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Found 11 papers, 4 papers with code
TAAT: Think and Act from Arbitrary Texts in Text2Motion
In this challenging dataset, we benchmark existing state-of-the-art methods and propose a novel two-stage framework to extract action labels from arbitrary texts by the Large Language Model (LLM) and then generate motions from action labels.
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the base dataset.
Self-Enhancement Improves Text-Image Retrieval in Foundation Visual-Language Models
The emergence of cross-modal foundation models has introduced numerous approaches grounded in text-image retrieval.
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult.
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Text consists of a category name and a fixed number of learnable parameters which are selected from our designed attribute word bank and serve as attributes.
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning
Based on this observation, we propose a simple strategy, i. e., increasing the number of training shots, to mitigate the loss of intrinsic dimension caused by robustness-promoting regularization.
Anti-Retroactive Interference for Lifelong Learning
Second, according to the similarity between incremental knowledge and base knowledge, we design an adaptive fusion of incremental knowledge, which helps the model allocate capacity to the knowledge of different difficulties.
Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation
Research on the generalization ability of deep neural networks (DNNs) has recently attracted a great deal of attention.
Associative Adversarial Learning Based on Selective Attack
We show that our method improves the recognition accuracy of adversarial training on ImageNet by 8. 32% compared with the baseline.
Cogradient Descent for Dependable Learning
Conventional gradient descent methods compute the gradients for multiple variables through the partial derivative.
IDARTS: Interactive Differentiable Architecture Search
Differentiable Architecture Search (DARTS) improves the efficiency of architecture search by learning the architecture and network parameters end-to-end.
