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$R^{2}$ Former: Unified Retrieval and Reranking Transformer for Place recognition

$R^{2}$ Former: Unified Retrieval and Reranking Transformer ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sijie Zhu Linjie Yang Chen Chen Mubarak Shah Xiaohui Shen Heng Wang ByteDance Center for Research in Computer Vision University of Central Florida

Visual Place recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN features for global retrieval and RANSAC-based geometric verification for reranking. However, RANSAC only employs geometric information but ignores other possible information that could be useful for reranking, e.g. local feature correlations, and attention values. In this paper, we propose a unified place recognition framework that handles both retrieval and reranking with a novel transformer model, named $R^vision$ Former. The proposed reranking module takes feature correlation, attention value, and $xy$ coordinates into account, and learns to determine whether the image pair is from the same location. The whole pipeline is end-to-end trainable and the reranking module alone can also be adopted on other CNN or transformer backbones as a generic component. Remarkably, $R^vision$ Former significantly outperforms state-of-the-art methods on major VPR datasets with much less inference time and memory consumption. It also achieves the state-of-the-art on the hold-out MSLS challenge set and could serve as a simple yet strong solution for real-world large-scale applications. Experiments also show vision transformer tokens are comparable and sometimes better than CNN local features on local matching. The code is released at https://***/Jeff-Zilence/R2Former.

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A Simple Framework for Text-Supervised Semantic Segmentation

A Simple Framework for Text-Supervised Semantic Segmentation

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conference on computer vision and pattern recognition (CVPR)

作者： Muyang Yi Quan Cui Hao Wu Cheng Yang Osamu Yoshie Hongtao Lu Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Waseda University ByteDance Inc.

Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pretraining (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly amelio-rate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at ***/muyangyi/SimSeg.

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Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution

Equivalent Transformation and Dual Stream Network Constructi...

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conference on computer vision and pattern recognition (CVPR)

作者： Jiahao Chao Zhou Zhou Hongfan Gao Jiali Gong Zhengfeng Yang Zhenbing Zeng Lydia Dehbi East China Normal University Chengdu Institute of Computer Applications of Chinese Academy of Sciences

In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices. To address this issue, many lightweight super-resolution models have been proposed. However, these models still contain time-consuming components that increase inference latency, limiting their real-world applications on mobile devices. In this paper, we propose a novel model for single-image super-resolution based on Equivalent Transformation and Dual Stream network construction (ETDS). ET method is proposed to transform time-consuming operators into time-friendly operations, such as convolution and ReLU, on mobile devices. Then, a dual stream network is designed to alleviate redundant parameters resulting from the use of ET and enhance the feature extraction ability. Taking full advantage of the advance of ET and the dual stream network structure, we develop the efficient SR model ETDS for mobile devices. The experimental results demonstrate that our ETDS achieves superior inference speed and reconstruction quality compared to previous lightweight SR methods on mobile devices. The code is available at https://***/ECNUSR/ETDS.

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Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM

Co-SLAM: Joint Coordinate and Sparse Parametric Encodings fo...

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conference on computer vision and pattern recognition (CVPR)

作者： Hengyi Wang Jingwen Wang Lourdes Agapito Department of Computer Science University College London

We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that performs robust camera tracking and high-fidelity surface reconstruction in real time. Co-SLAM represents the scene as a multi-resolution hash-grid to exploit its high convergence speed and ability to represent high-frequency local features. In addition, Co-SLAM incorporates one-blob encoding, to encourage surface coherence and completion in unobserved areas. This joint parametric-coordinate encoding enables real-time and robust performance by bringing the best of both worlds: fast convergence and surface hole filling. Moreover, our ray sampling strategy allows Co-SLAM to perform global bundle adjustment over all keyframes instead of requiring keyframe selection to maintain a small number of active keyframes as competing neural SLAM approaches do. Experimental results show that Co-SLAM runs at 10-17Hz and achieves state-of-the-art scene reconstruction results, and competitive tracking performance in various datasets and benchmarks (ScanNet, TUM, Replica, Synthetic RGBD). Project page: https://***/projects/CoSLAM

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Smallcap: Lightweight Image Captioning Prompted with Retrieval Augmentation

Smallcap: Lightweight Image Captioning Prompted with Retriev...

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conference on computer vision and pattern recognition (CVPR)

作者： Rita Ramos Bruno Martins Desmond Elliott Yova Kementchedjhieva INESC-ID Instituto Superior Técnico University of Lisbon Department of Computer Science University of Copenhagen Pioneer Center for AI

Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pretraining and finetuning. As an alternative to large models, we present Smallcap, which generates a caption conditioned on an input image and related captions retrieved from a datastore. Our model is lightweight and fast to train, as the only learned parameters are in newly introduced cross-attention layers between a pre-trained CLIP encoder and GPT-2 decoder. Smallcap can transfer to new domains without additional finetuning and can exploit large-scale data in a training-free fashion since the contents of the datastore can be readily replaced. Our experiments show that Smallcap, trained only on COCO, has competitive performance on this benchmark, and also transfers to other domains without retraining, solely through retrieval from target-domain data. Further improvement is achieved through the training-free exploitation of diverse human-labeled and web data, which proves to be effective for a range of domains, including the nocaps benchmark, designed to test generalization to unseen visual concepts. 1 1 Code: https://***/RitaRamo/smallcap.

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FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs

FJMP: Factorized Joint Multi-Agent Motion Prediction over Le...

