52 citations · 114 across the 14 of their papers we have counts for
16 papers
What Generation Knows: Unlocking Fine-Grained Understanding in Unified Multimodal Models
Tengju Ru, Weitong Lian, Zecong Tang +6
Fine-grained visual understanding requires distinguishing subtle differences in local attributes and spatial relations, yet remains challenging for existing vision-language models.…
AVCap: Reinforcing Audio-Video Joint Caption with Detail-Aware Reward
Mingyang Wu, Kaituo Feng, Bohao Li +3
Detailed audio-video joint captioning is essential for multimodal video understanding and generation. However, prior works are constrained by three main limitations: (1) the scarci…
Twins: Learn to Predict Unified Representations with Focal Loss
Kaixiong Gong, Xin Cai, Bin Lin +9
Unified multimodal models seek a shared visual token space that supports both multimodal understanding and image generation. Discrete methods unify the interface via a shared codeb…
UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks
Zhekai Chen, Chengqi Duan, Kaiyue Sun +4
The rapid development of large language models and multimodal large language models has accelerated the emergence of proactive agents capable of operating everyday tools and assist…
Video-R1: Reinforcing Video Reasoning in MLLMs
Kaituo Feng, Kaixiong Gong, Bohao Li +7
Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based reinforcement learning (RL), we introduce Video-R1 as the first attempt to systematically expl…
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Kaixiong Gong, Kaituo Feng, Bohao Li +8
Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While th…
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
Sijie Cheng, Kechen Fang, Yangyang Yu +6
Recent advancements in Multi-modal Large Language Models (MLLMs) have opened new avenues for applications in Embodied AI. Building on previous work, EgoThink, we introduce VidEgoTh…
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
Bohao Li, Yuying Ge, Yi Chen +3
Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real…
EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning
Yi Chen, Yuying Ge, Yixiao Ge +6
The pursuit of artificial general intelligence (AGI) has been accelerated by Multimodal Large Language Models (MLLMs), which exhibit superior reasoning, generalization capabilities…
SEED-Bench-2: Benchmarking Multimodal Large Language Models
Bohao Li, Yuying Ge, Yixiao Ge +4
Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating n…
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Bohao Li, Rui Wang, Guangzhi Wang +3
Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable…
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners
Renrui Zhang, Xiangfei Hu, Bohao Li +5
Visual recognition in low-data regimes requires deep neural networks to learn generalized representations from limited training samples. Recently, CLIP-based methods have shown pro…
Proposal Distribution Calibration for Few-Shot Object Detection
Bohao Li, Chang Liu, Mengnan Shi +3
Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step…
Collaboration of Pre-trained Models Makes Better Few-shot Learner
Renrui Zhang, Bohao Li, Wei Zhang +4
Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regim…
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Bohao Li, Boyu Yang, Chang Liu +3
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects. However,an imp…
Prototype Mixture Models for Few-shot Semantic Segmentation
Boyu Yang, Chang Liu, Bohao Li +2
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. Using a single prototype acquired direct…