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Flowering Time Prediction of Wheat From DIA-MS Data

Flowering Time Prediction of Wheat From DIA-MS Data

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Yan Yang Utpal Bose James Broadbent Sally Stockwell Keren Byrne Md Zakir Hossain Eric Stone Shannon Dillon A&F CSIRO Australia BDSI Australian National University Australia Curtin University Australia

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Traditional methods utilising data-independent acquisition mass spectrometry (DIA-MS) data for predictions depend on database searches against predefined spectral libraries for characterisation and quantification of the proteomes, limiting scalability and adaptability across various applications. However, directly applying existing networks on DIA-MS data represented as images for end-to-end predictions struggles to mine a predictive pattern, due to non-uniform region importance across the image and divergences exhibited among different regions of the image. To overcome these limitations, we propose a new frame-work with two modules: i) a dynamic sampling module that identifies regions of interest from the DIA-MS image, constraining the network to focus on the most informative regions of the image only; ii) a mixture of experts module that sparsely routes the regions of interest to related expert networks, facilitating adaptive computation of region features. The region features are then fused for predictions. Experimentally, to benchmark our method, we collected a large DIA-MS dataset of wheat for flowering time prediction, and our approach significantly outperforms previous end-to-end methods, i. e., 0.171 R2 improvements.

关键词： computer vision Limiting Adaptive systems Databases Scalability Flowering plants Benchmark testing Mass spectroscopy Libraries

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Learning Visual Grounding from Generative vision and Language Model

Learning Visual Grounding from Generative Vision and Languag...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Shijie Wang Dahun Kim Ali Taalimi Chen Sun Weicheng Kuo Brown University Google DeepMind Google Cloud

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Visual grounding tasks aim to localize image regions based on natural language references. In this work, we ex-plore whether generative VLMs predominantly trained on image-text data could be leveraged to scale up the text an-notation of visual grounding data. We find that grounding knowledge already exists in generative VLM and can be elicited by proper prompting. We thus prompt a VLM to generate object-level descriptions by feeding it object regions from existing object detection datasets. We fur-ther propose attribute modeling to explicitly capture the im-portant object attributes, and spatial relation modeling to capture inter-object relationship, both of which are common linguistic pattern in referring expression. Our constructed dataset (500K images, 1M objects, 16M referring expressions) is one of the largest grounding datasets to date, and the first grounding dataset with purely model-generated queries and human-annotated objects. To verify the qual-ity of this data, we conduct zero-shot transfer experiments to the popular RefCoco benchmarks for both referring expression comprehension (REC) and segmentation (RES) tasks. On both tasks, our model significantly outperform the state-of-the-art approaches without using human anno-tated visual grounding data. Our results demonstrate the promise of generative VLM to scale up visual grounding in the real world.

关键词： Training Visualization Image segmentation computer vision Grounding Crops Object detection Linguistics Benchmark testing Data models

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VLTP: vision-Language Guided Token Pruning for Task-Oriented Segmentation

VLTP: Vision-Language Guided Token Pruning for Task-Oriented...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Hanning Chen Yang Ni Wenjun Huang Yezi Liu SungHeon Jeong Fei Wen Nathaniel D. Bastian Hugo Latapie Mohsen Imani University of California Irvine CA USA Texas A&M University College Station TX USA United States Military Academy West Point NY USA Cisco San Jose CA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

vision Transformers (ViTs) have emerged as the backbone of many segmentation models, consistently achieving state-of-the-art (SOTA) performance. However, their success comes at a significant computational cost. Image token pruning is one of the most effective strategies to address this complexity. However, previous approaches fall short when applied to more complex task-oriented segmentation (TOS), where the class of each image patch is not predefined but dependent on the specific input task. This work introduces the vision Language Guided Token Pruning (VLTP), a novel token pruning mechanism that can accelerate ViT-based segmentation models, particularly for TOS guided by multi-modal large language model (MLLM). We argue that ViT does not need to process every image token through all of its layers—only the tokens related to reasoning tasks are necessary. We design a new pruning decoder to take both image tokens and vision-language guidance as input to predict the relevance of each image token to the task. Only image tokens with high relevance are passed to deeper layers of the ViT. Experiments show that the VLTP framework reduces the computational costs of ViT by approximately 25% without performance degradation and by around 40% with only a 1% performance drop. The code associated with this study can be found at this URL.

