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20232026
most citedInstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation

2 citations · 2 across the 8 of their papers we have counts for

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cs.CV2026

BlenderWorld: Benchmarking Visual Agents for 3D Creation from Real-World References

Xinyu Fang, Yuchi Wang, Weihao Yu +12

Recent advances in multimodal large language models (MLLMs) have enabled them to act as autonomous agents, creating and editing 3D models via code or direct software manipulation.…

cs.CV2025

CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images

Chengqi Duan, Kaiyue Sun, Rongyao Fang +10

Recent advances in Large Language Models (LLMs) and Vision Language Models (VLMs) have shown significant progress in mathematical reasoning, yet they still face a critical bottlene…

cs.CV2025

FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmark

Rongyao Fang, Aldrich Yu, Chengqi Duan +7

The advancement of open-source text-to-image (T2I) models has been hindered by the absence of large-scale, reasoning-focused datasets and comprehensive evaluation benchmarks, resul…

cs.CV2025

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving

Xuesong Chen, Linjiang Huang, Tao Ma +3

The integration of Vision-Language Models (VLMs) into autonomous driving systems has shown promise in addressing key challenges such as learning complexity, interpretability, and c…

cs.CV2025

GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning

Chengqi Duan, Rongyao Fang, Yuqing Wang +5

Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that specify multiple objects with precise…

cs.CV2025

GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing

Rongyao Fang, Chengqi Duan, Kun Wang +9

Current image generation and editing methods primarily process textual prompts as direct inputs without reasoning about visual composition and explicit operations. We present Gener…

cs.CV2024

StreamChat: Chatting with Streaming Video

Jihao Liu, Zhiding Yu, Shiyi Lan +5

This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content. In streaming interaction…

cs.CV2024

PUMA: Empowering Unified MLLM with Multi-granular Visual Generation

Rongyao Fang, Chengqi Duan, Kun Wang +7

Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimo…

cs.CV2024

NODI: Out-Of-Distribution Detection with Noise from Diffusion

Jingqiu Zhou, Aojun Zhou, Hongsheng Li

Out-of-distribution (OOD) detection is a crucial part of deploying machine learning models safely. It has been extensively studied with a plethora of methods developed in the liter…

cs.CV2023★ 2 cited

InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence Generation

Rongyao Fang, Shilin Yan, Zhaoyang Huang +4

Empowering models to dynamically accomplish tasks specified through natural language instructions represents a promising path toward more capable and general artificial intelligenc…