19 papers
CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modeling
Xin Shen, Chengyou Jia, Keshuo Xing +6
Beyond semantic content, camera parameters play a pivotal role in dictating the geometric perspective and appearance of any given image. While recent image editing models excel at…
ProtoGIB-Workload: Learning Workload-Specific Neural Topology Prototypes across Subjects
Yuzhe Zhang, Yixi Zhang, Shengdian Jiang +6
Reliable electroencephalography (EEG)-based mental workload recognition is crucial for adaptive human-centered systems, yet practical deployment requires models to generalize to us…
Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models
Bowen Ping, Xiangxin Zhou, Penghui Qi +3
Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for image and video generation. Met…
OmniTryOn: Video Try-On Anything at Once!
Changliang Xia, Chengyou Jia, Minnan Luo +3
Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restricted to single-garment tran…
GIDE: Unlocking Diffusion LLMs for Precise Training-Free Image Editing
Zifeng Zhu, Jiaming Han, Jiaxiang Zhao +2
While Diffusion Large Language Models (DLLMs) have demonstrated remarkable capabilities in multi-modal generation, performing precise, training-free image editing remains an open c…
Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models
Bowen Ping, Chengyou Jia, Minnan Luo +2
Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, mode…
Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
Ming Li, Yong-Jin Liu, Fang Liu +6
Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction…
-Predictor: Noise-Free Deterministic Diffusion for Dense Prediction
Changliang Xia, Chengyou Jia, Minnan Luo +3
Although diffusion models with strong visual priors have emerged as powerful dense prediction backbones, they overlook a core limitation: the stochastic noise at the core of diffus…
PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling
Bowen Ping, Chengyou Jia, Minnan Luo +4
Consistent image generation requires faithfully preserving identities, styles, and logical coherence across multiple images, which is essential for applications such as storytellin…
Multi-Modal Dataset Distillation in the Wild
Zhuohang Dang, Minnan Luo, Chengyou Jia +3
Recent multi-modal models have shown remarkable versatility in real-world applications. However, their rapid development encounters two critical data challenges. First, the trainin…
Why Settle for One? Text-to-ImageSet Generation and Evaluation
Chengyou Jia, Xin Shen, Zhuohang Dang +6
Despite remarkable progress in Text-to-Image models, many real-world applications require generating coherent image sets with diverse consistency requirements. Existing consistent…
From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios
Changliang Xia, Chengyou Jia, Zhuohang Dang +3
Dense prediction tasks hold significant importance of computer vision, aiming to learn pixel-wise annotated labels for input images. Despite advances in this field, existing method…
AutoGPS: Automated Geometry Problem Solving via Multimodal Formalization and Deductive Reasoning
Bowen Ping, Minnan Luo, Zhuohang Dang +2
Geometry problem solving presents distinctive challenges in artificial intelligence, requiring exceptional multimodal comprehension and rigorous mathematical reasoning capabilities…
ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
Chengyou Jia, Changliang Xia, Zhuohang Dang +3
Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the c…
AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant
Chengyou Jia, Minnan Luo, Zhuohang Dang +5
Digital agents capable of automating complex computer tasks have attracted considerable attention due to their immense potential to enhance human-computer interaction. However, exi…
Disentangled Noisy Correspondence Learning
Zhuohang Dang, Minnan Luo, Jihong Wang +6
Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impract…
A Unified Optimal Transport Framework for Cross-Modal Retrieval with Noisy Labels
Haochen Han, Minnan Luo, Huan Liu +1
Cross-modal retrieval (CMR) aims to establish interaction between different modalities, among which supervised CMR is emerging due to its flexibility in learning semantic category…
Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Zhuohang Dang, Minnan Luo, Chengyou Jia +3
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. Recently, to alleviate expensive data collection, co-occurring pairs from the Inte…
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
Chengyou Jia, Minnan Luo, Xiaojun Chang +6
Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing method…