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Sequential Modeling Enables Scalable Learning for Large vision Models

Sequential Modeling Enables Scalable Learning for Large Visi...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Bail, Yutong Geng, Xinyang Mangalam, Karttikeya Bar, Amir Yuille, Alan L. Darrell, Trevor Malik, Jitendra Efros, Alexei A. UC Berkeley BAIR Berkeley CA 94720 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350353006

We introduce a novel sequential modeling approach which enables learning a Large vision Model (LVM) without making use of any linguistic data. To do this, we define a common format, "visual sentences", in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions with-out needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion to-kens) is represented as sequences, the model can be trained to minimize a cross-entropy loss for next token prediction. By training across various scales of model architecture and data diversity, we provide empirical evidence that our models scale effectively. Many different vision tasks can be solved by designing suitable visual prompts at test time.

关键词： pretraining scaling Self-supervised Learning

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Hartley, Zane K. J. Lind, Rob J. Pound, Michael P. French, Andrew P. Univ Nottingham Wollaton Rd Nottingham NG8 1BB England Syngenta Jealotts Hill Int Res Ctr Warfield England

ISBN: (纸本)9798350365474

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Agriculture computer vision ControlNet Deep Learning Diffusion LoRA Plant Phenotyping

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Prompt Learning via Meta-Regularization

Prompt Learning via Meta-Regularization

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Park, Jinyoung Ko, Juyeon Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350353006

Pre-trained vision-language models have shown impressive success on various computer vision tasks with their zero-shot generalizability. Recently, prompt learning approaches have been explored to efficiently and effectively adapt the vision-language models to a variety of down-stream tasks. However, most existing prompt learning methods suffer from task overfitting since the general knowledge of the pre-trained vision language models is forgotten while the prompts are finetuned on a small data set from a specific target task. To address this issue, we propose a Prompt Meta-Regularization (ProMetaR) to improve the generalizability of prompt learning for vision-language models. Specifically, ProMetaR meta-learns both the regularizer and the soft prompts to harness the task-specific knowledge from the downstream tasks and task-agnostic general knowledge from the vision-language models. Further, ProMetaR augments the task to generate multiple virtual tasks to alleviate the meta-overfitting. In addition, we provide the analysis to comprehend how ProMetaR improves the generalizability of prompt tuning in the perspective of the gradient alignment. Our extensive experiments demonstrate that our ProMetaR improves the generalizability of conventional prompt learning methods under base-to-base/base-to-new and domain generalization settings. The code of ProMetaR is available at https://***/mlvlab/ProMetaR.

关键词： Meta-learning Prompt tuning vision-Language Model

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How Much You Ate? Food Portion Estimation on Spoons

How Much You Ate? Food Portion Estimation on Spoons

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sharma, Aaryam Czarnecki, Chris Chen, Yuhao Xi, Pengcheng Xu, Linlin Wong, Alexander Univ Waterloo Vis & Image Proc Lab Waterloo ON Canada Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9798350365474

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil's surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.

关键词： computer-vision estimation food nutrition volumetric

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Raghavan, Siddeshwar He, Jiangpeng Zhu, Fengqing Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350365474

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online (1)

关键词： computer vision long-tailed image classification online continual learning

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Building vision-Language Models on Solid Foundations with Masked Distillation

Building Vision-Language Models on Solid Foundations with Ma...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sameni, Sepehr Kafle, Kushal Tan, Hao Jenni, Simon Univ Bern Bern Switzerland Adobe Res San Jose CA USA

ISBN: (纸本)9798350353006

Recent advancements in vision-Language Models (VLMs) have marked a significant leap in bridging the gap between computer vision and natural language processing. However, traditional VLMs, trained through contrastive learning on limited and noisy image-text pairs, often lack the spatial and linguistic understanding to generalize well to dense vision tasks or less common languages. Our approach, Solid Foundation CLIP (SF-CLIP), circumvents this issue by implicitly building on the solid visual and language understanding of foundational models trained on vast amounts of unimodal data. SF-CLIP integrates contrastive image-text pretraining with a masked knowledge distillation from large foundational text and vision models. This methodology guides our VLM in developing robust text and image representations. As a result, SF-CLIP shows exceptional zero-shot classification accuracy and enhanced image and text retrieval capabilities, setting a new state of the art for ViT-B/16 trained on YFCC15M and CC12M. Moreover, the dense per-patch supervision enhances our zero-shot and linear probe performance in semantic segmentation tasks. A remarkable aspect of our model is its multilingual proficiency, evidenced by strong retrieval results in multiple languages despite being trained predominantly on English data. We achieve all of these improvements without sacrificing the training efficiency through our selective application of masked distillation and the inheritance of teacher word embeddings.

