TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
- URL: http://arxiv.org/abs/2106.11297v1
- Date: Mon, 21 Jun 2021 17:55:59 GMT
- Title: TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?
- Authors: Michael S. Ryoo, AJ Piergiovanni, Anurag Arnab, Mostafa Dehghani,
Anelia Angelova
- Abstract summary: We introduce a novel visual representation learning which relies on a handful of adaptively learned tokens.
Our experiments demonstrate strong performance on several challenging benchmarks for both image and video recognition tasks.
- Score: 89.17394772676819
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we introduce a novel visual representation learning which
relies on a handful of adaptively learned tokens, and which is applicable to
both image and video understanding tasks. Instead of relying on hand-designed
splitting strategies to obtain visual tokens and processing a large number of
densely sampled patches for attention, our approach learns to mine important
tokens in visual data. This results in efficiently and effectively finding a
few important visual tokens and enables modeling of pairwise attention between
such tokens, over a longer temporal horizon for videos, or the spatial content
in images. Our experiments demonstrate strong performance on several
challenging benchmarks for both image and video recognition tasks. Importantly,
due to our tokens being adaptive, we accomplish competitive results at
significantly reduced compute amount.
Related papers
- ElasticTok: Adaptive Tokenization for Image and Video [109.75935878130582]
We introduce ElasticTok, a method that conditions on prior frames to adaptively encode a frame into a variable number of tokens.
During inference, ElasticTok can dynamically allocate tokens when needed.
Our evaluations on images and video demonstrate the effectiveness of our approach in efficient token usage.
arXiv Detail & Related papers (2024-10-10T20:54:15Z) - VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation [66.00245701441547]
We introduce a novel approach to reduce vision compute by leveraging redundant vision tokens "skipping layers" rather than decreasing the number of vision tokens.
Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video.
arXiv Detail & Related papers (2024-08-29T17:21:58Z) - Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning [41.81009725976217]
We provide semantically-meaningful visual tokens to transformer encoders within a vision-language pre-training framework.
We demonstrate notable improvements over ViTs in learned representation quality across text-to-image and image-to-text retrieval tasks.
arXiv Detail & Related papers (2024-05-26T01:46:22Z) - LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation [37.72775203647514]
This paper proposes to use learnable meta tokens to formulate sparse tokens, which effectively learn key information and improve inference speed.
By employing Dual Cross-Attention (DCA) in the early stages with dense visual tokens, we obtain the hierarchical architecture LeMeViT with various sizes.
Experimental results in classification and dense prediction tasks show that LeMeViT has a significant $1.7 times$ speedup, fewer parameters, and competitive performance compared to the baseline models.
arXiv Detail & Related papers (2024-05-16T03:26:06Z) - LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models [35.88374542519597]
Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model.
Recent LMMs incorporate more complex visual inputs, such as high-resolution images and videos, which further increases the number of visual tokens significantly.
We propose PruMerge, a novel adaptive visual token reduction strategy that significantly reduces the number of visual tokens without compromising the performance of LMMs.
arXiv Detail & Related papers (2024-03-22T17:59:52Z) - Token-Label Alignment for Vision Transformers [93.58540411138164]
Data mixing strategies (e.g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs)
We identify a token fluctuation phenomenon that has suppressed the potential of data mixing strategies.
We propose a token-label alignment (TL-Align) method to trace the correspondence between transformed tokens and the original tokens to maintain a label for each token.
arXiv Detail & Related papers (2022-10-12T17:54:32Z) - Self-attention on Multi-Shifted Windows for Scene Segmentation [14.47974086177051]
We explore the effective use of self-attention within multi-scale image windows to learn descriptive visual features.
We propose three different strategies to aggregate these feature maps to decode the feature representation for dense prediction.
Our models achieve very promising performance on four public scene segmentation datasets.
arXiv Detail & Related papers (2022-07-10T07:36:36Z) - Align before Fuse: Vision and Language Representation Learning with
Momentum Distillation [52.40490994871753]
We introduce a contrastive loss to representations BEfore Fusing (ALBEF) through cross-modal attention.
We propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model.
ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks.
arXiv Detail & Related papers (2021-07-16T00:19:22Z) - Scaling Up Visual and Vision-Language Representation Learning With Noisy
Text Supervision [57.031588264841]
We leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps.
A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss.
We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme.
arXiv Detail & Related papers (2021-02-11T10:08:12Z)
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.