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conference on computer vision and pattern recognition (CVPR)

作者： Luke Rowe Martin Ethier Eli-Henry Dykhne Krzysztof Czarnecki School of Computer Science University of Waterloo

Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion Prediction framework for multi-agent interactive driving scenarios. FJMP models the future scene interaction dynamics as a sparse directed interaction graph, where edges denote explicit interactions between agents. We then prune the graph into a directed acyclic graph (DAG) and decompose the joint prediction task into a sequence of marginal and conditional predictions according to the partial ordering of the DAG, where joint future trajectories are decoded using a directed acyclic graph neural network (DAGNN). We conduct experiments on the INTERACTION and Argoverse 2 datasets and demonstrate that FJMP produces more accurate and scene-consistent joint trajectory predictions than non-factorized approaches, especially on the most interactive and kinematically interesting agents. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTION dataset.

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Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution

Revisiting Rolling Shutter Bundle Adjustment: Toward Accurat...

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conference on computer vision and pattern recognition (CVPR)

作者： Bangyan Liao Delin Qu Yifei Xue Huiqing Zhang Yizhen Lao College of Computer Science and Electronic Engineering Hunan University College of Electrical and Information Engineering Hunan University Shanghai AI Laboratory Jiangxi Provincial Natural Resources Cause Development Center

We propose an accurate and fast bundle adjustment (BA) solution that estimates the 6-DoF pose with an independent RS model of the camera and the geometry of the environment based on measurements from a rolling shutter (RS) camera. This tackles the challenges in the existing works, namely, relying on high frame rate video as input, restrictive assumptions on camera motion and poor efficiency. To this end, we first verify the positive influence of the image point normalization to RSBA. Then we present a novel visual residual covariance model to standardize the reprojection error during RSBA, which consequently improves the overall accuracy. Besides, we demonstrate the combination of Normalization and covariance standardization Weighting in RSBA (NW-RSBA) can avoid common planar degeneracy without the need to constrain the filming manner. Finally, we propose an acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix and Schur complement. The extensive synthetic and real data experiments verify the effectiveness and efficiency of the proposed solution over the state-of-the-art works.

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Thermal Spread Functions (TSF): Physics-Guided Material Classification

Thermal Spread Functions (TSF): Physics-Guided Material Clas...

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conference on computer vision and pattern recognition (CVPR)

作者： Aniket Dashpute Vishwanath Saragadam Emma Alexander Florian Willomitzer Aggelos Katsaggelos Ashok Veeraraghavan Oliver Cossairt Electrical and Computer Engineering Northwestern University Electrical and Computer Engineering Rice University Computer Science Northwestern University Wyant College of Optical Sciences University of Arizona

Robust and non-destructive material classification is a challenging but crucial first-step in numerous vision applications. We propose a physics-guided material classification framework that relies on thermal properties of the object. Our key observation is that the rate of heating and cooling of an object depends on the unique intrinsic properties of the material, namely the emissivity and diffusivity. We leverage this observation by gently heating the objects in the scene with a low-power laser for a fixed duration and then turning it off, while a thermal camera captures measurements during the heating and cooling process. We then take this spatial and temporal “thermal spread function” (TSF) to solve an inverse heat equation using the finite-differences approach, resulting in a spatially varying estimate of diffusivity and emissivity. These tuples are then used to train a classifier that produces a fine-grained material label at each spatial pixel. Our approach is extremely simple requiring only a small light source (low power laser) and a thermal camera, and produces robust classification results with 86% accuracy over 16 classes 1 1 Code: https://***/aniketdashpute/TSF.

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Curricular Object Manipulation in LiDAR-based Object Detection

Curricular Object Manipulation in LiDAR-based Object Detecti...

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conference on computer vision and pattern recognition (CVPR)

作者： Ziyue Zhu Qiang Meng Xiao Wang Ke Wang Liujiang Yan Jian Yang Tianjin Key Laboratory of Visual Computing and Intelligent Perception College of Computer Science Nankai University Didi Chuxing

This paper explores the potential of curriculum learning in LiDAR-based 3D object detection by proposing a curricular object manipulation (COM) framework. The framework embeds the curricular training strategy into both the loss design and the augmentation process. For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages. On top of the widely-used augmentation technique called GT-Aug in Li-DAR detection tasks, we propose a novel COMAug strategy which first clusters objects in ground-truth database based on well-designed heuristics. Group-level difficulties rather than individual ones are then predicted and updated during training for stable results. Model performance and generalization capabilities can be improved by sampling and augmenting progressively more difficult objects into the training samples. Extensive experiments and ablation studies reveal the superior and generality of the proposed framework. The code is available at https://***/ZZY816/COM.

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StyleSync: High-Fidelity Generalized and Personalized Lip Sync in Style-Based Generator

StyleSync: High-Fidelity Generalized and Personalized Lip Sy...

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conference on computer vision and pattern recognition (CVPR)

作者： Jiazhi Guan Zhanwang Zhang Hang Zhou Tianshu Hu Kaisiyuan Wang Dongliang He Haocheng Feng Jingtuo Liu Errui Ding Ziwei Liu Jingdong Wang Department of Computer Vision Technology (VIS) Baidu Inc. Tsinghua University The University of Sydney S-Lab Nanyang Technological University

Despite recent advances in syncing lip movements with any audio waves, current methods still struggle to balance generation quality and the model's generalization ability. Previous studies either require long-term data for training or produce a similar movement pattern on all subjects with low quality. In this paper, we propose StyleSync, an effective framework that enables high-fidelity lip synchronization. We identify that a style-based generator would sufficiently enable such a charming property on both one-shot and few-shot scenarios. Specifically, we design a mask-guided spatial information encoding module that preserves the details of the given face. The mouth shapes are accurately modified by audio through modulated convolutions. Moreover, our design also enables personalized lip-sync by introducing style space and generator refinement on only limited frames. Thus the identity and talking style of a target person could be accurately preserved. Extensive experiments demonstrate the effectiveness of our method in producing high-fidelity results on a variety of scenes. Resources can be found at https:/***/projects/StyleSync.

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