323 citations · 2.1k across the 141 of their papers we have counts for
144 papers
KAN or MLP: A Fairer Comparison
Runpeng Yu, Weihao Yu, Xinchao Wang
This paper does not introduce a novel method. Instead, it offers a fairer and more comprehensive comparison of KAN and MLP models across various tasks, including machine learning,…
Heavy Labels Out! Dataset Distillation with Label Space Lightening
Ruonan Yu, Songhua Liu, Zigeng Chen +2
Dataset distillation or condensation aims to condense a large-scale training dataset into a much smaller synthetic one such that the training performance of distilled and original…
MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities
Weihao Yu, Zhengyuan Yang, Lingfeng Ren +7
MM-Vet, with open-ended vision-language questions targeting at evaluating integrated capabilities, has become one of the most popular benchmarks for large multimodal model evaluati…
Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions
Yihao Ai, Yifei Qi, Bo Wang +3
Existing 2D human pose estimation research predominantly concentrates on well-lit scenarios, with limited exploration of poor lighting conditions, which are a prevalent aspect of d…
Encapsulating Knowledge in One Prompt
Qi Li, Runpeng Yu, Xinchao Wang
This paradigm encapsulates knowledge from various models into a solitary prompt without altering the original models or requiring access to the training data, which enables us to a…
LiteFocus: Accelerated Diffusion Inference for Long Audio Synthesis
Zhenxiong Tan, Xinyin Ma, Gongfan Fang +1
Latent diffusion models have shown promising results in audio generation, making notable advancements over traditional methods. However, their performance, while impressive with sh…
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled Approach
Taolin Zhang, Jiawang Bai, Zhihe Lu +4
Recent works on parameter-efficient transfer learning (PETL) show the potential to adapt a pre-trained Vision Transformer to downstream recognition tasks with only a few learnable…
Isomorphic Pruning for Vision Models
Gongfan Fang, Xinyin Ma, Michael Bi Mi +1
Structured pruning reduces the computational overhead of deep neural networks by removing redundant sub-structures. However, assessing the relative importance of different sub-stru…
Video-Infinity: Distributed Long Video Generation
Zhenxiong Tan, Xingyi Yang, Songhua Liu +1
Diffusion models have recently achieved remarkable results for video generation. Despite the encouraging performances, the generated videos are typically constrained to a small num…
Compositional Video Generation as Flow Equalization
Xingyi Yang, Xinchao Wang
Large-scale Text-to-Video (T2V) diffusion models have recently demonstrated unprecedented capability to transform natural language descriptions into stunning and photorealistic vid…
Few-Shot Anomaly Detection via Category-Agnostic Registration Learning
Chaoqin Huang, Haoyan Guan, Aofan Jiang +4
Most existing anomaly detection (AD) methods require a dedicated model for each category. Such a paradigm, despite its promising results, is computationally expensive and inefficie…
AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising
Zigeng Chen, Xinyin Ma, Gongfan Fang +2
Diffusion models have garnered significant interest from the community for their great generative ability across various applications. However, their typical multi-step sequential-…
MVGamba: Unify 3D Content Generation as State Space Sequence Modeling
Xuanyu Yi, Zike Wu, Qiuhong Shen +6
Recent 3D large reconstruction models (LRMs) can generate high-quality 3D content in sub-seconds by integrating multi-view diffusion models with scalable multi-view reconstructors.…
Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching
Xinyin Ma, Gongfan Fang, Michael Bi Mi +1
Diffusion Transformers have recently demonstrated unprecedented generative capabilities for various tasks. The encouraging results, however, come with the cost of slow inference, s…
StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization
Songhua Liu, Xin Jin, Xingyi Yang +2
Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain, making it a highly ambitious and challenging…
MambaOut: Do We Really Need Mamba for Vision?
Weihao Yu, Xinchao Wang
Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently introduced to address the quadratic complexity of the attention mechanism and subsequently…
GFlow: Recovering 4D World from Monocular Video
Shizun Wang, Xingyi Yang, Qiuhong Shen +2
Recovering 4D world from monocular video is a crucial yet challenging task. Conventional methods usually rely on the assumptions of multi-view videos, known camera parameters, or s…
Ungeneralizable Examples
Jingwen Ye, Xinchao Wang
The training of contemporary deep learning models heavily relies on publicly available data, posing a risk of unauthorized access to online data and raising concerns about data pri…
Distilled Datamodel with Reverse Gradient Matching
Jingwen Ye, Ruonan Yu, Songhua Liu +1
The proliferation of large-scale AI models trained on extensive datasets has revolutionized machine learning. With these models taking on increasingly central roles in various appl…
MindBridge: A Cross-Subject Brain Decoding Framework
Shizun Wang, Songhua Liu, Zhenxiong Tan +1
Brain decoding, a pivotal field in neuroscience, aims to reconstruct stimuli from acquired brain signals, primarily utilizing functional magnetic resonance imaging (fMRI). Currentl…
Hash3D: Training-free Acceleration for 3D Generation
Xingyi Yang, Xinchao Wang
The evolution of 3D generative modeling has been notably propelled by the adoption of 2D diffusion models. Despite this progress, the cumbersome optimization process per se present…
Unsegment Anything by Simulating Deformation
Jiahao Lu, Xingyi Yang, Xinchao Wang
Foundation segmentation models, while powerful, pose a significant risk: they enable users to effortlessly extract any objects from any digital content with a single click, potenti…
Relation Rectification in Diffusion Model
Yinwei Wu, Xingyi Yang, Xinchao Wang
Despite their exceptional generative abilities, large text-to-image diffusion models, much like skilled but careless artists, often struggle with accurately depicting visual relati…
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images
Chaoqin Huang, Aofan Jiang, Jinghao Feng +3
Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However, th…