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LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

LLM-Seg: Bridging Image Segmentation and Large Language Mode...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Junchi Wang Lei Ke ETH Zurich

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user intention via large language model reasoning and then segment the corresponding target. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg effectively connects the current foundational Segmentation Anything Model and the LLM by mask proposals selection. For the dataset, we propose an automatic data generation pipeline and construct a new reasoning segmentation dataset named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive performance compared with existing methods. Furthermore, our proposed pipeline can efficiently produce high-quality reasoning segmentation datasets. The LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark for training and evaluating various reasoning segmentation approaches. Our code, models and dataset are at https://***/wangjunchi/LLMSeg.

关键词： Training Image segmentation Large language models Design methodology Pipelines Cognition pattern recognition

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AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries

AdCo: Adversarial Contrast for Efficient Learning of Unsuper...

引用

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

作者： Hu, Qianjiang Wang, Xiao Hu, Wei Qi, Guo-Jun Peking Univ Beijing Peoples R China Purdue Univ W Lafayette IN 47907 USA Lab MAchine Percept & LEarning MAPLE Beijing Peoples R China

ISBN: (纸本)9781665445092

Contrastive learning relies on constructing a collection of negative examples that are sufficiently hard to discriminate against positive queries when their representations are self-trained. Existing contrastive learning methods either maintain a queue of negative samples over minibatches while only a small portion of them are updated in an iteration, or only use the other examples from the current minibatch as negatives. They could not closely track the change of the learned representation over iterations by updating the entire queue as a whole, or discard the useful information from the past minibatches. Alternatively, we present to directly learn a set of negative adversaries playing against the self-trained representation. Two players, the representation network and negative adversaries, are alternately updated to obtain the most challenging negative examples against which the representation of positive queries will be trained to discriminate. We further show that the negative adversaries are updated towards a weighted combination of positive queries by maximizing the adversarial contrastive loss, thereby allowing them to closely track the change of representations over time. Experiment results demonstrate the proposed Adversarial Contrastive (AdCo) model not only achieves superior performances (a top-1 accuracy of 73.2% over 200 epochs and 75.7% over 800 epochs with linear evaluation on ImageNet), but also can be pre-trained more efficiently with much shorter GPU time and fewer epochs.

关键词： Learning systems computer vision Codes Graphics processing units pattern recognition Task analysis

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Learning by Watching

Learning by Watching

引用

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

作者： Zhang, Jimuyang Ohn-Bar, Eshed Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for learning to drive preclude such a possibility as they assume direct access to an instrumented ego-vehicle with fully known observations and expert driver actions. However, such measurements cannot be directly accessed for the non-ego vehicles when learning by watching others. Therefore, in an application where data is regarded as a highly valuable asset, current approaches completely discard the vast portion of the training data that can be potentially obtained through indirect observation of surrounding vehicles. Motivated by this key insight, we propose the Learning by Watching (LbW) framework which enables learning a driving policy without requiring full knowledge of neither the state nor expert actions. To increase its data, i.e., with new perspectives and maneuvers, LbW makes use of the demonstrations of other vehicles in a given scene by (1) transforming the ego-vehicle's observations to their points of view, and (2) inferring their expert actions. Our LbW agent learns more robust driving policies while enabling data-efficient learning, including quick adaptation of the policy to rare and novel scenarios. In particular, LbW drives robustly even with a fraction of available driving data required by existing methods, achieving an average success rate of 92% on the original CARLA benchmark with only 30 minutes of total driving data and 82% with only 10 minutes.

关键词： computer vision Instruments Current measurement Training data Benchmark testing pattern recognition Noise measurement

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Cheng Chang Keyu Long Zijian Li Himanshu Rai Layer 6 AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset’s distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/himsR/DataCVChallenge-2024/tree/main

关键词： Training Visualization computer vision Codes Annotations Filtering conferences

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Adapting the Segment Anything Model During Usage in Novel Situations

Adapting the Segment Anything Model During Usage in Novel Si...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Robin Schön Julian Lorenz Katja Ludwig Rainer Lienhart Universität Augsburg Augsburg

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The interactive segmentation task consists in the creation of object segmentation masks based on user interactions. The most common way to guide a model towards producing a correct segmentation consists in clicks on the object and background. The recently published Segment Anything Model (SAM) supports a generalized version of the interactive segmentation problem and has been trained on an object segmentation dataset which contains 1.1B masks. Though being trained extensively and with the explicit purpose of serving as a foundation model, we show significant limitations of SAM when being applied for interactive segmentation on novel domains or object types. On the used datasets, SAM displays a failure rate FR 30 @90 of up to 72.6%. Since we still want such foundation models to be immediately applicable, we present a framework that can adapt SAM during immediate usage. For this we will leverage the user interactions and masks, which are constructed during the interactive segmentation process. We use this information to generate pseudo-labels, which we use to compute a loss function and optimize a part of the SAM model. The presented method causes a relative reduction of up to 48.1% in the FR 20 @85 and 46.6% in the FR 30 @90 metrics.

关键词： Measurement Adaptation models Image segmentation computer vision Computational modeling conferences Object segmentation

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Learning by Aligning Videos in Time

Learning by Aligning Videos in Time

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

作者： Haresh, Sanjay Kumar, Sateesh Coskun, Huseyin Syed, Shahram N. Konin, Andrey Zia, M. Zeeshan Quoc-Huy Tran Retrocausal Inc Seattle WA 98052 USA

ISBN: (纸本)9781665445092

We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task, while exploiting both frame-level and video-level information. We leverage a novel combination of temporal alignment loss and temporal regularization terms, which can be used as supervision signals for training an encoder network. Specifically, the temporal alignment loss (i.e., Soft-DTW) aims for the minimum cost for temporally aligning videos in the embedding space. However, optimizing solely for this term leads to trivial solutions, particularly, one where all frames get mapped to a small cluster in the embedding space. To overcome this problem, we propose a temporal regularization term (i.e., Contrastive-IDM) which encourages different frames to be mapped to different points in the embedding space. Extensive evaluations on various tasks, including action phase classification, action phase progression, and fine-grained frame retrieval, on three datasets, namely Pouring, Penn Action, and IKEA ASM, show superior performance of our approach over state-of-the-art methods for self-supervised representation learning from videos. In addition, our method provides significant performance gain where labeled data is lacking.

