Multimodal Large Models Are Effective Action Anticipators
- URL: http://arxiv.org/abs/2501.00795v1
- Date: Wed, 01 Jan 2025 10:16:10 GMT
- Title: Multimodal Large Models Are Effective Action Anticipators
- Authors: Binglu Wang, Yao Tian, Shunzhou Wang, Le Yang,
- Abstract summary: ActionLLM is a novel approach that treats video sequences as successive tokens, leveraging Large Language Models to anticipate future actions.
Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer.
To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding.
- Score: 10.454791411515812
- License:
- Abstract: The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. Code is available at https://github.com/2tianyao1/ActionLLM.git.
Related papers
- SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding [66.74446220401296]
We propose SynerGen-VL, a simple yet powerful encoder-free MLLM capable of both image understanding and generation.
We introduce the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy, which effectively support high-resolution image understanding.
Our code and models shall be released.
arXiv Detail & Related papers (2024-12-12T18:59:26Z) - Can MLLMs Guide Weakly-Supervised Temporal Action Localization Tasks? [6.7065734065794835]
We introduce a novel learning paradigm termed MLLM4WTAL.
It harnesses the potential of MLLM to offer temporal action key semantics and complete semantic priors.
It achieves this by integrating two distinct modules: Key Semantic Matching (KSM) and Complete Semantic Reconstruction (CSR)
arXiv Detail & Related papers (2024-11-13T09:37:24Z) - RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks.
Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs.
In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
arXiv Detail & Related papers (2024-10-18T03:45:19Z) - Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering [53.39158264785098]
Long-term Video Question Answering (VideoQA) is a challenging vision-and-language bridging task.
We present an entirely end-to-end solution for VideoQA: Multi-granularity Contrastive cross-modal collaborative Generation model.
arXiv Detail & Related papers (2024-10-12T06:21:58Z) - Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models [12.687494201105066]
This paper proposes Traj-LLM, the first to investigate the potential of using Large Language Models (LLMs) to generate future motion from agents' past/observed trajectories and scene semantics.
LLMs' powerful comprehension abilities capture a spectrum of high-level scene knowledge and interactive information.
Emulating the human-like lane focus cognitive function, we introduce lane-aware probabilistic learning powered by the pioneering Mamba module.
arXiv Detail & Related papers (2024-05-08T09:28:04Z) - Towards Modeling Learner Performance with Large Language Models [7.002923425715133]
This paper investigates whether the pattern recognition and sequence modeling capabilities of LLMs can be extended to the domain of knowledge tracing.
We compare two approaches to using LLMs for this task, zero-shot prompting and model fine-tuning, with existing, non-LLM approaches to knowledge tracing.
While LLM-based approaches do not achieve state-of-the-art performance, fine-tuned LLMs surpass the performance of naive baseline models and perform on par with standard Bayesian Knowledge Tracing approaches.
arXiv Detail & Related papers (2024-02-29T14:06:34Z) - Model Composition for Multimodal Large Language Models [71.5729418523411]
We propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model.
Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters.
arXiv Detail & Related papers (2024-02-20T06:38:10Z) - Towards More Unified In-context Visual Understanding [74.55332581979292]
We present a new ICL framework for visual understanding with multi-modal output enabled.
First, we quantize and embed both text and visual prompt into a unified representational space.
Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them.
arXiv Detail & Related papers (2023-12-05T06:02:21Z) - 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) - MotionLM: Multi-Agent Motion Forecasting as Language Modeling [15.317827804763699]
We present MotionLM, a language model for multi-agent motion prediction.
Our approach bypasses post-hoc interactions where individual agent trajectory generation is conducted prior to interactive scoring.
The model's sequential factorization enables temporally causal conditional rollouts.
arXiv Detail & Related papers (2023-09-28T15:46:25Z)
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.