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Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation

Cross-Domain Multi-task Learning for Object Detection and Sa...

引用

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

作者： Khattar, Apoorv Hegde, Srinidhi Hebbalaguppe, Ramya TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665448994

Multi-task learning (MTL) is a learning paradigm that aims at joint optimization of multiple tasks using a single neural network for better performance and generalization. In practice, MTL rests on the inherent assumption of availability of common datasets with ground truth labels for each of the downstream tasks. However, collecting such a common annotated dataset is laborious for complex computer vision tasks such as the saliency estimation which would require the eye fixation points as the ground truth data. To this end, we propose a novel MTL framework in the absence of common annotated dataset for joint estimation of important downstream tasks in computer vision - object detection and saliency estimation. Unlike many state-of-the-art methods, that rely on common annotated datasets for training, we consider the annotations from different datasets for jointly training different tasks, calling this setting as cross-domain MTL. We adapt MUTAN [3] framework to fuse features from different datasets to learn domain invariant features capturing the relatedness of different tasks. We demonstrate the improvement in the performance and generalizability of our MTL architecture. We also show that the proposed MTL network offers a 13% reduction in memory footprint due to parameter sharing between the related tasks.

关键词： Training computer vision Neural networks Estimation Object detection computer architecture pattern recognition

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Occlusion Guided Scene Flow Estimation on 3D Point Clouds

Occlusion Guided Scene Flow Estimation on 3D Point Clouds

引用

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

作者： Ouyang, Bojun Raviv, Dan Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665448994

3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow estimation. Our architecture is the first to gauge the occlusion in 3D scene flow estimation on point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the state-of-the-art results.(1,2)

关键词： Symbiosis Measurement Deep learning Three-dimensional displays Estimation computer architecture Tools

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Two-stage Network For Single Image Super-Resolution

Two-stage Network For Single Image Super-Resolution

引用

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

作者： Han, Yuzhuo Du, Xiaobiao Yang, Zhi Dalian Univ Technol Dalian Peoples R China Jilin Univ Zhuhai Coll Zhuhai Peoples R China Dibaocheng Shanghai Med Imaging Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781665448994

The task of single-image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurred images. Most previous super-resolution (SR) methods based on the convolutional neural networks (CNN) tend to design more complex network structures to directly learn the mapping between low-resolution images and high-resolution images. However, this is not the best choice to blindly increase the network depth, because the performance improvement may not increase, but it will increase the computational cost. To solve this problem, we propose an effective method that learns high frequency information in high-resolution images to enhance the image reconstruction. In this work, we propose a two-stage network (TSN) to recover clear SR images. The proposed TSN firstly learns the high frequency information in high-resolution images, then learns how to transform to high-resolution images. A large number of experiments show that our TSN achieves satisfactory performance.

关键词： computer vision Inverse problems conferences Computational modeling Superresolution Transforms pattern recognition

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Localized Latent Updates for Fine-Tuning vision-Language Models

Localized Latent Updates for Fine-Tuning Vision-Language Mod...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Moritz Ibing Isaak Lim Leif Kobbelt Visual Computing Institute RWTH Aachen University

Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific datasets. When doing so, it is desirable that updating the model is fast and that the model does not lose its capabilities on data outside of the dataset, as is often the case with classical fine-tuning approaches. In this work we suggest a lightweight adapter that only updates the models predictions close to seen datapoints. We demonstrate the effectiveness and speed of this relatively simple approach in the context of few-shot learning, where our results both on classes seen and unseen during training are comparable with or improve on the state of the art.

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T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos

T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder f...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Artur Xarles Sergio Escalera Thomas B. Moeslund Albert Clapés Universitat de Barcelona Spain Computer Vision Center Spain Aalborg University Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we introduce T-DEED, a Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in sports videos. T-DEED addresses multiple challenges in the task, including the need for discriminability among frame representations, high output temporal resolution to maintain prediction precision, and the necessity to capture information at different temporal scales to handle events with varying dynamics. It tackles these challenges through its specifically designed architecture, featuring an encoder-decoder for leveraging multiple temporal scales and achieving high output temporal resolution, along with temporal modules designed to increase token discriminability. Leveraging these characteristics, T-DEED achieves SOTA performance on the FigureSkating and FineDiving datasets. Code is available at https://***/arturxe2/T-DEED.

关键词： computer vision Codes conferences computer architecture pattern recognition Videos Sports

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A Simple Baseline for Fast and Accurate Depth Estimation on Mobile Devices

A Simple Baseline for Fast and Accurate Depth Estimation on ...