关键词： Uniform resource locators Image segmentation computer vision Image recognition Computational modeling Large language models Transformers Computational efficiency Decoding Load modeling

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TRNeRF: Restoring Blurry, Rolling Shutter, and Noisy Thermal Images with Neural Radiance Fields

TRNeRF: Restoring Blurry, Rolling Shutter, and Noisy Thermal...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Spencer Carmichael Manohar Bhat Mani Ramanagopal Austin Buchan Ram Vasudevan Katherine A. Skinner University of Michigan Ann Arbor MI USA Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Thermal cameras offer unique detection capabilities in building inspections, search and rescue operations, and autonomous vehicle perception. Of the different types of thermal cameras, uncooled microbolometers are often chosen due to their relative affordability, small size, and low power consumption. However, microbolometers suffer from motion blur, rolling shutter distortions, and fixed pattern noise, which limit the conditions of their use. Nearly all prior methods for microbolometer image restoration account for only one of these degradations, and current techniques addressing microbolometer blur and rolling shutter are limited. This paper presents TRNeRF, a thermal image restoration method that jointly addresses all three degradations by incorporating the microbolometer image formation model with Neural Radiance Fields (NeRFs). To evaluate TRNeRF, this paper introduces a new real-world dataset that is uniquely designed to support two novel quantitative evaluation strategies for thermal image restoration. Experiments demonstrate that TRNeRF is able to recover sharp, global shutter, and clear thermal images, even under extremely aggressive camera motion that causes existing methods to fail. The code and dataset are available at: https://***/trnerf.

关键词： Degradation Power demand Noise Neural radiance field Cameras Distortion Rendering (computer graphics) Thermal noise Image restoration Thermal degradation

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SpectFormer: Frequency and Attention is what you need in a vision Transformer

SpectFormer: Frequency and Attention is what you need in a V...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Badri N. Patro Vinay P. Namboodiri Vijay S. Agneeswaran Microsoft University of Bath

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

vision transformers have been applied successfully for image recognition tasks. There have been either multi-headed self-attention based (ViT [12], DelT [54]) simi-lar to the original work in textual models or more re-cently based on spectral layers (Fnet [29], GFNet [46], AFNO [15]). We hypothesize that spectral layers cap-ture high-frequency information such as lines and edges, while attention layers capture token interactions. We inves-tigate this hypothesis through this work and observe that indeed mixing spectral and multi-headed attention layers provides a better transformer architecture. We thus pro-pose the novel Spectformer architecture for vision trans-formers that has initial spectral and deeper multi-headed attention layers. We believe that the resulting representation allows the transformer to capture the feature repre-sentation appropriately and it yields improved performance over other transformer representations. For instance, it im-proves the top-1 accuracy by 2% on ImageNet compared to both GFNet-H and LiT. SpectFormer-H-S reaches 84.25% top-1 accuracy on ImageNet-1 K (state of the art for small version). Further, Spectformer-H-L achieves 85.7% which is the state of the art for the comparable base version of the transformers. We further validated the SpectFormer per-formance in other scenarios such as transfer learning on standard datasets such as ClFAR-10, ClFAR-100, Oxford-lIlT-flower, and Standford Car datasets. We then investigate its use in downstream tasks such as object detection and instance segmentation on the MS-COCO dataset and ob-serve that Spectformer shows consistent performance that is comparable to the best backbones and can be further optimized and improved. The source code is available on this website https://***/badripatro/SpectFormers.

关键词： Instance segmentation computer vision Accuracy Source coding Image edge detection Transfer learning Object detection Transformer cores Transformers Standards

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Loose Social-Interaction recognition in Real-World Therapy Scenarios

Loose Social-Interaction Recognition in Real-World Therapy S...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Abid Ali Rui Dai Ashish Marisetty Guillaume Astruc Monique Thonnat Jean-Marc Odobez Susanne Thümmler Francois Bremond INRIA University Cote d’Azur Idiap

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The computer vision community has explored dyadic interactions for atomic actions such as pushing, carrying-object, etc. However, with the advancement in deep learning models, there is a need to explore more complex dyadic situations such as loose interactions. These are interactions where two people perform certain atomic activities to complete a global action irrespective of temporal synchronisation and physical engagement, like cooking-together for example. Analysing these types of dyadic-interactions has several useful applications in the medical domain for social-skills development and mental health diagnosis. To achieve this, we propose a novel dual-path architecture to capture the loose interaction between two individuals. Our model learns global abstract features from each stream via a CNNs backbone and fuses them using a new Global-Layer-Attention module based on a cross-attention strategy. We evaluate our model on real-world autism diagnoses such as our Loose-Interaction dataset, and the publicly available Autism dataset for loose interactions. Our network achieves baseline results on the Loose-Interaction and SOTA results on the Autism datasets. Moreover, we study different social interactions by experimenting on a publicly available dataset i.e. NTU-RGB+D (interactive classes from both NTU-60 and NTU-120). We have found that different interactions require different network designs. We also compare a slightly different version of our method (details in Section 3.6) by incorporating time information to address tight interactions achieving SOTA results.

