9 citations · 13 across the 21 of their papers we have counts for
22 papers
AffineTok: Semantic Affine Consistency for Diffusion-Friendly Visual Tokenizer
Junqiu Yu, Pandeng Li, Yikai Wang +11
Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be organized to facilitate denoisi…
fMRI2Face: A Full-HD fMRI-Video Dataset and Geometry-Guided Neural Decoding Framework for Dynamic Human Face Reconstruction
Jingyang Huo, Xiangru Huang, Chentao Shen +6
Reconstructing dynamic human faces from brain activity provides a powerful way to study how the mind perceives identity, expression, and facial motion. However, progress in fMRI-ba…
Conformal Reliability: A New Evaluation Metric for Conditional Generation
Yachen Gao, Xinwei Sun, Yikai Wang +4
Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliability of these models, which ta…
The Pictorial Cortex: Zero-Shot Cross-Subject fMRI-to-Image Reconstruction via Compositional Latent Modeling
Jingyang Huo, Yikai Wang, Yanwei Fu +1
Decoding visual experiences from human brain activity remains a central challenge at the intersection of neuroscience, neuroimaging, and artificial intelligence. A critical obstacl…
ActiveVLA: Injecting Active Perception into Vision-Language-Action Models for Precise 3D Robotic Manipulation
Zhenyang Liu, Yongchong Gu, Yikai Wang +2
Recent advances in robot manipulation have leveraged pre-trained vision-language models (VLMs) and explored integrating 3D spatial signals into these models for effective action pr…
Aligned Stable Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency
Yikai Wang, Junqiu Yu, Chenjie Cao +2
Generative image inpainting can produce realistic results even with large, irregular masks, but existing methods still suffer from two common problems: (1) Unwanted object insertio…
Spatial-Temporal Aware Visuomotor Diffusion Policy Learning
Zhenyang Liu, Yikai Wang, Kuanning Wang +3
Visual imitation learning is effective for robots to learn versatile tasks. However, many existing methods rely on behavior cloning with supervised historical trajectories, limitin…
ReasonGrounder: LVLM-Guided Hierarchical Feature Splatting for Open-Vocabulary 3D Visual Grounding and Reasoning
Zhenyang Liu, Yikai Wang, Sixiao Zheng +4
Open-vocabulary 3D visual grounding and reasoning aim to localize objects in a scene based on implicit language descriptions, even when they are occluded. This ability is crucial f…
Adaptive Pruning of Pretrained Transformer via Differential Inclusions
Yizhuo Ding, Ke Fan, Yikai Wang +2
Large transformers have demonstrated remarkable success, making it necessary to compress these models to reduce inference costs while preserving their perfor-mance. Current compres…
Unified Lexical Representation for Interpretable Visual-Language Alignment
Yifan Li, Yikai Wang, Yanwei Fu +3
Visual-Language Alignment (VLA) has gained a lot of attention since CLIP's groundbreaking work. Although CLIP performs well, the typical direct latent feature alignment lacks clari…
3D StreetUnveiler with Semantic-aware 2DGS -- a simple baseline
Jingwei Xu, Yikai Wang, Yiqun Zhao +2
Unveiling an empty street from crowded observations captured by in-car cameras is crucial for autonomous driving. However, removing all temporarily static objects, such as stopped…
Towards Global Optimal Visual In-Context Learning Prompt Selection
Chengming Xu, Chen Liu, Yikai Wang +2
Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in in-context examples to enh…
NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation
Jingyang Huo, Yikai Wang, Xuelin Qian +4
Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality…
Repositioning the Subject within Image
Yikai Wang, Chenjie Cao, Ke Fan +4
Current image manipulation primarily centers on static manipulation, such as replacing specific regions within an image or altering its overall style. In this paper, we introduce a…
Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency
Yikai Wang, Chenjie Cao, Junqiu Yu +3
Recent advances in image inpainting increasingly use generative models to handle large irregular masks. However, these models can create unrealistic inpainted images due to two mai…
Coarse-to-Fine Amodal Segmentation with Shape Prior
Jianxiong Gao, Xuelin Qian, Yikai Wang +4
Amodal object segmentation is a challenging task that involves segmenting both visible and occluded parts of an object. In this paper, we propose a novel approach, called Coarse-to…
LeftRefill: Filling Right Canvas based on Left Reference through Generalized Text-to-Image Diffusion Model
Chenjie Cao, Yunuo Cai, Qiaole Dong +2
This paper introduces LeftRefill, an innovative approach to efficiently harness large Text-to-Image (T2I) diffusion models for reference-guided image synthesis. As the name implies…
Entity-Level Text-Guided Image Manipulation
Yikai Wang, Jianan Wang, Guansong Lu +4
Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical appl…
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels
Yikai Wang, Yanwei Fu, Xinwei Sun
A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean samp…
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels
Yikai Wang, Xinwei Sun, Yanwei Fu
Noisy training set usually leads to the degradation of generalization and robustness of neural networks. In this paper, we propose using a theoretically guaranteed noisy label dete…
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
Yikai Wang, Li Zhang, Yuan Yao +1
Deep learning based models have excelled in many computer vision tasks and appear to surpass humans' performance. However, these models require an avalanche of expensive human labe…
Instance Credibility Inference for Few-Shot Learning
Yikai Wang, Chengming Xu, Chen Liu +2
Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm…