A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023

• URL: http://arxiv.org/abs/2307.06569v1
• Date: Thu, 13 Jul 2023 05:54:05 GMT
• Title: A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023
• Authors: Yi Cheng, Ziwei Xu, Fen Fang, Dongyun Lin, Hehe Fan, Yongkang Wong, Ying Sun, Mohan Kankanhalli
• Abstract summary: We present our findings from a study conducted on the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action Recognition. Our research focuses on the innovative application of a differentiable logic loss in the training to leverage the co-occurrence relations between verb and noun. Our final submission (entitled NS-LLM') achieved the first place in terms of top-1 action recognition accuracy.
• Score: 23.323548254515494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this technical report, we present our findings from a study conducted on the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action Recognition. Our research focuses on the innovative application of a differentiable logic loss in the training to leverage the co-occurrence relations between verb and noun, as well as the pre-trained Large Language Models (LLMs) to generate the logic rules for the adaptation to unseen action labels. Specifically, the model's predictions are treated as the truth assignment of a co-occurrence logic formula to compute the logic loss, which measures the consistency between the predictions and the logic constraints. By using the verb-noun co-occurrence matrix generated from the dataset, we observe a moderate improvement in model performance compared to our baseline framework. To further enhance the model's adaptability to novel action labels, we experiment with rules generated using GPT-3.5, which leads to a slight decrease in performance. These findings shed light on the potential and challenges of incorporating differentiable logic and LLMs for knowledge extraction in unsupervised domain adaptation for action recognition. Our final submission (entitled `NS-LLM') achieved the first place in terms of top-1 action recognition accuracy.

Related papers

• Supervised Gradual Machine Learning for Aspect Category Detection [0.9857683394266679]
  Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence. We propose a novel approach to tackle the ACD task by combining Deep Neural Networks (DNNs) with Gradual Machine Learning (GML) in a supervised setting.
  arXiv  Detail & Related papers  (2024-04-08T07:21:46Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• Latent Alignment with Deep Set EEG Decoders [44.128689862889715]
  We introduce the Latent Alignment method that won the Benchmarks for EEG Transfer Learning competition. We present its formulation as a deep set applied on the set of trials from a given subject. Our experimental results show that performing statistical distribution alignment at later stages in a deep learning model is beneficial to the classification accuracy.
  arXiv  Detail & Related papers  (2023-11-29T12:40:45Z)
• Domain Adaptation with Adversarial Training on Penultimate Activations [82.9977759320565]
  Enhancing model prediction confidence on unlabeled target data is an important objective in Unsupervised Domain Adaptation (UDA) We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features.
  arXiv  Detail & Related papers  (2022-08-26T19:50:46Z)
• Adaptive Fine-Grained Predicates Learning for Scene Graph Generation [122.4588401267544]
  General Scene Graph Generation (SGG) models tend to predict head predicates and re-balancing strategies prefer tail categories. We propose an Adaptive Fine-Grained Predicates Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates for SGG. Our proposed model-agnostic strategy significantly boosts performance of benchmark models on VG-SGG and GQA-SGG datasets by up to 175% and 76% on Mean Recall@100, achieving new state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-11T03:37:57Z)
• LogiGAN: Learning Logical Reasoning via Adversarial Pre-training [58.11043285534766]
  We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Inspired by the facilitation effect of reflective thinking in human learning, we simulate the learning-thinking process with an adversarial Generator-Verifier architecture. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets.
  arXiv  Detail & Related papers  (2022-05-18T08:46:49Z)
• Algorithms and Theory for Supervised Gradual Domain Adaptation [19.42476993856205]
  We study the problem of supervised gradual domain adaptation, where labeled data from shifting distributions are available to the learner along the trajectory. Under this setting, we provide the first generalization upper bound on the learning error under mild assumptions. Our results are algorithm agnostic for a range of loss functions, and only depend linearly on the averaged learning error across the trajectory.
  arXiv  Detail & Related papers  (2022-04-25T13:26:11Z)
• RuleBert: Teaching Soft Rules to Pre-trained Language Models [21.69870624809201]
  We introduce a classification task where, given facts and soft rules, the PLM should return a prediction with a probability for a given hypothesis. We propose a revised loss function that enables the PLM to learn how to predict precise probabilities for the task. Our evaluation results show that the resulting fine-tuned models achieve very high performance, even on logical rules that were unseen at training.
  arXiv  Detail & Related papers  (2021-09-24T16:19:25Z)
• Incremental Verification of Fixed-Point Implementations of Neural Networks [0.19573380763700707]
  We develop and evaluate a novel symbolic verification framework using incremental bounded model checking (BMC), satisfiability modulo theories (SMT), and invariant inference. Our approach was able to verify and produce examples for 85.8% of 21 test cases considering different input images, and 100% of the properties related to covering methods.
  arXiv  Detail & Related papers  (2020-12-21T10:03:44Z)
• Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation [116.48885692054724]
  We propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation. We develop a deep Q-learning model to select both accurate and representative pseudo-labeled instances. Our proposed method is evaluated on several benchmark datasets for SSDA, and demonstrates superior performance to all the comparison methods.
  arXiv  Detail & Related papers  (2020-12-07T03:37:38Z)
• Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation [49.295165476818866]
  This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation. Existing approaches usually regard the pseudo label as the ground truth to fully exploit the unlabeled target-domain data. This paper proposes to explicitly estimate the prediction uncertainty during training to rectify the pseudo label learning.
  arXiv  Detail & Related papers  (2020-03-08T12:37:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.