activity
20242026
most citedA Comprehensive Survey on Composed Image Retrieval

1 citations · 1 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CV2026

Thinking Beyond Videos: Unifying Video Reasoning and Deep Research for Open-World Video Agents

Wenqi Liu, Shijie Ma, Yunxiao Wang +21

Open-world video understanding often requires a model to locate sparse visual evidence and acquire external knowledge that is absent from the video and its parametric memory. While…

cs.RO2026

RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model

Kehan Li, Bohan Hou, Minghao Zhu +28

We present RynnBrain 1.1, a family of embodied foundation models spanning 2B, 9B, and 122B-A10B scales. Trained with a unified spatio-temporal and physically grounded framework, Ry…

cs.CV2026

FiRE: Enhancing MLLMs with Fine-Grained Context Learning for Complex Image Retrieval

Bohan Hou, Haoqiang Lin, Xuemeng Song +4

Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significant potential as universal im…

cs.CV2026

InterLV-Search: Benchmarking Interleaved Multimodal Agentic Search

Bohan Hou, Jiuning Gu, Jiayan Guo +5

Existing benchmarks for multimodal agentic search evaluate multimodal search and visual browsing, but visual evidence is either confined to the input or treated as an answer endpoi…

cs.RO2026

RynnBrain: Open Embodied Foundation Models

Ronghao Dang, Jiayan Guo, Bohan Hou +23

Despite rapid progress in multimodal foundation models, embodied intelligence community still lacks a unified, physically grounded foundation model that integrates perception, reas…

cs.CV2025

ImgEdit: A Unified Image Editing Dataset and Benchmark

Yang Ye, Xianyi He, Zongjian Li +5

Recent advancements in generative models have enabled high-fidelity text-to-image generation. However, open-source image-editing models still lag behind their proprietary counterpa…

cs.MM2025★ 1 cited

A Comprehensive Survey on Composed Image Retrieval

Xuemeng Song, Haoqiang Lin, Haokun Wen +3

Composed Image Retrieval (CIR) is an emerging yet challenging task that allows users to search for target images using a multimodal query, comprising a reference image and a modifi…

cs.CV2025

ORFormer: Occlusion-Robust Transformer for Accurate Facial Landmark Detection

Jui-Che Chiang, Hou-Ning Hu, Bo-Syuan Hou +4

Although facial landmark detection (FLD) has gained significant progress, existing FLD methods still suffer from performance drops on partially non-visible faces, such as faces wit…

cs.CV2024

Pseudo-triplet Guided Few-shot Composed Image Retrieval

Bohan Hou, Haoqiang Lin, Haokun Wen +3

Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image with a multimodal query, i.e., a reference image, and its complementary modification tex…