16 citations · 39 across the 12 of their papers we have counts for
16 papers
MMPostTrainBench: Benchmarking Autonomous Research for Multimodal Post-Training
Yuxin Liu, Yuxuan Wang, Zhenxin Lei +9
Autonomous research seeks sustained model improvements through iterative experimentation and feedback. LLM agents show promise in automating machine learning and language-model pos…
Edit2TikZ: A Comprehensive and Challenging Benchmark for Scientific Figure Editing with TikZ
Zongyun Zhang, Jiacheng Ruan, Xian Gao +5
Although multimodal large language models (MLLMs) have shown substantial potential in visual understanding and graphic code generation, editing scientific figures through code pres…
VLZip: Unified Visual and Textual Compression for Interleaved Long-Context Modeling
Yuqi Zhang, Cheng Chen, Yuyu Guo +6
Vision Language Models (VLMs) face significant challenges with ultra-long, interleaved image-text sequences due to the quadratic complexity of self-attention. Current solutions eit…
Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification
Wujian Peng, Lingchen Meng, Yuxuan Cai +7
Unified Multimodal Modeling aims to integrate visual understanding and generation within a single system. However, existing approaches typically rely on two disparate visual tokeni…
Qwen3-VL Technical Report
Shuai Bai, Yuxuan Cai, Ruizhe Chen +61
We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively…
Unify Robot Actions in Camera Frame
Sicheng Xie, Lingchen Meng, Zijie Diao +9
Cross-embodiment robot learning requires a unified action representation with consistent semantics across robot platforms. Existing representations suffer from platform-specific in…
Enhancing Vision Foundation Models via Multimodal Continual Pre-Training
Yitong Chen, Lingchen Meng, Wujian Peng +4
Vision Foundation Models (VFMs) provide strong visual representations for a wide range of applications. In this work, we enhance prevailing VFMs through multimodal training, allowi…
FOCUS: Towards Universal Foreground Segmentation
Zuyao You, Lingyu Kong, Lingchen Meng +1
Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for e…
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection
Yitong Chen, Wenhao Yao, Lingchen Meng +3
Enabling models to recognize vast open-world categories has been a longstanding pursuit in object detection. By leveraging the generalization capabilities of vision-language models…
INST-IT: Boosting Instance Understanding via Explicit Visual Prompt Instruction Tuning
Wujian Peng, Lingchen Meng, Yitong Chen +7
Large Multimodal Models (LMMs) have made significant breakthroughs with the advancement of instruction tuning. However, while existing models can understand images and videos at a…
V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results
Jiaqi Wang, Yuhang Zang, Pan Zhang +31
Detecting objects in real-world scenes is a complex task due to various challenges, including the vast range of object categories, and potential encounters with previously unknown…
DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs
Lingchen Meng, Jianwei Yang, Rui Tian +4
Most large multimodal models (LMMs) are implemented by feeding visual tokens as a sequence into the first layer of a large language model (LLM). The resulting architecture is simpl…
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Junke Wang, Lingchen Meng, Zejia Weng +3
Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data. Despite the promising performanc…
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
Lingchen Meng, Shiyi Lan, Hengduo Li +3
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the…
Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection
Lingchen Meng, Xiyang Dai, Jianwei Yang +7
Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to expl…
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen +4
Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of vision tasks recently. While achieving excellent performance…