4 citations · 4 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…
Structured Context Transformer for Generic Event Boundary Detection
Congcong Li, Xinyao Wang, Dexiang Hong +4
Generic Event Boundary Detection (GEBD) aims to detect moments where humans naturally perceive as event boundaries. In this paper, we present Structured Context Transformer (or SC-…