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The STVchrono Dataset: Towards Continuous Change recognition in Time

The STVchrono Dataset: Towards Continuous Change Recognition...

引用

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

作者： Sun, Yanjun Qiu, Yue Khan, Mariia Matsuzawa, Fumiya Iwata, Kenji Natl Inst Adv Ind Sci & Technol Tokyo Japan Keio Univ Tokyo Japan Edith Cowan Univ Joondalup Australia

ISBN: (纸本)9798350353006

Recognizing continuous changes offers valuable insights into past historical events, supports current trend analysis, and facilitates future planning. This knowledge is crucial for a variety of fields, such as meteorology and agriculture, environmental science, urban planning and construction, tourism, and cultural preservation. Currently available datasets in the field of scene change understanding primarily concentrate on two main tasks: the detection of changed regions within a scene and the linguistic description of the change content. Existing datasets focus on recognizing discrete changes, such as adding or deleting an object from two images, and largely rely on artificially generated images. Consequently, the existing change understanding methods primarily focus on identifying distinct object differences, overlooking the importance of continuous, gradual changes occurring over extended time intervals. To address the above issues, we propose a novel bench-mark dataset, STVchrono, targeting the localization and description of long-term continuous changes in real-world scenes. The dataset consists of 71,900 photographs from Google Street View API taken over an 18-year span across 50 cities all over the world. Our STVchrono dataset is designed to support real-world continuous change recognition and description in both image pairs and extended image sequences, while also enabling the segmentation of changed regions. We conduct experiments to evaluate state-of-the-art methods on continuous change description and segmentation, as well as multimodal Large Language Models for describing changes. Our findings reveal that even the most advanced methods lag human performance, emphasizing the need to adapt them to continuously changing real-world scenarios. We hope that our benchmark dataset will further facilitate the research of temporal change recognition in a dynamic world. The STVchrono dataset is available at STVchrono Dataset.

关键词： Image segmentation

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Point Cloud Instance Segmentation using Probabilistic Embeddings

Point Cloud Instance Segmentation using Probabilistic Embedd...

引用

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

作者： Zhang, Biao Wonka, Peter KAUST Thuwal Saudi Arabia

ISBN: (纸本)9781665445092

In this paper we propose a new framework for point cloud instance segmentation. Our framework has two steps: an embedding step and a clustering step. In the embedding step, our main contribution is to propose a probabilistic embedding space for point cloud embedding. Specifically, each point is represented as a tri-variate normal distribution. In the clustering step, we propose a novel loss function, which benefits both the semantic segmentation and the clustering. Our experimental results show important improvements to the SOTA, i.e., 3.1% increased average per-category mAP on the PartNet dataset.

关键词： Image segmentation computer vision Uncertainty Three-dimensional displays Semantics Gaussian distribution Probabilistic logic

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Deep Video Codec Control for vision Models

Deep Video Codec Control for Vision Models

引用

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

作者： Reich, Christoph Debnath, Biplob Patel, Deep Prangemeier, Tim Cremers, Daniel Chakradhar, Srimat NEC Labs Amer Inc San Jose CA 95110 USA Tech Univ Munich Munich Germany Tech Univ Darmstadt Darmstadt Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9798350365474

Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video codecs (e.g., H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.

关键词： Codec Coding for Machines Deep Video Codec Control Deep vision Models End-to-end Learning Rate Control Self-supervised Learning Standardization Video Coding

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ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

ViP-LLaVA: Making Large Multimodal Models Understand Arbitra...

引用

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

作者： Cai, Mu Liu, Haotian Mustikovela, Siva Karthik Meyer, Gregory P. Chai, Yuning Park, Dennis Lee, Yong Jae Univ Wisconsin Madison WI 53706 USA Cruise LLC San Francisco CA USA

ISBN: (纸本)9798350353006

While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge, we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow". Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings, yet achieves state-of-the-art performance on region-understanding tasks like Visual7W, PointQA, and Visual Commonsense Reasoning benchmark. Furthermore, we present ViP-Bench, a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions, enabling future research in this domain. Code, data, and model are publicly available.

关键词： Large Language Models Large Multimodal Models Multimodal Benchmark Region-level Understanding vision-language models Visual Commonsense Reasoning Visual Prompts

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Low-Rank Rescaled vision Transformer Fine-Tuning: A Residual Design Approach

Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual...

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

作者： Dong, Wei Zhang, Xing Chen, Bihui Yang, Dawei Lin, Zhijun Yang, Qingsen Wang, Peng Yang, Yang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Xian Univ Architecture & Technol Coll Informat & Control Engn Xian Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9798350353006

Parameter-efficient fine-tuning for pre-trained vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently, there is a lack of focus on guiding this delicate trade-off. In this study, we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices, providing insights into the tuning dynamics of existing methods. Building upon this understanding, we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks, all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://***/zstarN70/***.

