Multi-level and Multi-modal Action Anticipation
- URL: http://arxiv.org/abs/2506.02382v1
- Date: Tue, 03 Jun 2025 02:39:33 GMT
- Title: Multi-level and Multi-modal Action Anticipation
- Authors: Seulgi Kim, Ghazal Kaviani, Mohit Prabhushankar, Ghassan AlRegib,
- Abstract summary: Action anticipation, the task of predicting future actions from partially observed videos, is crucial for advancing intelligent systems.<n>We introduce textitMulti-level and Multi-modal Action Anticipation (m&m-Ant), a novel multi-modal action anticipation approach.<n>Experiments on widely used datasets, including Breakfast, 50 Salads, and DARai, demonstrate the effectiveness of our approach.
- Score: 12.921307214813357
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Action anticipation, the task of predicting future actions from partially observed videos, is crucial for advancing intelligent systems. Unlike action recognition, which operates on fully observed videos, action anticipation must handle incomplete information. Hence, it requires temporal reasoning, and inherent uncertainty handling. While recent advances have been made, traditional methods often focus solely on visual modalities, neglecting the potential of integrating multiple sources of information. Drawing inspiration from human behavior, we introduce \textit{Multi-level and Multi-modal Action Anticipation (m\&m-Ant)}, a novel multi-modal action anticipation approach that combines both visual and textual cues, while explicitly modeling hierarchical semantic information for more accurate predictions. To address the challenge of inaccurate coarse action labels, we propose a fine-grained label generator paired with a specialized temporal consistency loss function to optimize performance. Extensive experiments on widely used datasets, including Breakfast, 50 Salads, and DARai, demonstrate the effectiveness of our approach, achieving state-of-the-art results with an average anticipation accuracy improvement of 3.08\% over existing methods. This work underscores the potential of multi-modal and hierarchical modeling in advancing action anticipation and establishes a new benchmark for future research in the field. Our code is available at: https://github.com/olivesgatech/mM-ant.
Related papers
- Action Quality Assessment via Hierarchical Pose-guided Multi-stage Contrastive Regression [25.657978409890973]
Action Assessment (AQA) aims at automatic and fair evaluation of athletic performance.<n>Current methods focus on segmenting video into fixed frames, which disrupts the temporal continuity of sub-actions.<n>We propose a novel action quality assessment method through hierarchically pose-guided multi-stage contrastive regression.
arXiv Detail & Related papers (2025-01-07T10:20:16Z) - From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation [30.161471749050833]
We propose a novel end-to-end video modeling architecture that utilizes attention mechanisms, named Anticipation via Recognition and Reasoning (ARR)
ARR decomposes the action anticipation task into action recognition and reasoning tasks, and effectively learns the statistical relationship between actions by next action prediction (NAP)
In addition, to address the challenge of relationship modeling that requires extensive training data, we propose an innovative approach for the unsupervised pre-training of the decoder.
arXiv Detail & Related papers (2024-08-05T18:38:29Z) - PALM: Predicting Actions through Language Models [74.10147822693791]
We introduce PALM, an approach that tackles the task of long-term action anticipation.
Our method incorporates an action recognition model to track previous action sequences and a vision-language model to articulate relevant environmental details.
Our experimental results demonstrate that PALM surpasses the state-of-the-art methods in the task of long-term action anticipation.
arXiv Detail & Related papers (2023-11-29T02:17:27Z) - Exploiting Modality-Specific Features For Multi-Modal Manipulation
Detection And Grounding [54.49214267905562]
We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks.
Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment.
We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
arXiv Detail & Related papers (2023-09-22T06:55:41Z) - Temporal DINO: A Self-supervised Video Strategy to Enhance Action
Prediction [15.696593695918844]
This paper introduces a novel self-supervised video strategy for enhancing action prediction inspired by DINO (self-distillation with no labels)
The experimental results showcase significant improvements in prediction performance across 3D-ResNet, Transformer, and LSTM architectures.
These findings highlight the potential of our approach in diverse video-based tasks such as activity recognition, motion planning, and scene understanding.
arXiv Detail & Related papers (2023-08-08T21:18:23Z) - Inductive Attention for Video Action Anticipation [16.240254363118016]
We propose an inductive attention model, dubbed IAM, which leverages the current prior predictions as the query to infer future action.
Our method consistently outperforms the state-of-the-art anticipation models on multiple large-scale egocentric video datasets.
arXiv Detail & Related papers (2022-12-17T09:51:17Z) - AntPivot: Livestream Highlight Detection via Hierarchical Attention
Mechanism [64.70568612993416]
We formulate a new task Livestream Highlight Detection, discuss and analyze the difficulties listed above and propose a novel architecture AntPivot to solve this problem.
We construct a fully-annotated dataset AntHighlight to instantiate this task and evaluate the performance of our model.
arXiv Detail & Related papers (2022-06-10T05:58:11Z) - Self-Regulated Learning for Egocentric Video Activity Anticipation [147.9783215348252]
Self-Regulated Learning (SRL) aims to regulate the intermediate representation consecutively to produce representation that emphasizes the novel information in the frame of the current time-stamp.
SRL sharply outperforms existing state-of-the-art in most cases on two egocentric video datasets and two third-person video datasets.
arXiv Detail & Related papers (2021-11-23T03:29:18Z) - Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and
Contrastive Meta-Learning [51.03781020616402]
Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications.
We propose a few-shot fine-grained action recognition problem, aiming to recognize novel fine-grained actions with only few samples given for each class.
Although progress has been made in coarse-grained actions, existing few-shot recognition methods encounter two issues handling fine-grained actions.
arXiv Detail & Related papers (2021-08-15T02:21:01Z) - Learning Long-term Visual Dynamics with Region Proposal Interaction
Networks [75.06423516419862]
We build object representations that can capture inter-object and object-environment interactions over a long-range.
Thanks to the simple yet effective object representation, our approach outperforms prior methods by a significant margin.
arXiv Detail & Related papers (2020-08-05T17:48:00Z) - Knowledge Distillation for Action Anticipation via Label Smoothing [21.457069042129138]
Human capability to anticipate near future from visual observations and non-verbal cues is essential for developing intelligent systems.
We implement a multi-modal framework based on long short-term memory (LSTM) networks to summarize past observations and make predictions at different time steps.
Experiments show that label smoothing systematically improves performance of state-of-the-art models for action anticipation.
arXiv Detail & Related papers (2020-04-16T15:38:53Z)
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.