关键词： CLIP Distillation LLM Multilingual Multimodal Representation Learning

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Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary

Transcending the Limit of Local Window: Advanced Super-Resol...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Leheng Li, Yawei Zhou, Xingyu Zhao, Xiaorui Gu, Shuhang Univ Elect Sci & Technol China Chengdu Peoples R China Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

ISBN: (纸本)9798350353013;9798350353006

Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs), especially Transformers for super-resolution, have seen significant advancements in recent years, challenges still remain, particularly in limited receptive field caused by window-based self-attention. To address these issues, we introduce a group of auxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions, we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.

关键词： dictionary learning image super-resolution vision transformer

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OOSTraj: Out-of-Sight Trajectory Prediction With vision-Positioning Denoising

OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Posi...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Haichao Xu, Yi Lu, Hongsheng Shimizu, Takayuki Fu, Yun Northeastern Univ 360 Huntington Ave Boston MA 02115 USA Toyota Motor North Amer 465 N Bernardo Ave Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

Trajectory prediction is fundamental in computer vision and autonomous driving, particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data, neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range, physical obstructions, and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns, as they can result in missing essential, non-visible objects. To bridge this gap, we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects, our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj), setting a new benchmark for future research.

关键词： autonomous driving denosing OOSTraj Out-of-Sight Trajectory Prediction Trajectory Prediction vision-Positioning

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Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration

Dynamic Cues-Assisted Transformer for Robust Point Cloud Reg...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Hong Yan, Pei Xiang, Sihe Tan, Yihua Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Hubei Engn Res Ctr Machine Vision & Intelligent S Wuhan Peoples R China

ISBN: (纸本)9798350353006

Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have predominantly embraced a coarse-to-fine matching mechanism, with the key to matching the superpoints located in patches with interframe consistent structures. However, previous methods still face challenges with ambiguous matching, because the interference information aggregated from irrelevant regions may disturb the capture of interframe consistency relations, leading to wrong matches. To address this issue, we propose Dynamic Cues-Assisted Transformer (DCATr). Firstly, the interference from irrelevant regions is greatly reduced by constraining attention to certain cues, i.e., regions with highly correlated structures of potential corresponding superpoints. Secondly, cues-assisted attention is designed to mine the inter-frame consistency relations, while more attention is assigned to pairs with high consistent confidence in feature aggregation. Finally, a dynamic updating fashion is proposed to facilitate mining richer consistency information, further improving aggregated features' distinctiveness and relieving matching ambiguity. Extensive evaluations on indoor and outdoor standard benchmarks demonstrate that DCATr outperforms all state-of-the-art methods.

关键词： ambiguous feature matching Point cloud registration transformer

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LLaFS: When Large Language Models Meet Few-Shot Segmentation

LLaFS: When Large Language Models Meet Few-Shot Segmentation

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Lanyun Chen, Tianrun Ji, Deyi Ye, Jieping Liu, Jun Singapore Univ Technol & Design Singapore Singapore Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

This paper proposes LLaFS, the first attempt to leverage large language models (LLMs) in few-shot segmentation. In contrast to the conventional few-shot segmentation methods that only rely on the limited and biased information from the annotated support images, LLaFS leverages the vast prior knowledge gained by LLM as an effective supplement and directly uses the LLM to segment images in a few-shot manner. To enable the text-based LLM to handle image-related tasks, we carefully design an input instruction that allows the LLM to produce segmentation results represented as polygons, and propose a region-attribute table to simulate the human visual mechanism and provide multi-modal guidance. We also synthesize pseudo samples and use curriculum learning for pretraining to augment data and achieve better optimization. LLaFS achieves state-of-the-art results on multiple datasets, showing the potential of using LLMs for few-shot computer vision tasks.

关键词： Few-shot segmentation Large vision-language models

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