Search Results for author: Kaihua Tang

Found 12 papers, 12 papers with code

Generalized Logit Adjustment: Calibrating Fine-tuned Models by Removing Label Bias in Foundation Models

1 code implementation NeurIPS 2023 Beier Zhu, Kaihua Tang, Qianru Sun, Hanwang Zhang

In this study, we systematically examine the biases in foundation models and demonstrate the efficacy of our proposed Generalized Logit Adjustment (GLA) method.

Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization

1 code implementation6 Aug 2022 Jiaxin Qi, Kaihua Tang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang

If the context in every class is evenly distributed, OOD would be trivial because the context can be easily removed due to an underlying principle: class is invariant to context.

Out-of-Distribution Generalization

Identifying Hard Noise in Long-Tailed Sample Distribution

1 code implementation27 Jul 2022 Xuanyu Yi, Kaihua Tang, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang

Such imbalanced training data makes a classifier less discriminative for the tail classes, whose previously "easy" noises are now turned into "hard" ones -- they are almost as outliers as the clean tail samples.


Invariant Feature Learning for Generalized Long-Tailed Classification

1 code implementation19 Jul 2022 Kaihua Tang, Mingyuan Tao, Jiaxin Qi, Zhenguang Liu, Hanwang Zhang

In fact, even if the class is balanced, samples within each class may still be long-tailed due to the varying attributes.

Attribute Classification +1

Adversarial Visual Robustness by Causal Intervention

2 code implementations17 Jun 2021 Kaihua Tang, Mingyuan Tao, Hanwang Zhang

As these visual confounders are imperceptible in general, we propose to use the instrumental variable that achieves causal intervention without the need for confounder observation.

Adversarial Robustness

Distilling Causal Effect of Data in Class-Incremental Learning

1 code implementation CVPR 2021 Xinting Hu, Kaihua Tang, Chunyan Miao, Xian-Sheng Hua, Hanwang Zhang

We propose a causal framework to explain the catastrophic forgetting in Class-Incremental Learning (CIL) and then derive a novel distillation method that is orthogonal to the existing anti-forgetting techniques, such as data replay and feature/label distillation.

Class Incremental Learning Incremental Learning

Counterfactual VQA: A Cause-Effect Look at Language Bias

1 code implementation CVPR 2021 Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, Ji-Rong Wen

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language.

counterfactual Counterfactual Inference +2

Learning to Segment the Tail

1 code implementation CVPR 2020 Xinting Hu, Yi Jiang, Kaihua Tang, Jingyuan Chen, Chunyan Miao, Hanwang Zhang

Real-world visual recognition requires handling the extreme sample imbalance in large-scale long-tailed data.

Few-Shot Learning Incremental Learning

Unbiased Scene Graph Generation from Biased Training

6 code implementations CVPR 2020 Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, Hanwang Zhang

Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".

Causal Inference counterfactual +2

Auto-Encoding Scene Graphs for Image Captioning

2 code implementations CVPR 2019 Xu Yang, Kaihua Tang, Hanwang Zhang, Jianfei Cai

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions.

Decoder Image Captioning +2

Learning to Compose Dynamic Tree Structures for Visual Contexts

6 code implementations CVPR 2019 Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, Wei Liu

We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A.

Graph Generation Panoptic Scene Graph Generation +2

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