16 papers
ETA: A New Agentic Paradigm for Embodied Tasks
Yitong Chen, Zezheng Huai, Sixian Li +7
When will robots have their ChatGPT moment? Such a breakthrough requires a general-purpose robot that can handle unfamiliar tasks in unfamiliar environments, remain controllable ov…
Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models
Yitong Chen, Shiduo Zhang, Jingjing Gong +1
One-step generation from standard diffusion or Flow-Matching training is rarely effective for text-to-image or video generation without distillation or specialized one-step objecti…
Rethinking Scientific Discovery in the Agentic Era
Yining Zheng, Yuxin Wang, Jiahao Lu +27
Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem formulation, literature gro…
IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Yitong Chen, Zijie Diao, Junke Wang +5
Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spac…
Channel-wise Vector Quantization
Wei Song, Tianhang Wang, Yitong Chen +5
We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantiza…
Compositional Text-to-Image Generation Via Region-aware Bimodal Direct Preference Optimization
Zhuohan Liu, Wujian Peng, Yitong Chen +1
Despite the rapid progress of text-to-image (T2I) models, generating images that accurately reflect complex compositional prompts (covering attribute bindings, object relationships…
DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders
Tianhang Wang, Yitong Chen, Wei Song +3
Representation Autoencoders (RAEs) leverage frozen vision foundation models (VFMs) as tokenizer encoders, providing robust high-level representations that facilitate fast convergen…
DPC-VQA: Decoupling Quality Perception and Residual Calibration for Video Quality Assessment
Xinyue Li, Shubo Xu, Zhichao Zhang +3
Recent multimodal large language models (MLLMs) have shown promising performance on video quality assessment (VQA) tasks. However, adapting them to new scenarios remains expensive…
CaTok: Taming Mean Flows for One-Dimensional Causal Image Tokenization
Yitong Chen, Zuxuan Wu, Xipeng Qiu +1
Autoregressive (AR) language models rely on causal tokenization, but extending this paradigm to vision remains non-trivial. Current visual tokenizers either flatten 2D patches into…
ELIQ: A Label-Free Framework for Quality Assessment of Evolving AI-Generated Images
Xinyue Li, Zhiming Xu, Min Tang +5
Generative text-to-image models are advancing at an unprecedented pace, continuously shifting the perceptual quality ceiling and rendering previously collected labels unreliable fo…
Decoupling Perception and Calibration: Label-Efficient Image Quality Assessment Framework
Xinyue Li, Zhichao Zhang, Zhiming Xu +4
Recent multimodal large language models (MLLMs) have demonstrated strong capabilities in image quality assessment (IQA) tasks. However, adapting such large-scale models is computat…
Unison: A Fully Automatic, Task-Universal, and Low-Cost Framework for Unified Understanding and Generation
Shihao Zhao, Yitong Chen, Zeyinzi Jiang +5
Unified understanding and generation is a highly appealing research direction in multimodal learning. There exist two approaches: one trains a transformer via an auto-regressive pa…
ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer
Yuan Tian, Min Zhou, Yitong Chen +19
Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection fr…
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…
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…