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Found 11 papers, 11 papers with code
Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization
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.
Identifying Hard Noise in Long-Tailed Sample Distribution
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
In fact, even if the class is balanced, samples within each class may still be long-tailed due to the varying attributes.
Ranked #1 on
Long-tail Learning
on ImageNet-GLT
Adversarial Visual Robustness by Causal Intervention
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.
Distilling Causal Effect of Data in Class-Incremental Learning
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.
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.
Ranked #29 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Counterfactual VQA: A Cause-Effect Look at Language Bias
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.
Learning to Segment the Tail
Real-world visual recognition requires handling the extreme sample imbalance in large-scale long-tailed data.
Unbiased Scene Graph Generation from Biased Training
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".
Ranked #1 on
Scene Graph Generation
on Visual Genome
Auto-Encoding Scene Graphs for Image Captioning
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.
Learning to Compose Dynamic Tree Structures for Visual Contexts
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.
Ranked #3 on
Panoptic Scene Graph Generation
on PSG Dataset