关键词： Training computer vision Costs Performance gain pattern recognition Task analysis

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Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection

Dynamic Addition of Noise in a Diffusion Model for Anomaly D...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Justin Tebbe Jawad Tayyub Otto von Guericke University Magdeburg Magdeburg Germany Endress + Hauser Maulburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction. Despite their merits, they struggle to localize anomalies of varying scales, especially larger anomalies such as entire missing components. Addressing this, we present a novel framework that enhances the capability of diffusion models, by extending the previous introduced implicit conditioning approach [24] in three significant ways. First, we incorporate a dynamic step size computation that allows for variable noising steps in the forward process guided by an initial anomaly prediction. Second, we demonstrate that denoising an only scaled input, without any added noise, outperforms conventional denoising process. Third, we project images in a latent space to abstract away from fine details that interfere with reconstruction of large missing components. Additionally, we propose a fine-tuning mechanism that facilitates the model to effectively grasp the nuances of the target domain. Our method undergoes rigorous evaluation on prominent anomaly detection datasets VisA, BTAD and MVTec yielding strong performance. Importantly, our framework effectively localizes anomalies regardless of their scale, marking a pivotal advancement in diffusion-based anomaly detection.

关键词： Location awareness Technological innovation computer vision conferences Noise reduction Noise Diffusion models

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Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation

Cross-Domain Gradient Discrepancy Minimization for Unsupervi...

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

作者： Du, Zhekai Li, Jingjing Su, Hongzu Zhu, Lei Lu, Ke Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Shandong Normal Univ Jinan Shandong Peoples R China

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned from a well-labeled source domain to an unlabled target domain. Recently, adversarial domain adaptation with two distinct classifiers (bi-classifier) has been introduced into UDA which is effective to align distributions between different domains. Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers. However, the similarity of the outputs cannot guarantee the accuracy of target samples, i.e., traget samples may match to wrong categories even if the discrepancy between two classifiers is small. To challenge this issue, in this paper, we propose a cross-domain gradient discrepancy minimization (CGDM) method which explicitly minimizes the discrepancy of gradients generated by source samples and target samples. Specifically, the gradient gives a cue for the semantic information of target samples so it can be used as a good supervision to improve the accuracy of target samples. In order to compute the gradient signal of target smaples, we further obtain target pseudo labels through a clustering-based self-supervised learning. Extensive experiments on three widely used UDA datasets show that our method surpasses many previous state-of-the-arts.

关键词： computer vision Semantics Minimization Adversarial machine learning pattern recognition Reliability

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DaFF: Dual Attentive Feature Fusion for Multispectral Pedestrian Detection

DaFF: Dual Attentive Feature Fusion for Multispectral Pedest...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Afnan Althoupety Li-Yun Wang Wu-Chi Feng Banafsheh Rekabdar Portland State University USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inspired by how humans perceive and interpret the world using multiple senses, multi-modal learning involves integrating information from multiple modalities to improve understanding and performance in various tasks. Aligning with that notion, our key intuition is to utilize multi-model learning to solve the domain shift problem in night-time pedestrian *** this paper, we show that pairing RGB and infrared (IR) image features increases the robustness of pedestrian detection at night. Indeed, this solution is unbiased towards a specific time of the day as the IR domain reduces the reliance on lighting and serves as complementary information to the RGB domain. Our work aims at exploiting the power of attention mechanisms to guide a multi-modal framework in feature fusing from RGB and IR modalities. Our novel fusion approach, named dual attentive feature fusion (DaFF), leverages the duality of the transformer and channel-wise global attentions. To demonstrate the effectiveness of DaFF, we conducted experiments on two real-world multispectral pedestrian datasets. Extensive experimental results reveal the superiority of DaFF. We believe that combining the complementary properties of RGB and IR modalities is an effective remedy to mitigate the domain shift problem in pedestrian detection.

关键词： Degradation computer vision Pedestrians Attention mechanisms conferences Lighting Feature extraction

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Delving Deep into Many-to-many Attention for Few-shot Video Object Segmentation

Delving Deep into Many-to-many Attention for Few-shot Video ...

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

作者： Chen, Haoxin Wu, Hanjie Zhao, Nanxuan Ren, Sucheng He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

This paper tackles the task of Few-Shot Video Object Segmentation (FSVOS), i.e., segmenting objects in the query videos with certain class specified in a few labeled support images. The key is to model the relationship between the query videos and the support images for propagating the object information. This is a many-to-many problem and often relies on full-rank attention, which is computationally intensive. In this paper, we propose a novel Domain Agent Network (DAN), breaking down the full-rank attention into two smaller ones. We consider one single frame of the query video as the domain agent, bridging between the support images and the query video. Our DAN allows a linear space and time complexity as opposed to the original quadratic form with no loss of performance. In addition, we introduce a learning strategy by combining meta-learning with online learning to further improve the segmentation accuracy. We build a FSVOS benchmark on the Youtube-VIS dataset and conduct experiments to demonstrate that our method outperforms baselines on both computational cost and accuracy, achieving the state-of-the-art performance. Code is available at https://***/scutpaul/DANet.

关键词： Image segmentation computer vision Codes Computational modeling Object segmentation pattern recognition Computational efficiency

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