Search Results for author: Zesen Cheng

Found 14 papers, 6 papers with code

GraCo: Granularity-Controllable Interactive Segmentation

no code implementations1 May 2024 Yian Zhao, Kehan Li, Zesen Cheng, Pengchong Qiao, Xiawu Zheng, Rongrong Ji, Chang Liu, Li Yuan, Jie Chen

In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing additional parameters to input.

Interactive Segmentation Segmentation

Instance Brownian Bridge as Texts for Open-vocabulary Video Instance Segmentation

1 code implementation18 Jan 2024 Zesen Cheng, Kehan Li, Hao Li, Peng Jin, Chang Liu, Xiawu Zheng, Rongrong Ji, Jie Chen

To mold instance queries to follow Brownian bridge and accomplish alignment with class texts, we design Bridge-Text Alignment (BTA) to learn discriminative bridge-level representations of instances via contrastive objectives.

Instance Segmentation Semantic Segmentation +1

FreestyleRet: Retrieving Images from Style-Diversified Queries

1 code implementation5 Dec 2023 Hao Li, Curise Jia, Peng Jin, Zesen Cheng, Kehan Li, Jialu Sui, Chang Liu, Li Yuan

In this paper, we propose the Style-Diversified Query-Based Image Retrieval task, which enables retrieval based on various query styles.

Image Retrieval Retrieval

NewsDialogues: Towards Proactive News Grounded Conversation

1 code implementation12 Aug 2023 Siheng Li, Yichun Yin, Cheng Yang, Wangjie Jiang, Yiwei Li, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang

In this paper, we propose a novel task, Proactive News Grounded Conversation, in which a dialogue system can proactively lead the conversation based on some key topics of the news.

Response Generation

AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models

no code implementations12 Aug 2023 Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang

Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years.

Few-Shot Learning Language Modelling

Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment

4 code implementations20 May 2023 Peng Jin, Hao Li, Zesen Cheng, Jinfa Huang, Zhennan Wang, Li Yuan, Chang Liu, Jie Chen

In this paper, we propose the Disentangled Conceptualization and Set-to-set Alignment (DiCoSA) to simulate the conceptualizing and reasoning process of human beings.

Retrieval Video Retrieval

TG-VQA: Ternary Game of Video Question Answering

no code implementations17 May 2023 Hao Li, Peng Jin, Zesen Cheng, Songyang Zhang, Kai Chen, Zhennan Wang, Chang Liu, Jie Chen

Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them.

Contrastive Learning Question Answering +2

Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation

no code implementations ICCV 2023 Kehan Li, Yian Zhao, Zhennan Wang, Zesen Cheng, Peng Jin, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen

Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis.

Interactive Segmentation

DiffusionRet: Generative Text-Video Retrieval with Diffusion Model

4 code implementations ICCV 2023 Peng Jin, Hao Li, Zesen Cheng, Kehan Li, Xiangyang Ji, Chang Liu, Li Yuan, Jie Chen

Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i. e., p(candidates|query).

Retrieval Video Retrieval

Parallel Vertex Diffusion for Unified Visual Grounding

no code implementations13 Mar 2023 Zesen Cheng, Kehan Li, Peng Jin, Xiangyang Ji, Li Yuan, Chang Liu, Jie Chen

An intuitive materialization of our paradigm is Parallel Vertex Diffusion (PVD) to directly set vertex coordinates as the generation target and use a diffusion model to train and infer.

Visual Grounding

ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

no code implementations CVPR 2023 Kehan Li, Zhennan Wang, Zesen Cheng, Runyi Yu, Yian Zhao, Guoli Song, Chang Liu, Li Yuan, Jie Chen

Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e. g., unsupervised semantic segmentation (USS).

Image Segmentation Unsupervised Semantic Segmentation

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

2 code implementations CVPR 2023 Yanmin Wu, Xinhua Cheng, Renrui Zhang, Zesen Cheng, Jian Zhang

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues.

Object Sentence +1

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