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

作者： Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Tencent GY Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose a simple but effective encoder-decoder based network for fast and accurate depth estimation on mobile devices. Unlike other depth estimation methods using heavy context modeling modules, the encoder with a fast downsampling strategy is employed to obtain sufficient receptive field and contexts at a faster rate. To obtain dense prediction, a light decoder is adopted to recover back to the original resolution. Additionally, to improve the representative ability of the light network, we introduce a teacher-student strategy. It relies on a distillation process ensuring that the student (the proposed light network) learns from the teacher. The proposed method achieves a good trade-off between latency and accuracy. We evaluated the proposed algorithm on the MAI 2021 Monocular Depth Estimation Challenge and achieved a score of 129.41, ranked the first place, which wins the second by a large margin (129.41 v.s. 14.51). More specifically, the proposed method achieves a si-RMSE score of 0.28 with 97 ms on the Raspberry Pi 4.

关键词： Performance evaluation computer vision conferences Estimation Feature extraction Mobile handsets pattern recognition

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Beyond VQA: Generating Multi-word Answers and Rationales to Visual Questions

Beyond VQA: Generating Multi-word Answers and Rationales to ...

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

作者： Dua, Radhika Kancheti, Sai Srinivas Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India

ISBN: (纸本)9781665448994

Visual Question Answering is a multi-modal task that aims to measure high-level visual understanding. Contemporary VQA models are restrictive in the sense that answers are obtained via classification over a limited vocabulary (in the case of open-ended VQA), or via classification over a set of multiple-choice-type answers. In this work, we present a completely generative formulation where a multi-word answer is generated for a visual query. To take this a step forward, we introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer. We propose an end-to-end architecture to solve this task and describe how to evaluate it. We show that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as through a human Turing Test.

关键词： Deep learning Visualization Vocabulary Computational modeling conferences computer architecture Knowledge discovery

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Adaptive Spatial-Temporal Fusion of Multi-Objective Networks for Compressed Video Perceptual Enhancement

Adaptive Spatial-Temporal Fusion of Multi-Objective Networks...

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

作者： Zheng, He Li, Xin Liu, Fanglong Jiang, Lielin Zhang, Qi Li, Fu Dang, Qingqing He, Dongliang Baidu Inc Dept Comp Vis Technol VIS Bldg 2Baidu Sci Pk Beijing Peoples R China

ISBN: (纸本)9781665448994

Perceptual quality enhancement of heavily compressed videos is a difficult, unsolved problem because there still not exists a suitable perceptual similarity loss function between two video pairs. Motivated by the fact that it is hard to design unified training objectives which are perceptual-friendly for enhancing regions with smooth content and regions with rich textures simultaneously, in this paper, we propose a simple yet effective novel solution dubbed "Adaptive Spatial-Temporal Fusion of Two-Stage Multi-Objective Networks" (ASTF) to adaptive fuse the enhancement results from networks trained with two different optimization objectives. Specifically, the proposed ASTF takes an enhancement frame along with its neighboring frames as input to jointly predict a mask to indicate regions with high-frequency textual details. Then we use the mask to fuse two enhancement results which can retain both smooth content and rich textures. Extensive experiments show that our method achieves a promising performance of compressed video perceptual quality enhancement.

关键词： Training computer vision Adaptation models Adaptive systems Fuses conferences pattern recognition

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Traffic Video Event Retrieval via Text Query using Vehicle Appearance and Motion Attributes

Traffic Video Event Retrieval via Text Query using Vehicle A...

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

作者： Tien-Phat Nguyen Ba-Thinh Tran-Le Xuan-Dang Thai Nguyen, Tam, V Do, Minh N. Minh-Triet Tran Univ Sci Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam John von Neumann Inst Ho Chi Minh City Vietnam Univ Dayton Dayton OH 45469 USA Univ Illinois Urbana IL USA

ISBN: (纸本)9781665448994

Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query's attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset of Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.

关键词： Visualization computer vision Tracking conferences Urban areas Semantics pattern recognition

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Long-Tailed recognition of SAR Aerial View Objects by Cascading and Paralleling Experts

Long-Tailed Recognition of SAR Aerial View Objects by Cascad...

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

作者： Yang, Cheng-Yen Hsu, Hung-Min Cai, Jiarui Hwang, Jenq-Neng Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA

ISBN: (纸本)9781665448994

Aerial View Object Classification (AVOC) has started to adopt deep learning approaches with significant success in recent years, but limited to optical data. On the other hand, Synthetic Aperture Radar (SAR) has wild aerial view related applications in the remote sensing field. However, SAR has received far less attention due to the special characteristics of the SAR data, which is the long-tailed distribution of the aerial view objects that increases the difficulty of classification. In this paper, we present a two-branch framework, including the cascading expert branch and paralleling expert branch, to tackle the long-tailed distribution of the dataset. Our proposed multi-expert architecture achieves 24.675% and 26.029% in the development phase and testing phase, respectively, in the NTIRE 2021 Multi-modal Aerial View Object Classification Challenge Track I. The proposed method is proved to possess the effectiveness (top-tier performance among 157 participants) and efficiency (i.e., a lightweight architecture) for the AVOC task.

关键词： computer architecture Radar tracking Optical imaging Radar polarimetry pattern recognition Optical sensors Task analysis

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