关键词： Low-Rank Adaptation Parameter-efficient fine-tuning Singular Value Decomposition

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Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

Good at captioning, bad at counting: Benchmarking GPT-4V on ...

引用

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

作者： Zhang, Chenhui Wang, Sherrie MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350365474

Large vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess world knowledge that leads to strong performance on location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting. Our benchmark is publicly available on this website. A full version of this paper can be found here.

关键词： benchmark Earth observation foundation model vision-language model

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An End-to-End vision Transformer Approach for Image Copy Detection

An End-to-End Vision Transformer Approach for Image Copy Det...

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

作者： Lee, Jiahe Steven Hsu, Wynne Lee, Mong Li Natl Univ Singapore Inst Data Sci Singapore Singapore Natl Univ Singapore Ctr Trusted Internet & Community Singapore Singapore

ISBN: (纸本)9798350365474

Image copy detection is one of the pivotal tools to safeguard online information integrity. The challenge lies in determining whether a query image is an edited copy, which necessitates the identification of candidate source images through a retrieval process. The process requires discriminative features comprising of both global descriptors that are designed to be augmentation-invariant and local descriptors that can capture salient foreground objects to assess whether a query image is an edited copy of some source reference image. This work describes an end-to-end solution that leverage a vision Transformer model to learn such discriminative features and perform implicit matching between the query image and the reference image. Experimental results on two benchmark datasets demonstrate that the proposed solution outperforms state-of-the-art methods. Case studies illustrate the effectiveness of our approach in matching reference images from which the query images have been copy-edited.

关键词： image copy detection image retrieval

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Benchmarking Zero-Shot recognition with vision-Language Models: Challenges on Granularity and Specificity

Benchmarking Zero-Shot Recognition with Vision-Language Mode...

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

作者： Xu, Zhenlin Zhu, Yi Deng, Siqi Mittal, Abhay Chen, Yanbei Wang, Manchen Favaro, Paolo Tighe, Joseph Modolo, Davide AWS AI Labs Seattle WA 98109 USA Boson AI Santa Clara CA 95054 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350365474

This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks like image captioning, they face challenges in open-world settings. Our benchmarks test VLMs' consistency in understanding concepts across semantic granularity levels and their response to varying text specificity. Findings show that VLMs favor moderately fine-grained concepts and struggle with specificity, often misjudging texts that differ from their training data. Extensive evaluations reveal limitations in current VLMs, particularly in distinguishing between correct and subtly incorrect descriptions. While fine-tuning offers some improvements, it doesn't fully address these issues, highlighting the need for VLMs with enhanced generalization capabilities for real-world applications. This study provides insights into VLM limitations and suggests directions for developing more robust models.

关键词： Benchmarking Foundational Models vision and language models Zero-shot recognition

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Variational Autoencoders Pursue PCA Directions (by Accident) 32

Variational Autoencoders Pursue PCA Directions (by Accident)

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Rolinek, Michal Zietlow, Dominik Martius, Georg Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (纸本)9781728132938

The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.

关键词： computer vision Theory Deep Learning Representation Learning

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Modality-Collaborative Test-Time Adaptation for Action recognition

Modality-Collaborative Test-Time Adaptation for Action Recog...

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

作者： Xiong, Baochen Yang, Xiaoshan Song, Yaguang Wang, Yaowei Xu, Changsheng Chinese Acad Sci CASIA Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Video-based Unsupervised Domain Adaptation (VUDA) method improves the generalization of the video model, enabling it to be applied to action recognition tasks in different environments. However, these methods require continuous access to source data during the adaptation process, which are impractical in real scenarios where the source videos are not available with concerns in transmission efficiency or privacy issues. To address this problem, in this paper, we focus on the Multimodal Video Test-Time Adaptation (MVTTA) task. Existing image-based TTA methods cannot be directly applied to this task because videos have domain shifts in multimodal and temporal, which brings difficulties to adaptation. To address the above challenges, we pro-pose a Modality-Collaborative Test-Time Adaptation (MC-TTA) Network. MC-TTA contains maintain teacher and student memory banks respectively for generating pseudo-prototypes and target-prototypes. In the teacher model, we propose Self-assembled Source-friendly Feature Reconstruction (SSFR) to encourage the teacher memory bank to store features that are more likely to be consistent with the source distribution. Through multimodal prototype alignment and cross-modal relative consistency, our method can effectively alleviate domain shift in videos. We evaluate the proposed model on four public video datasets. The results show that our model outperforms existing state-of-the-art methods.

关键词： Multi-modal learning

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