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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object D...

引用

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

作者： Liu, Mingxuan Hayes, Tyler L. Ricci, Elisa Csurka, Gabriela Volpi, Riccardo Univ Trento Trento Italy NAVER LABS Europe Meylan France Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class;ii) it integrates these categories into hierarchy-aware sentences;iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by large language models. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source.

关键词： Object Detection Open-vocabulary vision-Language

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Lookahead Exploration with Neural Radiance Representation for Continuous vision-Language Navigation

Lookahead Exploration with Neural Radiance Representation fo...

引用

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

作者： Wang, Zihan Li, Xiangyang Yang, Jiahao Liu, Yeqi Hu, Junjie Jiang, Ming Jiang, Shuqiang Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI USA Indiana Univ Dept Human Ctr Comp Indianapolis IN 46204 USA

ISBN: (纸本)9798350353006

vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step, the agent selects from possible candidate locations and then makes the move. For better navigation planning, the lookahead exploration strategy aims to effectively evaluate the agent's next action by accurately anticipating the future environment of candidate locations. To this end, some existing works predict RGB images for future environments, while this strategy suffers from image distortion and high computational cost. To address these issues, we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments, which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore, with the predicted future environmental representations, our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method. The code is available at https://***/MrZihan/HNR-VLN

关键词： Lookahead Exploration Neural Radiance Fields vision-and-Language Navigation

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DAtRNet: Disentangling Fashion Attribute Embedding for Substitute Item Retrieval

DAtRNet: Disentangling Fashion Attribute Embedding for Subst...

引用

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

作者： Bhattacharya, Gaurab Kilari, Nikhil Gubbi, Jayavardhana Lakshmi, Bagya, V Pal, Arpan Balamuralidhar, P. TCS Res Mumbai Maharashtra India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive substitute recommendation for fashion products improves the online retail customer experience. Traditional fashion search platforms incorporate product metadata between the query products and the products to be retrieved. In this paper, we propose DAtRNet, an attribute representation network to disentangle the features in the query product. It is used to recommend attribute-aware substitute items based on the conditional similarity of the retrieved products. The proposed architecture relies on attribute-level similarity providing a fine-grained recommendation. In addition, a concurrent axial attention mechanism is proposed that generates global information embedding and adaptively re-calibrates the soft attention masks. Overall, the end-to-end framework enables the system to disentangle the attribute features and independently deals with them to enhance its flexibility towards one or multiple attributes. The proposed method outperforms the state-ofthe-art by a significant margin.

关键词： computer vision conferences computer architecture Metadata Search problems Feature extraction pattern recognition

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Pre-training vision Models with Mandelbulb Variations

Pre-training Vision Models with Mandelbulb Variations

引用

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

作者： Chiche, Benjamin Naoto Horikawa, Yuto Fujita, Ryo Rist Inc 830 Hongakujimae-ChoGojo-DoriShimogyo-Ku Kyoto 6008102 Japan

ISBN: (纸本)9798350353006

The use of models that have been pre-trained on natural image datasets like ImageNet may face some limitations. First, this use may be restricted due to copyright and license on the training images, and privacy laws. Second, these datasets and models may incorporate societal and ethical biases. Formula-driven supervised learning (FDSL) enables model pre-training to circumvent these issues. This consists of generating a synthetic image dataset based on mathematical formulae and pre-training the model on it. In this work, we propose novel FDSL datasets based on Mandelbulb Variations. These datasets contain RGB images that are projections of colored objects deriving from the 3D Mandelbulb fractal. Pre-training ResNet-50 on one of our proposed datasets MandelbulbVAR-1k enables an average top-1 accuracy over target classification datasets that is at least 1% higher than pre-training on existing FDSL datasets. With regard to anomaly detection on MVTec AD, pre-training the WideResNet-50 backbone on MandelbulbVAR-1k enables PatchCore to achieve 97.2% average image-level AUROC. This is only 1.9% lower than pre-training on ImageNet-1k (99.1%) and 4.5% higher than pre-training on the second-best performing FDSL dataset i.e. VisualAtom-1k (92.7%). Regarding vision Transformer (ViT) pre-training, another dataset that we propose and coin MandelbulbVAR-Hybrid-21k enables ViT-Base to achieve 82.2% top-1 accuracy on ImageNet-1k, which is 0.4% higher than pre-training on ImageNet-21k (81.8%) and only 0.1% lower than pre-training on VisualAtom-1k (82.3%).

关键词： anomaly detection classification convolutional neural network Formula-driven supervised learning fractal mandelbulb pre-training vision transformer

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Fast-n-Squeeze: towards real-time spectral reconstruction from RGB images

Fast-n-Squeeze: towards real-time spectral reconstruction fr...

引用

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

作者： Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336Bldg U14 Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present an efficient method for the reconstruction of multispectral information from RGB images, as part of the NTIRE 2022 Spectral Reconstruction Challenge. Given an input image, our method determines a global RGB-to-spectral linear transformation matrix, based on a search through optimal matrices from training images that share low-level features with the input. The resulting spectral signatures are then adjusted by a global scaling factor, determined through a lightweight SqueezeNet-inspired neural network. By combining the efficiency of linear transformation matrices with the data-driven effectiveness of convolutional neural networks, we are able to achieve superior performance than winners of the previous editions of the challenge.

关键词： Training Measurement Image resolution Neural networks Real-time systems pattern recognition Convolutional neural networks

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3D Room Layout Recovery Generalizing across Manhattan and Non-Manhattan Worlds

3D Room Layout Recovery Generalizing across Manhattan and No...

