MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition

• URL: http://arxiv.org/abs/2305.07214v1
• Date: Fri, 12 May 2023 03:05:40 GMT
• Title: MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition
• Authors: Xinyu Gong, Sreyas Mohan, Naina Dhingra, Jean-Charles Bazin, Yilei Li, Zhangyang Wang, Rakesh Ranjan
• Abstract summary: "Multimodal Generalization" (MMG) aims to study how systems can generalize when data from certain modalities is limited or even completely missing. MMG consists of two novel scenarios, designed to support security, and efficiency considerations in real-world applications. New fusion module with modality dropout training, contrastive-based alignment training, and a novel cross-modal loss for better few-shot performance.
• Score: 73.80088682784587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data from certain modalities is limited or even completely missing. We thoroughly investigate MMG in the context of standard supervised action recognition and the more challenging few-shot setting for learning new action categories. MMG consists of two novel scenarios, designed to support security, and efficiency considerations in real-world applications: (1) missing modality generalization where some modalities that were present during the train time are missing during the inference time, and (2) cross-modal zero-shot generalization, where the modalities present during the inference time and the training time are disjoint. To enable this investigation, we construct a new dataset MMG-Ego4D containing data points with video, audio, and inertial motion sensor (IMU) modalities. Our dataset is derived from Ego4D dataset, but processed and thoroughly re-annotated by human experts to facilitate research in the MMG problem. We evaluate a diverse array of models on MMG-Ego4D and propose new methods with improved generalization ability. In particular, we introduce a new fusion module with modality dropout training, contrastive-based alignment training, and a novel cross-modal prototypical loss for better few-shot performance. We hope this study will serve as a benchmark and guide future research in multimodal generalization problems. The benchmark and code will be available at https://github.com/facebookresearch/MMG_Ego4D.

Related papers

• RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection [61.71770293720491]
  We propose a novel two-stage Robust modAlity-imcomplete fusing and Detecting frAmewoRk, abbreviated as RADAR. Our bootstrapping philosophy is to enhance two stages in MIIAD, improving the robustness of the Multimodal Transformer. Our experimental results demonstrate that the proposed RADAR significantly surpasses conventional MIAD methods in terms of effectiveness and robustness.
  arXiv  Detail & Related papers  (2024-10-02T16:47:55Z)
• HyperMM : Robust Multimodal Learning with Varying-sized Inputs [4.377889826841039]
  HyperMM is an end-to-end framework designed for learning with varying-sized inputs. We introduce a novel strategy for training a universal feature extractor using a conditional hypernetwork. We experimentally demonstrate the advantages of our method in two tasks: Alzheimer's disease detection and breast cancer classification.
  arXiv  Detail & Related papers  (2024-07-30T12:13:18Z)
• Combating Missing Modalities in Egocentric Videos at Test Time [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues. We propose a novel approach to address this issue at test time without requiring retraining. MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Exploring Missing Modality in Multimodal Egocentric Datasets [89.76463983679058]
  We introduce a novel concept -Missing Modality Token (MMT)-to maintain performance even when modalities are absent. Our method mitigates the performance loss, reducing it from its original $sim 30%$ drop to only $sim 10%$ when half of the test set is modal-incomplete.
  arXiv  Detail & Related papers  (2024-01-21T11:55:42Z)
• VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias [17.107961913114778]
  multimodal misinformation is a growing problem on social media platforms. In this study, we investigate and identify the presence of unimodal bias in widely-used MMD benchmarks. We introduce a new method -- termed Crossmodal HArd Synthetic MisAlignment (CHASMA) -- for generating realistic synthetic training data.
  arXiv  Detail & Related papers  (2023-04-27T12:28:29Z)
• Deep Multimodal Fusion for Generalizable Person Re-identification [15.250738959921872]
  DMF is a Deep Multimodal Fusion network for the general scenarios on person re-identification task. Rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. A realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world.
  arXiv  Detail & Related papers  (2022-11-02T07:42:48Z)
• Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities [76.08541852988536]
  We propose to use invariant features for a missing modality imagination network (IF-MMIN) We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
  arXiv  Detail & Related papers  (2022-10-27T12:16:25Z)
• Meta-Learning with Self-Improving Momentum Target [72.98879709228981]
  We propose Self-improving Momentum Target (SiMT) to improve the performance of a meta-learner. SiMT generates the target model by adapting from the temporal ensemble of the meta-learner. We show that SiMT brings a significant performance gain when combined with a wide range of meta-learning methods.
  arXiv  Detail & Related papers  (2022-10-11T06:45:15Z)

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.