activity
20232026
most citedMulti-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection

5 citations · 8 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2026

Paths: Prompt-aware Spatio-temporal Transformer with Hierarchical Multi-modal Fusion for RGB-Event Video Person Re-Identification

Yakun Huo, Yingquan Wang, Yangyang Liu +4

RGB-Event Video Person Re-Identification (RE-VReID) aims to retrieve specific person across non-overlapping cameras with complementary RGB videos and event streams. However, existi…

cs.CV2026

GROVE: Growing and Reasoning over Temporally Stratified Memory from Streaming Video Experience

Sitong Gong, Caixin Kang, Tianyu Yan +7

A wearable assistant should both answer questions about its visual history and recognize when that history is useful to the present situation. Existing video-memory systems primari…

cs.CV2026

Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos

Gong Sitong, Tianyu Yan, Caixin Kang +6

When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proa…

cs.CV2026

3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum

Yuliang Zhang, Fang He, Lulu Peng +5

Placenta Accreta Spectrum (PAS) is a rare but highly dangerous obstetric disease. Early and accurate PAS diagnosis is critical for maternal health. Traditional PAS diagnosis relies…

cs.AI2026

Perception or Prejudice: Can MLLMs Go Beyond First Impressions of Personality?

Caixin Kang, Tianyu Yan, Sitong Gong +8

Multimodal Large Language Models (MLLMs) are increasingly deployed in human-facing roles where personality perception is critical, yet existing benchmarks evaluate this capability…

cs.CV2026

HFP-SAM: Hierarchical Frequency Prompted SAM for Efficient Marine Animal Segmentation

Pingping Zhang, Tianyu Yan, Yuhao Wang +7

Marine Animal Segmentation (MAS) aims at identifying and segmenting marine animals from complex marine environments. Most of previous deep learning-based MAS methods struggle with…

cs.HC2025

Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novels

Yifei Huang, Tianyu Yan, Sitong Gong +5

We present the Living Novel, an end-to-end system that transforms any literary work into an immersive, multi-character conversational experience. This system is designed to solve t…

cs.CV2025

Towards Interactive Intelligence for Digital Humans

Yiyi Cai, Xuangeng Chu, Xiwei Gao +16

We introduce Interactive Intelligence, a novel paradigm of digital human that is capable of personality-aligned expression, adaptive interaction, and self-evolution. To realize thi…

cs.CV2024

MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt

Yuhao Wang, Xuehu Liu, Tianyu Yan +4

Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary image information from different modalities. Recently, large-scale pre-trai…

cs.CV2024★ 5 cited

Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection

Shixuan Gao, Pingping Zhang, Tianyu Yan +1

Salient Object Detection (SOD) aims to identify and segment the most prominent objects in images. Advanced SOD methods often utilize various Convolutional Neural Networks (CNN) or…

cs.CV2024★ 2 cited

MAS-SAM: Segment Any Marine Animal with Aggregated Features

Tianyu Yan, Zifu Wan, Xinhao Deng +3

Recently, Segment Anything Model (SAM) shows exceptional performance in generating high-quality object masks and achieving zero-shot image segmentation. However, as a versatile vis…

cs.CV2024★ 1 cited

Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM

Pingping Zhang, Tianyu Yan, Yang Liu +1

As an important pillar of underwater intelligence, Marine Animal Segmentation (MAS) involves segmenting animals within marine environments. Previous methods don't excel in extracti…

cs.CV2023

TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images

Tianyu Yan, Zifu Wan, Pingping Zhang +2

In the remote sensing field, Change Detection (CD) aims to identify and localize the changed regions from dual-phase images over the same places. Recently, it has achieved great pr…