Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention

• URL: http://arxiv.org/abs/2211.11701v1
• Date: Mon, 21 Nov 2022 18:22:39 GMT
• Title: Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
• Authors: Zineng Tang, Jaemin Cho, Jie Lei, Mohit Bansal
• Abstract summary: We present Perceiver-VL, a vision-and-language framework that efficiently handles high-dimensional multimodal inputs such as long videos and text. Our framework scales with linear complexity, in contrast to the quadratic complexity of self-attention used in many state-of-the-art transformer-based models.
• Score: 100.81495948184649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Perceiver-VL, a vision-and-language framework that efficiently handles high-dimensional multimodal inputs such as long videos and text. Powered by the iterative latent cross-attention of Perceiver, our framework scales with linear complexity, in contrast to the quadratic complexity of self-attention used in many state-of-the-art transformer-based models. To further improve the efficiency of our framework, we also study applying LayerDrop on cross-attention layers and introduce a mixed-stream architecture for cross-modal retrieval. We evaluate Perceiver-VL on diverse video-text and image-text benchmarks, where Perceiver-VL achieves the lowest GFLOPs and latency while maintaining competitive performance. In addition, we also provide comprehensive analyses of various aspects of our framework, including pretraining data, scalability of latent size and input size, dropping cross-attention layers at inference to reduce latency, modality aggregation strategy, positional encoding, and weight initialization strategy. Our code and checkpoints are available at: https://github.com/zinengtang/Perceiver_VL

Related papers

• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning [38.26304604660713]
  ADEM-VL is an efficient vision-language method that tunes models based on pretrained large language models. Our framework surpasses existing methods by an average accuracy of 0.77% on ScienceQA dataset.
  arXiv  Detail & Related papers  (2024-10-23T11:31:06Z)
• DeepSeek-VL: Towards Real-World Vision-Language Understanding [24.57011093316788]
  We present DeepSeek-VL, an open-source Vision-Language (VL) Model for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios. We create a use case taxonomy from real user scenarios and construct an instruction tuning dataset.
  arXiv  Detail & Related papers  (2024-03-08T18:46:00Z)
• APoLLo: Unified Adapter and Prompt Learning for Vision Language Models [58.9772868980283]
  We present APoLLo, a unified multi-modal approach that combines Adapter and Prompt learning for Vision-Language models. APoLLo achieves a relative gain up to 6.03% over MaPLe (SOTA) on novel classes for 10 diverse image recognition datasets.
  arXiv  Detail & Related papers  (2023-12-04T01:42:09Z)
• Towards Unified Token Learning for Vision-Language Tracking [65.96561538356315]
  We present a vision-language (VL) tracking pipeline, termed textbfMMTrack, which casts VL tracking as a token generation task. Our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target.
  arXiv  Detail & Related papers  (2023-08-27T13:17:34Z)
• Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning [16.902924543372713]
  State-of-the-art methods achieve impressive performance by pre-training on large-scale datasets. We propose an efficient framework for multimodal alignment by introducing a novel visual semantic module. Experiments show that the proposed ASH-Nets achieve competitive results.
  arXiv  Detail & Related papers  (2023-08-18T10:40:25Z)
• VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment [52.489874804051304]
  VoLTA is a new vision-language pre-training paradigm that only utilizes image-caption data but fine-grained region-level image understanding. VoLTA pushes multi-modal fusion deep into the uni-modal backbones during pre-training. Experiments on a wide range of vision- and vision-language downstream tasks demonstrate the effectiveness of VoLTA.
  arXiv  Detail & Related papers  (2022-10-09T01:49:58Z)
• COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval [59.15034487974549]
  We propose a novel COllaborative Two-Stream vision-language pretraining model termed COTS for image-text retrieval. Our COTS achieves the highest performance among all two-stream methods and comparable performance with 10,800X faster in inference. Importantly, our COTS is also applicable to text-to-video retrieval, yielding new state-ofthe-art on the widely-used MSR-VTT dataset.
  arXiv  Detail & Related papers  (2022-04-15T12:34:47Z)
• Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation [5.281694565226513]
  We apply contrastive learning to enhance the discriminative power of the multi-scale features extracted by semantic segmentation networks. By first mapping the encoder's multi-scale representations to a common feature space, we instantiate a novel form of supervised local-global constraint.
  arXiv  Detail & Related papers  (2022-03-25T01:24:24Z)

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.