4 citations · 4 across the 15 of their papers we have counts for
19 papers
How Far Are We from Removing the Visual Encoder? Scaling Laws for Encoder-Free Multimodal Pretraining
Lin Chen, Bolin Ni, Qi Yang +6
Most modern multimodal large language models (MLLMs) build on a pretrained visual encoder that provides a strong visual prior. Encoder-free MLLMs instead learn visual representatio…
Dr.Credit: Rubric-Grounded Process Credit Assignment for Deep Research Agents
Yingjian Zhu, Zhenyi Wang, Jiaxin Guo +6
Rubric-based tasks are increasingly addressed through reinforcement learning (RL), with rubric scores used as training rewards. However, these rewards typically supervise final ans…
DICS: Exploring Data Intrinsic Consistency for Visual Instruction Selection
Yuyang Hong, Jinhui Guo, Jiaqi Gu +6
Visual instruction tuning is crucial for advancing the vision-language alignment and instruction-following capabilities of Vision-Language Models (VLMs). However, identifying optim…
DiffPDE: Masked Diffusion Language Models as PDE Solver
Wenxuan Guo, Yuyang Hong, Lubin Fan +4
Existing approaches for synthesizing Partial Differential Equation (PDE) solvers predominantly rely on autoregressive models, yet their global left-to-right decoding incurs substan…
Joint Training of Multi-Token Prediction in Reinforcement Learning via Optimal Coefficient Calibration
Zili Wang, Jiajun Chai, Lin Chen +3
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as the standard paradigm for improving reasoning capability of large language models, while Multi-Token Prediction…
WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering
Yingjian Zhu, Xinming Wang, Kun Ding +3
Multi-modal Retrieval-Augmented Generation (RAG) has emerged as a highly effective paradigm for Knowledge-Based Visual Question Answering (KB-VQA). Despite recent advancements, pre…
VisBrowse-Bench: Benchmarking Visual-Native Search for Multimodal Browsing Agents
Zhengbo Zhang, Jinbo Su, Zhaowen Zhou +14
The rapid advancement of Multimodal Large Language Models (MLLMs) has enabled browsing agents to acquire and reason over multimodal information in the real world. But existing benc…
Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding
Lin Chen, Bolin Ni, Qi Yang +5
Despite the remarkable capabilities of Multimodal Large Language Models (MLLMs), they still suffer from visual fading in long-context scenarios. Specifically, the attention to visu…
CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering
Yuyang Hong, Jiaqi Gu, Yujin Lou +7
Knowledge-based visual question answering (KB-VQA) demonstrates significant potential for handling knowledge-intensive tasks. However, conflicts arise between static parametric kno…
SAM-MI: A Mask-Injected Framework for Enhancing Open-Vocabulary Semantic Segmentation with SAM
Lin Chen, Yingjian Zhu, Qi Yang +3
Open-vocabulary semantic segmentation (OVSS) aims to segment and recognize objects universally. Trained on extensive high-quality segmentation data, the segment anything model (SAM…
Knowledge-based Visual Question Answer with Multimodal Processing, Retrieval and Filtering
Yuyang Hong, Jiaqi Gu, Qi Yang +6
Knowledge-based visual question answering (KB-VQA) requires visual language models (VLMs) to integrate visual understanding with external knowledge retrieval. Although retrieval-au…
Taming Modality Entanglement in Continual Audio-Visual Segmentation
Yuyang Hong, Qi Yang, Tao Zhang +5
Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings while preserving performance on previ…
R-4B: Incentivizing General-Purpose Auto-Thinking Capability in MLLMs via Bi-Mode Annealing and Reinforce Learning
Qi Yang, Bolin Ni, Shiming Xiang +3
Multimodal Large Language Models (MLLMs) equipped with step-by-step thinking capabilities have demonstrated remarkable performance on complex reasoning problems. However, this thin…
Faster and Better LLMs via Latency-Aware Test-Time Scaling
Zili Wang, Tianyu Zhang, Haoli Bai +5
Test-Time Scaling (TTS) has proven effective in improving the performance of Large Language Models (LLMs) during inference. However, existing research has overlooked the efficiency…
Efficient Redundancy Reduction for Open-Vocabulary Semantic Segmentation
Lin Chen, Qi Yang, Kun Ding +5
Open-vocabulary semantic segmentation (OVSS) is an open-world task that aims to assign each pixel within an image to a specific class defined by arbitrary text descriptions. While…
Continuous Speculative Decoding for Autoregressive Image Generation
Zili Wang, Zheng Zhang, Kun Ding +3
Continuous visual autoregressive (AR) models have demonstrated promising performance in image generation, but their inherently sequential nature results in slow inference speed. Sp…
Draw an Audio: Leveraging Multi-Instruction for Video-to-Audio Synthesis
Qi Yang, Binjie Mao, Zili Wang +6
Foley is a term commonly used in filmmaking, referring to the addition of daily sound effects to silent films or videos to enhance the auditory experience. Video-to-Audio (V2A), as…
AVESFormer: Efficient Transformer Design for Real-Time Audio-Visual Segmentation
Zili Wang, Qi Yang, Linsu Shi +4
Recently, transformer-based models have demonstrated remarkable performance on audio-visual segmentation (AVS) tasks. However, their expensive computational cost makes real-time in…
Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation
Qi Yang, Xing Nie, Tong Li +5
Recently, an audio-visual segmentation (AVS) task has been introduced, aiming to group pixels with sounding objects within a given video. This task necessitates a first-ever audio-…