1 citations · 1 across the 17 of their papers we have counts for
18 papers
Beyond Explicit Intent: Discovering and Shaping Artistic Intent through Human-AI Co-Creation
Renye Yan, Jialun Zhong, Jikang Cheng +3
Generative AI is becoming an important medium for artistic creation, yet existing interactions often assume that creators can specify intentions in advance and accurately communica…
SwiftExplorer: Training-free Diffusion Model Alignment with Swift Diversity Exploration
Renye Yan, Jikang Cheng, You Wu +5
Diffusion models have general generative abilities but struggle to align with specific objectives. Fine-tuning can improve alignment, yet its training cost is often prohibitive. Th…
Can We Perform Online RL for Image Editing without Editing Rewards?
Qichao Ma, Jikang Cheng, Ling Liang +3
Reinforcement learning (RL) enables direct preference optimization for image editing through editing-specific rewards, which remain less developed due to costly triplet supervision…
Pixel-Space Diffusion Transformers
Renye Yan, Jikang Cheng, You Wu +8
Latent diffusion models (LDMs) enable efficient high-resolution image synthesis by denoising in a VAE-compressed latent space. However, fixed visual tokenizers can discard fine tex…
PAST: Prompt-Adaptive Sampling Termination for Efficient Diffusion Model
Renye Yan, Jikang Cheng, You Wu +4
While diffusion models have made significant progress in text-to-image tasks, they still exhibit limitations when directly optimizing downstream objectives. Although Reinforcement…
Explore or Converge? Stage-Guided Per-Step Optimization for Diffusion Models
Renye Yan, Jikang Cheng, You Wu +4
Diffusion models have strong generative capabilities. However, their maximum likelihood training objective only focuses on reconstructing the data distribution, making it difficult…
GenTrack: Physical Alignment for Robot-Native Motion Generation and Zero-Shot Humanoid Tracking
Zeyu Ling, Xinyao Yu, Renye Yan +4
General-purpose humanoid trackers can execute diverse references, but their zero-shot coverage depends on large embodied corpora that are costly to extend. Text-to-motion generator…
InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection
Jikang Cheng, Hao Shen, Xueyi Zhang +4
The rapid evolution of face forgery techniques has introduced an increasing variety of manipulations. Incremental Face Forgery Detection (IFFD), which incrementally adds new forger…
Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?
Renye Yan, Jikang Cheng, Shikun Sun +7
Despite strong image-generation performance, diffusion models' reconstruction objectives limit alignment with human preferences. RL enables such alignment through explicit rewards.…
Tutor-Student Reinforcement Learning: A Dynamic Curriculum for Robust Deepfake Detection
Zhanhe Lei, Zhongyuan Wang, Jikang Cheng +5
Standard supervised training for deepfake detection treats all samples with uniform importance, which can be suboptimal for learning robust and generalizable features. In this work…
A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World
Jikang Cheng, Renye Yan, Zhiyuan Yan +5
Existing methods for deepfake detection aim to develop generalizable detectors. Although "generalizable" is the ultimate target once and for all, with limited training forgeries an…
When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection
Hao Shen, Jikang Cheng, Renye Yan +3
The rapid advancement of face generation techniques has introduced an increasing variety of forgery methods, making incremental deepfake detection essential for maintaining robust…
VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension
Zeyu Ling, Bo Han, Shiyang Li +3
Large language models (LLMs) are, by design, inherently capable of multi-task learning: through a unified next-token prediction paradigm, they can naturally address a wide variety…
Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection
Jikang Cheng, Zhiyuan Yan, Ying Zhang +5
The rapid advancement of face forgery techniques has introduced a growing variety of forgeries. Incremental Face Forgery Detection (IFFD), involving gradually adding new forgery da…
Can We Leave Deepfake Data Behind in Training Deepfake Detector?
Jikang Cheng, Zhiyuan Yan, Ying Zhang +3
The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with…
IDRetracor: Towards Visual Forensics Against Malicious Face Swapping
Jikang Cheng, Jiaxin Ai, Zhen Han +4
The face swapping technique based on deepfake methods poses significant social risks to personal identity security. While numerous deepfake detection methods have been proposed as…
DePatch: Towards Robust Adversarial Patch for Evading Person Detectors in the Real World
Jikang Cheng, Ying Zhang, Zhongyuan Wang +2
Recent years have seen an increasing interest in physical adversarial attacks, which aim to craft deployable patterns for deceiving deep neural networks, especially for person dete…
ED: Explicit Data-level Debiasing for Deepfake Detection
Jikang Cheng, Ying Zhang, Qin Zou +4
Learning intrinsic bias from limited data has been considered the main reason for the failure of deepfake detection with generalizability. Apart from the discovered content and spe…