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

作者： Jia, Haijing Yi, Hong Fujiki, Hirochika Zhang, Hengzhi Wang, Wei Odamaki, Makoto Ricoh Software Res Ctr Beijing Co Ltd Beijing Peoples R China Ricoh Co Ltd Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent 3D room layout recovery approaches mostly concentrate on Manhattan layouts, where the vertical walls are orthogonal with respect to each other, even though there are many rooms with non-Manhattan layouts in the real world. This paper presents a room layout recovery method generalizing across Manhattan and non-Manhattan worlds. Without introducing additional supervision, we extend current Manhattan layout recovery methods by predicting an extra surface normal feature, which is further used for an adaptive post-processing to reconstruct layouts of arbitrary shapes. Experimental results show that our method has a great improvement on non-Manhattan layouts while being capable of generalizing across Manhattan and non-Manhattan layouts.

关键词： Surface reconstruction computer vision Three-dimensional displays Shape conferences Layout Transformers

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Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting Completing M.C Escher's Print Gallery

Comparison of CoModGANs, LaMa and GLIDE for Art Inpainting C...

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

作者： Cipolina-Kun, Lucia Caenazzo, Simone Mazzei, Gaston ML Collect San Francisco CA 94117 USA Riskcare Ltd London England Univ Paris Saclay CNRS LISN VENISE Team Orsay France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Digital art restoration has benefited from inpainting models to correct the degradation or missing sections of a painting. This work compares three current state-of-the art models for inpainting of large missing regions. We provide qualitative and quantitative comparison of the performance by CoModGANs, LaMa and GLIDE in inpainting of blurry and missing sections of images. We use Escher's incomplete painting Print Gallery as our test study since it presents several of the challenges commonly present in restorative inpainting.

关键词： Degradation Image resolution Computational modeling conferences Digital art Image restoration pattern recognition

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Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

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

作者： Bangunharcana, Antyanta Kim, Soohyun Kim, Kyung-Soo Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This work focuses on improving the Conv-GRU-based optical flow update within a DROID-SLAM framework. Prior optical flow models typically follow a UNet or coarse-to-fine architecture in order to extract long-range cross-correlation and context cues. This helps flow estimation in the presence of large motion and challenging image regions, e.g., textureless regions. We propose modifications to the Conv-GRU module which follows the rationale of these prior models by integrating (Atrous) Spatial Pyramid Pooling and global self-attention into the Conv-GRU block. By enlarging the receptive field through the aforementioned modifications, the model is able to integrate information from a larger context window, thus improving the robustness even when given inputs that comprise challenging image regions. We show empirically through extensive experiments the gain in accuracy through these modifications.

关键词： computer vision Simultaneous localization and mapping conferences Computational modeling Estimation computer architecture Robustness

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SparseOcc: Rethinking Sparse Latent Representation for vision-Based Semantic Occupancy Prediction

SparseOcc: Rethinking Sparse Latent Representation for Visio...

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

作者： Tang, Pin Wang, Zhongdao Wang, Gum-ling Zheng, Jilai Ren, Xiangxuan Feng, Bailan Ma, Chao Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350353006

vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and space complexity, which limits scalability in terms of perception range or spatial resolution. Existing approaches compress the dense representation using projections like Bird's Eye View (BEV) or Tri-Perspective View (TPV). Although efficient, these projections result in information loss, especially for tasks like semantic occupancy prediction. To address this, we propose SparseOcc, an efficient occupancy network inspired by sparse point cloud processing. It utilizes a lossless sparse latent representation with three key innovations. Firstly, a 3D sparse diffuser performs latent completion using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature pyramid and sparse interpolation enhance scales with information from others. Finally, the transformer head is redesigned as a sparse variant. SparseOcc achieves a remarkable 74.9% reduction on FLOPs over the dense baseline. Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in part can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.

关键词： Autonomous Driving Occupancy Prediction Sparse Representation vision-Based Perception

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Dual Memory Networks: A Versatile Adaptation Approach for vision-Language Models

Dual Memory Networks: A Versatile Adaptation Approach for Vi...

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

作者： Zhang, Yabin Zhu, Wenjie Tang, Hui Ma, Zhiyuan Zhou, Kaiyang Zh, Lei HKPolyU Hong Kong Peoples R China OPPO Hong Kong Peoples R China HKUST Hong Kong Peoples R China HKBU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent research. The adaptation strategies can be typically categorized into three paradigms: zero-shot adaptation, few-shot adaptation, and the recently-proposed training-free few-shot adaptation. Most existing approaches are tailored for a specific setting and can only cater to one or two of these paradigms. In this paper, we introduce a versatile adaptation approach that can effectively work under all three settings. Specifically, we propose the dual memory networks that comprise dynamic and static memory components. The static memory caches training data knowledge, enabling training-free few-shot adaptation, while the dynamic memory preserves historical test features online during the testing process, allowing for the exploration of additional data insights beyond the training set. This novel capability enhances model performance in the few-shot setting and enables model usability in the absence of training data. The two memory networks employ the same flexible memory interactive strategy, which can operate in a training-free mode and can be further enhanced by incorporating learnable projection layers. Our approach is tested across 11 datasets under the three task settings. Remarkably, in the zero-shot scenario, it outperforms existing methods by over 3% and even shows superior results against methods utilizing external training data. Additionally, our method exhibits robust performance against natural distribution shifts. Codes are available at https://***/YBZh/DMN.

关键词： dual memory networks versatile adaptation vision-language models

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