21 citations · 54 across the 12 of their papers we have counts for
12 papers
DisCo: Disentangled Control for Realistic Human Dance Generation
Tan Wang, Linjie Li, Kevin Lin +6
Generative AI has made significant strides in computer vision, particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements, it remains challenging…
Explaining Language Models' Predictions with High-Impact Concepts
Ruochen Zhao, Shafiq Joty, Yongjie Wang +1
The emergence of large-scale pretrained language models has posed unprecedented challenges in deriving explanations of why the model has made some predictions. Stemmed from the com…
Equivariant Similarity for Vision-Language Foundation Models
Tan Wang, Kevin Lin, Linjie Li +5
This study explores the concept of equivariance in vision-language foundation models (VLMs), focusing specifically on the multimodal similarity function that is not only the major…
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data
Tan Wang, Qianru Sun, Sugiri Pranata +2
We are interested in learning robust models from insufficient data, without the need for any externally pre-trained checkpoints. First, compared to sufficient data, we show why ins…
ClothFormer:Taming Video Virtual Try-on in All Module
Jianbin Jiang, Tan Wang, He Yan +1
The task of video virtual try-on aims to fit the target clothes to a person in the video with spatio-temporal consistency. Despite tremendous progress of image virtual try-on, they…
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation
Zhaozheng Chen, Tan Wang, Xiongwei Wu +3
Extracting class activation maps (CAM) is arguably the most standard step of generating pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the crux…
Self-Supervised Learning Disentangled Group Representation as Feature
Tan Wang, Zhongqi Yue, Jianqiang Huang +2
A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In…
Free Lunch for Co-Saliency Detection: Context Adjustment
Lingdong Kong, Prakhar Ganesh, Tan Wang +3
We unveil a long-standing problem in the prevailing co-saliency detection systems: there is indeed inconsistency between training and testing. Constructing a high-quality co-salien…
Causal Attention for Unbiased Visual Recognition
Tan Wang, Chang Zhou, Qianru Sun +1
Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different back…
Counterfactual Zero-Shot and Open-Set Visual Recognition
Zhongqi Yue, Tan Wang, Hanwang Zhang +2
We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only t…
Visual Commonsense R-CNN
Tan Wang, Jianqiang Huang, Hanwang Zhang +1
We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual regi…
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
Tan Wang, Xing Xu, Yang Yang +3
A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on…