TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations

• URL: http://arxiv.org/abs/2409.03206v1
• Date: Thu, 5 Sep 2024 02:54:17 GMT
• Title: TC-LLaVA: Rethinking the Transfer from Image to Video Understanding with Temporal Considerations
• Authors: Mingze Gao, Jingyu Liu, Mingda Li, Jiangtao Xie, Qingbin Liu, Bo Zhao, Xi Chen, Hui Xiong,
• Abstract summary: We propose two strategies to enhance the model's capability in video understanding tasks. The first approach focuses on the enhancement of Rotary Position Embedding (RoPE) with Temporal-Aware Dual RoPE. The second approach involves enhancing the Attention Mask with the Frame-wise Block Causal Attention Mask.
• Score: 23.188508465235717
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) have significantly improved performance across various image-language applications. Recently, there has been a growing interest in adapting image pre-trained MLLMs for video-related tasks. However, most efforts concentrate on enhancing the vision encoder and projector components, while the core part, Large Language Models (LLMs), remains comparatively under-explored. In this paper, we propose two strategies to enhance the model's capability in video understanding tasks by improving inter-layer attention computation in LLMs. Specifically, the first approach focuses on the enhancement of Rotary Position Embedding (RoPE) with Temporal-Aware Dual RoPE, which introduces temporal position information to strengthen the MLLM's temporal modeling capabilities while preserving the relative position relationships of both visual and text tokens. The second approach involves enhancing the Attention Mask with the Frame-wise Block Causal Attention Mask, a simple yet effective method that broadens visual token interactions within and across video frames while maintaining the causal inference mechanism. Based on these proposed methods, we adapt LLaVA for video understanding tasks, naming it Temporal-Considered LLaVA (TC-LLaVA). Our TC-LLaVA achieves new state-of-the-art performance across various video understanding benchmarks with only supervised fine-tuning (SFT) on video-related datasets.

Related papers

• Efficient Transfer Learning for Video-language Foundation Models [13.166348605993292]
  We propose a simple yet effective Multi-modal Spatio-supervised (MSTA) to improve the alignment between representations in the text and vision branches. We evaluate the effectiveness of our approach across four tasks: zero-shot transfer, few-shot learning, base-to-valiant, and fully-language learning.
  arXiv  Detail & Related papers  (2024-11-18T01:25:58Z)
• 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)
• Dense Connector for MLLMs [89.50595155217108]
  We introduce the Dense Connector - a plug-and-play vision-language connector that significantly enhances existing MLLMs. Building on this, we also propose the Efficient Dense Connector, which achieves performance comparable to LLaVA-v1.5 with only 25% of the visual tokens. Our model, trained solely on images, showcases remarkable zero-shot capabilities in video understanding as well.
  arXiv  Detail & Related papers  (2024-05-22T16:25:03Z)
• ST-LLM: Large Language Models Are Effective Temporal Learners [58.79456373423189]
  Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation. How to effectively encode and understand videos in video-based dialogue systems remains to be solved. We propose ST-LLM, an effective video-LLM baseline with spatial-temporal sequence modeling inside LLM.
  arXiv  Detail & Related papers  (2024-03-30T10:11:26Z)
• Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion [70.9767518332692]
  Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. We propose a two phase paradigm, browse-and-concentrate, to enable in-depth multimodal context fusion.
  arXiv  Detail & Related papers  (2024-02-19T14:59:07Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding [112.3913646778859]
  We propose a simple yet effective video-language modeling framework, S-ViLM. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features. S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T22:45:07Z)
• Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring [82.84513669453744]
  Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs. We revisit temporal modeling in the context of image-to-video knowledge transferring. We present a simple and effective temporal modeling mechanism extending CLIP model to diverse video tasks.
  arXiv  Detail & Related papers  (2023-01-26T14:12:02Z)

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.