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20232026
most citedDriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving

18 citations · 38 across the 16 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

Looped Diffusion Transformer

Yong Xien Chng, Tianyi Chen, Wenwen Tong +7

Improving text-to-image models has traditionally relied on increasing model size or the number of denoising steps. In this work, we explore an alternative way to scale computation…

cs.CV2026

SenseNova-U1.5: Towards Native Unified Visual Intelligence

Haiwen Diao, Jiahao Wang, Chenjing Ding +62

We launch SenseNova-U1.5, an 8B-MoT native unified multimodal model that understands, reasons about, and generates visual content within an encoder-free and VAE-free architecture.…

cs.CV2026

InterSketch: An Interleaved Reasoning Model with Self-correcting Visual Sketch and Stepwise Reward

Zhiwei Ning, Wenwen Tong, Xiangli Kong +12

While vision-language models (VLMs) have exhibited multi-turn visual reasoning capabilities, their reasoning trajectories remain relatively shallow and are dominated by a text-cent…

cs.CV2026

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

Haiwen Diao, Penghao Wu, Hanming Deng +55

Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fra…

cs.CV2026

V-ABS: Action-Observer Driven Beam Search for Dynamic Visual Reasoning

Zhiwei Ning, Xuanang Gao, Jiaxi Cao +6

Multimodal large language models (MLLMs) have achieved remarkable success in general perception, yet complex multi-step visual reasoning remains a persistent challenge. Although re…

cs.CV2026

EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams

Dongchuan Ran, Linyu Ou, Xueheng Li +5

Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users. While emerging benchmarks ad…

cs.CV2026

SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning

Yong Xien Chng, Tao Hu, Wenwen Tong +10

While Vision-Language Models (VLMs) can solve complex tasks through agentic reasoning, their capabilities remain largely constrained to text-oriented chain-of-thought or isolated t…

cs.CV2025

InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialogue

Wenwen Tong, Hewei Guo, Dongchuan Ran +23

We introduce InteractiveOmni, a unified and open-source omni-modal large language model for audio-visual multi-turn interaction, ranging from 4B to 8B parameters, designed to lead…

cs.CV2025

TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos

Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang +7

Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing ben…

cs.CV2024★ 16 cited

How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Zhe Chen, Weiyun Wang, Hao Tian +32

In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models…

cs.CV2023★ 18 cited

DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving

Erfei Cui, Wenhai Wang, Zhiqi Li +7

Large language models (LLMs) have opened up new possibilities for intelligent agents, endowing them with human-like thinking and cognitive abilities. In this work, we delve into th…

cs.CV2023★ 1 cited

Scene as Occupancy

Chonghao Sima, Wenwen Tong, Tai Wang +8

Human driver can easily describe the complex traffic scene by visual system. Such an ability of precise perception is essential for driver's planning. To achieve this, a geometry-a…

cs.CV2023★ 2 cited

3D Data Augmentation for Driving Scenes on Camera

Wenwen Tong, Jiangwei Xie, Tianyu Li +7

Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone. While data augmentation is an effective technique to enrich…