4 citations · 8 across the 14 of their papers we have counts for
17 papers
Revisit to Segment: Working Memory Distillation for Reasoning Segmentation
Cilin Yan, Yilun Qiu, Wanyang Zhang +4
Multimodal large language models (MLLMs) have approached image segmentation by reasoning about visual content and predicting target locations. Their generated responses contain rea…
RegRet: Enhancing Region-Level Retrieval in Large Multimodal Models
Xun Liang, Honghui Yang, Weihang Pan +6
Region-level retrieval aims to align user-specified image regions with relevant regions or textual descriptions, playing a crucial role in realworld applications such as e-commerce…
SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models
Hongxiang Li, Hongxu Chen, Chenyang Zhu +5
Multimodal Large Language Models (MLLMs) have achieved remarkable success in visual understanding but remain constrained in visual generation due to the fundamental feature discrep…
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation
Yiming Zhao, Yu Zeng, Wenxuan Huang +11
Large Vision-Language Models (LVLMs) have shown significant progress in video understanding, yet they face substantial challenges in tasks requiring precise spatiotemporal localiza…
LRCP: Low-Rank Compressibility Guided Visual Token Pruning for Efficient LVLMs
Hongyu Lu, Feng Zhang, Wenwei Jin +5
Large vision-language models (LVLMs) achieve strong multimodal understanding, but their inference cost grows rapidly with the number of visual tokens, especially for high-resolutio…
Weaver: End-to-End Agentic System Training for Video Interleaved Reasoning
Yudi Shi, Shangzhe Di, Qirui Chen +5
Video reasoning constitutes a comprehensive assessment of a model's capabilities, as it demands robust perceptual and interpretive skills, thereby serving as a means to explore the…
CrossVid: A Comprehensive Benchmark for Evaluating Cross-Video Reasoning in Multimodal Large Language Models
Jingyao Li, Jingyun Wang, Molin Tan +6
Cross-Video Reasoning (CVR) presents a significant challenge in video understanding, which requires simultaneous understanding of multiple videos to aggregate and compare informati…
GIR-Bench: Versatile Benchmark for Generating Images with Reasoning
Hongxiang Li, Yaowei Li, Bin Lin +7
Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation, showing great promise for advanced multimodal inte…
Object-centric Video Question Answering with Visual Grounding and Referring
Haochen Wang, Qirui Chen, Cilin Yan +5
Video Large Language Models (VideoLLMs) have recently demonstrated remarkable progress in general video understanding. However, existing models primarily focus on high-level compre…
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services
Fei Zhao, Chonggang Lu, Yue Wang +22
As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform con…
Progressive Scaling Visual Object Tracking
Jack Hong, Shilin Yan, Zehao Xiao +4
In this work, we propose a progressive scaling training strategy for visual object tracking, systematically analyzing the influence of training data volume, model size, and input r…
WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
Jack Hong, Shilin Yan, Jiayin Cai +3
We introduce WorldSense, the first benchmark to assess the multi-modal video understanding, that simultaneously encompasses visual, audio, and text inputs. In contrast to existing…
DynaPrompt: Dynamic Test-Time Prompt Tuning
Zehao Xiao, Shilin Yan, Jack Hong +6
Test-time prompt tuning enhances zero-shot generalization of vision-language models but tends to ignore the relatedness among test samples during inference. Online test-time prompt…
LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant
Yikun Liu, Pingan Chen, Jiayin Cai +5
With the rapid advancement of multimodal information retrieval, increasingly complex retrieval tasks have emerged. Existing methods predominately rely on task-specific fine-tuning…
Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective
Ouxiang Li, Jiayin Cai, Yanbin Hao +3
With recent generative models facilitating photo-realistic image synthesis, the proliferation of synthetic images has also engendered certain negative impacts on social platforms,…
VISA: Reasoning Video Object Segmentation via Large Language Models
Cilin Yan, Haochen Wang, Shilin Yan +5
Existing Video Object Segmentation (VOS) relies on explicit user instructions, such as categories, masks, or short phrases, restricting their ability to perform complex video segme…
A Sanity Check for AI-generated Image Detection
Shilin Yan, Ouxiang Li, Jiayin Cai +4
With the rapid development of generative models, discerning AI-generated content has evoked increasing attention from both industry and academia. In this paper, we conduct a sanity…