76 citations · 78 across the 22 of their papers we have counts for
22 papers
REZE: Recognition-Based Zero-Shot Extraction for Video Temporal Grounding
Boyang Li, Chenhui Gou, Jianfei Cai
Video temporal grounding (VTG) refers to the task of identifying the time interval in a video that corresponds to a given natural-language query. A common zero-shot strategy asks a…
Sample-Efficient Learning from Agent Experience
Chenhui Gou, Haoqin Tu, Yunhao Fang +2
Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offer…
EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
Deyao Zhu, Xin Zhou, Shengling Qin +44
Pretraining scaling laws reveal that model capability improves predictably with data and compute. But learning from real world environments after deployment remains far less unders…
LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models
Zicheng Duan, Jiatong Xia, Zeyu Zhang +7
Recent generative video world models aim to simulate visual environment evolution, allowing an observer to interactively explore the scene via camera control. However, they implici…
VQ-VA World: Towards High-Quality Visual Question-Visual Answering
Chenhui Gou, Zilong Chen, Zeyu Wang +10
This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question -- an ability that has recently emerged in prop…
LightFusion: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation
Zeyu Wang, Zilong Chen, Chenhui Gou +8
Unified multimodal models have recently shown remarkable gains in both capability and versatility, yet most leading systems are still trained from scratch and require substantial c…
Where and What Matters: Sensitivity-Aware Task Vectors for Many-Shot Multimodal In-Context Learning
Ziyu Ma, Chenhui Gou, Yiming Hu +4
Large Multimodal Models (LMMs) have shown promising in-context learning (ICL) capabilities, but scaling to many-shot settings remains difficult due to limited context length and hi…
UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis
Junzhi Ning, Wei Li, Cheng Tang +24
Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central to medical AI. Most existing sy…
An Empirical Study on How Video-LLMs Answer Video Questions
Chenhui Gou, Ziyu Ma, Zicheng Duan +6
Taking advantage of large-scale data and pretrained language models, Video Large Language Models (Video-LLMs) have shown strong capabilities in answering video questions. However,…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
Emerging Properties in Unified Multimodal Pretraining
Chaorui Deng, Deyao Zhu, Kunchang Li +9
Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. In this work, we introduce BAGEL, an open-source foundationa…
Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis
Hongyu Sun, Qiuhong Ke, Ming Cheng +4
This paper proposes a general solution to enable point cloud recognition models to handle distribution shifts at test time. Unlike prior methods, which rely heavily on training dat…
Sparsity Forcing: Reinforcing Token Sparsity of MLLMs
Feng Chen, Yefei He, Lequan Lin +4
Sparse attention mechanisms aim to reduce computational overhead with minimal accuracy loss by selectively processing salient tokens. Despite their effectiveness, most methods mere…
Mobile-VideoGPT: Fast and Accurate Model for Mobile Video Understanding
Abdelrahman Shaker, Muhammad Maaz, Chenhui Gou +3
Video understanding models often struggle with high computational requirements, extensive parameter counts, and slow inference speed, making them inefficient for practical use. To…
Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
Feng Chen, Chenhui Gou, Jing Liu +6
As multimodal large language models (MLLMs) advance rapidly, rigorous evaluation has become essential, providing further guidance for their development. In this work, we focus on a…
EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance
Zicheng Duan, Yuxuan Ding, Chenhui Gou +3
Zero-shot personalized image generation models aim to produce images that align with both a given text prompt and subject image, requiring the model to incorporate both sources of…
How Well Can Vision Language Models See Image Details?
Chenhui Gou, Abdulwahab Felemban, Faizan Farooq Khan +4
Large Language Model-based Vision-Language Models (LLM-based VLMs) have demonstrated impressive results in various vision-language understanding tasks. However, how well these VLMs…
InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows
Kirolos Ataallah, Eslam Abdelrahman, Mahmoud Ahmed +4
Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a significant challenge for multi-modal models. Existing benchmark…
DrVideo: Document Retrieval Based Long Video Understanding
Ziyu Ma, Chenhui Gou, Hengcan Shi +4
Most of the existing methods for video understanding primarily focus on videos only lasting tens of seconds, with limited exploration of techniques for handling long videos. The in…
JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments
Duy-Tho Le, Chenhui Gou, Stavya Datta +4
Autonomous robot systems have attracted increasing research attention in recent years, where environment understanding is a crucial step for robot navigation, human-robot interacti…
Strong and Controllable Blind Image Decomposition
Zeyu Zhang, Junlin Han, Chenhui Gou +2
Blind image decomposition aims to decompose all components present in an image, typically used to restore a multi-degraded input image. While fully recovering the clean image is ap…
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Jian Wang, Chenhui Gou, Qiman Wu +4
Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in th…