关键词： Deep learning Autism computer vision Fuses Computational modeling Medical treatment Mental health computer architecture Synchronization Medical diagnostic imaging

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Spatio-Temporal Context Prompting for Zero-Shot Action Detection

Spatio-Temporal Context Prompting for Zero-Shot Action Detec...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Wei-Jhe Huang Min-Hung Chen Shang-Hong Lai National Tsing Hua University Taiwan NVIDIA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Spatio-temporal action detection encompasses the tasks of localizing and classifying individual actions within a video. Recent works aim to enhance this process by incorporating interaction modeling, which captures the relationship between people and their surrounding context. However, these approaches have primarily focused on fully-supervised learning, and the current limitation lies in the lack of generalization capability to recognize unseen action categories. In this paper, we aim to adapt the pretrained image-language models to detect unseen actions. To this end, we propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction. Meanwhile, our Context Prompting module will utilize contextual information to prompt labels, thereby enhancing the generation of more representative text features. Moreover, to address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism which employs pretrained visual knowledge to find each person's interest context tokens, and then these tokens will be used for prompting to generate text features tailored to each individual. To evaluate the ability to detect unseen actions, we propose a comprehensive benchmark on J-HMDB, UCF101-24, and AVA datasets. The experiments show that our method achieves superior results compared to previous approaches and can be further extended to multi-action videos, bringing it closer to real-world applications. The code and data can be found in ST-CLIP.

关键词： Adaptation models Visualization computer vision Codes Text recognition Benchmark testing Context modeling

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PROCEEDINGS - CVPR '85, ieee computer SOCIETY conference ON computer vision AND pattern recognition.

PROCEEDINGS - CVPR '85, IEEE COMPUTER SOCIETY CONFERENCE ON ...

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Proceedings - CVPR '85, ieee computer Society conference on computer vision and pattern recognition.

作者： Anon

ISBN: (纸本)0818606339

The following topics are dealt with: edge and boundary analysis;vision systems;motion;shape and 2-D description;stereo and 3-D description;pattern recognition;3-D models;architectures;vision models and texture;image segmentation;applications and parallel algorithms;3-D analysis;contour analysis;character recognition;3-D descriptions from multiple views;and parallel architectures for image processing. 123 papers were presented, of which 121 are published in full in the present proceedings.

关键词： pattern recognition

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Data-Efficient 3D Visual Grounding via Order-Aware Referring

Data-Efficient 3D Visual Grounding via Order-Aware Referring

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Tung-Yu Wu Sheng-Yu Huang Yu-Chiang Frank Wang Department of Electrical Engineering National Taiwan University NVIDIA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

3D visual grounding aims to identify the target object within a 3D point cloud scene referred to by a natural language description. Previous works usually require significant data relating to point color and their descriptions to exploit the corresponding complicated verbo-visual relations. In our work, we introduce Vigor, a novel Data-Efficient 3D Visual Grounding framework via Order-aware Referring. Vigor leverages LLM to produce a desirable referential order from the input description for 3D visual grounding. With the proposed stacked object-referring blocks, the predicted anchor objects in the above order allow one to locate the target object progressively with-out supervision on the identities of anchor objects or exact relations between anchor/target objects. We also present an order-aware warm-up training strategy, which augments referential orders for pre-training the visual grounding framework, allowing us to better capture the complex verbo-visual relations and benefit the desirable data-efficient learning scheme. Experimental results on the NR3D and ScanRefer datasets demonstrate our superiority in low-resource scenarios. In particular, Vigor surpasses current state-of-the-art frameworks by 9.3% and 7.6% grounding accuracy under 1% data and 10% data settings on the NR3D dataset, respectively.

关键词： Training Point cloud compression Visualization computer vision Three-dimensional displays Grounding Image color analysis Natural languages Benchmark testing Object recognition

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Learning Multiple Object States from Actions via Large Language Models

Learning Multiple Object States from Actions via Large Langu...

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ieee Workshop on Applications of computer vision (WACV)

作者： Masatoshi Tateno Takuma Yagi Ryosuke Furuta Yoichi Sato The University of Tokyo National Institute of Advanced Industrial Science and Technology (AIST)

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recognizing the states of objects in a video is crucial in understanding the scene beyond actions and objects. For instance, an egg can be raw, cracked, and whisked while cooking an omelet, and these states can coexist simulta-neously (an egg can be both raw and whisked). However, most existing research assumes single object state change (e.g. uncracked → cracked), overlooking the coexisting nature of multiple object states and the influence of past states on the current state. We formulate object state recognition as a multi-label classification task that explicitly handles multiple states. We then propose to learn multiple object states from narrated videos by leveraging LLMs to generate pseudo-labels from the transcribed narrations, capturing the influence of past states. The challenge is that narrations mostly describe human actions in the video but rarely explain object states. Therefore, we use LLM's knowledge of the relationship between actions and states to derive the missing object states. We further accumulate the derived object states to consider the past state contexts to infer current object state pseudo-labels. We newly collect Multiple Object States Transition (MOST) dataset, which includes manual multi-label annotation for evaluation purposes, covering 60 object states across six object categories. Experimental results show that our model trained on LLM-generated pseudo-labels significantly outperforms strong vision-language models, demonstrating the effectiveness of our pseudo-labeling framework that considers past context via LLMs.

关键词： computer vision Analytical models Accuracy Annotations Computational modeling Large language models Catalysts Multi label classification Manuals Context modeling

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