96 citations · 312 across the 36 of their papers we have counts for
37 papers
Foresight Without Seeing: Latent Futures for World Action Models
Jiakai Huang, Zhongbo Wu, Siyu Xu +5
World Action Models (WAMs) connect visual prediction with robot control, but supplying predictive context often requires expensive future-video generation. Direct policies avoid th…
Reward as An Agent for Embodied World Models
Pu Li, Zhigang Lin, Qiang Wu +3
While RL has become a promising tool for refining world models, existing methods largely rely on conservative rollouts near the training distribution, limiting exploration, behavio…
Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI
Kairos Team, Fei Wang, Shan You +21
We introduce \textbf{Kairos}, a regret-aware native world-action model stack for Physical AI. Kairos is motivated by the view that a physical world model should not aim to fully si…
Seeing Realism from Simulation: Efficient Video Transfer for Vision-Language-Action Data Augmentation
Chenyu Hui, Xiaodi Huang, Siyu Xu +5
Vision-language-action (VLA) models typically rely on large-scale real-world videos, whereas simulated data, despite being inexpensive and highly parallelizable to collect, often s…
Learning to Expand Images for Efficient Visual Autoregressive Modeling
Ruiqing Yang, Kaixin Zhang, Zheng Zhang +2
Autoregressive models have recently shown great promise in visual generation by leveraging discrete token sequences akin to language modeling. However, existing approaches often su…
ActVAR: Activating Mixtures of Weights and Tokens for Efficient Visual Autoregressive Generation
Kaixin Zhang, Ruiqing Yang, Yuan Zhang +2
Visual Autoregressive (VAR) models enable efficient image generation via next-scale prediction but face escalating computational costs as sequence length grows. Existing static pru…
Learning Mask Invariant Mutual Information for Masked Image Modeling
Tao Huang, Yanxiang Ma, Shan You +1
Masked autoencoders (MAEs) represent a prominent self-supervised learning paradigm in computer vision. Despite their empirical success, the underlying mechanisms of MAEs remain ins…
Decoupled Video Generation with Chain of Training-free Diffusion Model Experts
Wenhao Li, Yichao Cao, Xiu Su +5
Video generation models hold substantial potential in areas such as filmmaking. However, current video diffusion models need high computational costs and produce suboptimal results…
LocalMamba: Visual State Space Model with Windowed Selective Scan
Tao Huang, Xiaohuan Pei, Shan You +3
Recent advancements in state space models, notably Mamba, have demonstrated significant progress in modeling long sequences for tasks like language understanding. Yet, their applic…
Active Generation for Image Classification
Tao Huang, Jiaqi Liu, Shan You +1
Recently, the growing capabilities of deep generative models have underscored their potential in enhancing image classification accuracy. However, existing methods often demand the…
Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models
Wenhao Li, Xiu Su, Yu Han +3
Diffusion models have demonstrated remarkable efficacy in various generative tasks with the predictive prowess of denoising model. Currently, diffusion models employ a uniform deno…
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
Yichao Cao, Qingfei Tang, Xiu Su +4
Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting triplets, and serving as th…
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures
Wenhao Li, Xiu Su, Shan You +3
Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencod…
CoNe: Contrast Your Neighbours for Supervised Image Classification
Mingkai Zheng, Shan You, Lang Huang +5
Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon , Triplet, and max-margin) mainly focus on grouping…
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Mingkai Zheng, Shan You, Lang Huang +4
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a…
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection
Yichao Cao, Qingfei Tang, Feng Yang +4
Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and object…
Knowledge Diffusion for Distillation
Tao Huang, Yuan Zhang, Mingkai Zheng +4
The representation gap between teacher and student is an emerging topic in knowledge distillation (KD). To reduce the gap and improve the performance, current methods often resort…
Can GPT-4 Perform Neural Architecture Search?
Mingkai Zheng, Xiu Su, Shan You +4
We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures. Our proposed approach, \textb…
Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations
Haoyu Xie, Changqi Wang, Mingkai Zheng +4
Recent breakthroughs in semi-supervised semantic segmentation have been developed through contrastive learning. In prevalent pixel-wise contrastive learning solutions, the model ma…
ScaleNet: Searching for the Model to Scale
Jiyang Xie, Xiu Su, Shan You +3
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales. Current methods either simply resort…
LightViT: Towards Light-Weight Convolution-Free Vision Transformers
Tao Huang, Lang Huang, Shan You +3
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias. Recent works thus resort to c…
Masked Distillation with Receptive Tokens
Tao Huang, Yuan Zhang, Shan You +4
Distilling from the feature maps can be fairly effective for dense prediction tasks since both the feature discriminability and localization priors can be well transferred. However…
Green Hierarchical Vision Transformer for Masked Image Modeling
Lang Huang, Shan You, Mingkai Zheng +3
We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate…
Knowledge Distillation from A Stronger Teacher
Tao Huang, Shan You, Fei Wang +2
Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-ar…