Establishing object-level correspondence between egocentric and exocentric views is essential for intelligent assistants to deliver precise and intuitive visual guidance. However,…
SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based Image Editing
Ming Li, Xin Gu, Fan Chen +4
Due to the challenges of manually collecting accurate editing data, existing datasets are typically constructed using various automated methods, leading to noisy supervision signal…
Vidi: Large Multimodal Models for Video Understanding and Editing
Vidi Team, Celong Liu, Chia-Wen Kuo +20
Humans naturally share information with those they are connected to, and video has become one of the dominant mediums for communication and expression on the Internet. To support t…
OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding
Jiali Yao, Xinran Deng, Xin Gu +6
In this paper, we propose spatio-temporal omni-object video grounding, dubbed OmniSTVG, a new STVG task that aims at localizing spatially and temporally all targets mentioned in th…
Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding
Xin Gu, Yaojie Shen, Chenxi Luo +5
Transformer has attracted increasing interest in STVG, owing to its end-to-end pipeline and promising result. Existing Transformer-based STVG approaches often leverage a set of obj…
Multi-Reward as Condition for Instruction-based Image Editing
Xin Gu, Ming Li, Libo Zhang +4
High-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are c…
Edit3K: Universal Representation Learning for Video Editing Components
Xin Gu, Libo Zhang, Fan Chen +4
This paper focuses on understanding the predominant video creation pipeline, i.e., compositional video editing with six main types of editing components, including video effects, a…
Context-Guided Spatio-Temporal Video Grounding
Xin Gu, Heng Fan, Yan Huang +2
Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements, current methods easily suff…
Local Compressed Video Stream Learning for Generic Event Boundary Detection
Libo Zhang, Xin Gu, Congcong Li +2
Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be…
Accurate and Fast Compressed Video Captioning
Yaojie Shen, Xin Gu, Kai Xu +3
Existing video captioning approaches typically require to first sample video frames from a decoded video and then conduct a subsequent process (e.g., feature extraction and/or capt…
Text with Knowledge Graph Augmented Transformer for Video Captioning
Xin Gu, Guang Chen, Yufei Wang +3
Video captioning aims to describe the content of videos using natural language. Although significant progress has been made, there is still much room to improve the performance for…