WACV
Collection
Accepted papers for WACV (IEEE/CVF Winter Conference on Applications of Computer Vision), one dataset per year. • 7 items • Updated
paper_id stringlengths 31 121 | title stringlengths 8 133 | authors listlengths 1 11 | cvf_url stringlengths 89 179 | pdf_url stringlengths 90 180 | supp_url stringlengths 101 137 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | bibtex large_stringlengths 207 508 | abstract large_stringlengths 608 1.9k |
|---|---|---|---|---|---|---|---|---|---|
Sang_Inferring_Super-Resolution_Depth_from_a_Moving_Light-Source_Enhanced_RGB-D_Sensor_WACV_2020_paper | Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach | [
"Lu Sang",
"Bjoern Haefner",
"Daniel Cremers"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Sang_Inferring_Super-Resolution_Depth_from_a_Moving_Light-Source_Enhanced_RGB-D_Sensor_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Sang_Inferring_Super-Resolution_Depth_from_a_Moving_Light-Source_Enhanced_RGB-D_Sensor_WACV_2020_paper.pdf | null | 1912.06501 | title_snapshot | @InProceedings{Sang_2020_WACV,author = {Sang, Lu and Haefner, Bjoern and Cremers, Daniel},title = {Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {Mar... | A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented. Practically, an LED light source is attached to a commodity RGB-D sensor and is used to capture objects from multiple viewpoints with unknown motion. This non-static camera-to-object setup is described wit... |
Nabavi_Unsupervised_Learning_of_Camera_Pose_with_Compositional_Re-estimation_WACV_2020_paper | Unsupervised Learning of Camera Pose with Compositional Re-estimation | [
"Seyed shahabeddin Nabavi",
"Mehrdad Hosseinzadeh",
"Ramin Fahimi",
"Yang Wang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Nabavi_Unsupervised_Learning_of_Camera_Pose_with_Compositional_Re-estimation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Nabavi_Unsupervised_Learning_of_Camera_Pose_with_Compositional_Re-estimation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Nabavi_Unsupervised_Learning_of_WACV_2020_supplemental.pdf | 2001.06479 | title_snapshot | @InProceedings{Nabavi_2020_WACV,author = {Nabavi, Seyed shahabeddin and Hosseinzadeh, Mehrdad and Fahimi, Ramin and Wang, Yang},title = {Unsupervised Learning of Camera Pose with Compositional Re-estimation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {M... | We consider the problem of unsupervised camera pose estimation. Given an input video sequence, our goal is to estimate the camera pose (i.e. the camera motion) between consecutive frames. Traditionally, this problem is tackled by placing strict constraints on the transformation vector or by incorporating optical flow t... |
Ramasinghe_Blended_Convolution_and_Synthesis_for_Efficient_Discrimination_of_3D_Shapes_WACV_2020_paper | Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes | [
"Sameera Ramasinghe",
"Salman Khan",
"Nick Barnes",
"Stephen Gould"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Ramasinghe_Blended_Convolution_and_Synthesis_for_Efficient_Discrimination_of_3D_Shapes_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Ramasinghe_Blended_Convolution_and_Synthesis_for_Efficient_Discrimination_of_3D_Shapes_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Ramasinghe_Blended_Convolution_and_WACV_2020_supplemental.pdf | 1908.10209 | title_snapshot | @InProceedings{Ramasinghe_2020_WACV,author = {Ramasinghe, Sameera and Khan, Salman and Barnes, Nick and Gould, Stephen},title = {Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {Mar... | Existing models for shape analysis directly learn feature representations on 3D point clouds. We argue that 3D point clouds are highly redundant and hold irregular (permutation-invariant) structure, which makes it difficult to achieve inter-class discrimination efficiently. In this paper, we propose a two-pronged solut... |
Li_A_Multi-Scale_Guided_Cascade_Hourglass_Network_for_Depth_Completion_WACV_2020_paper | A Multi-Scale Guided Cascade Hourglass Network for Depth Completion | [
"Ang Li",
"Zejian Yuan",
"Yonggen Ling",
"Wanchao Chi",
"shenghao zhang",
"Chong Zhang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Li_A_Multi-Scale_Guided_Cascade_Hourglass_Network_for_Depth_Completion_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Li_A_Multi-Scale_Guided_Cascade_Hourglass_Network_for_Depth_Completion_WACV_2020_paper.pdf | null | null | null | @InProceedings{Li_2020_WACV,author = {Li, Ang and Yuan, Zejian and Ling, Yonggen and Chi, Wanchao and zhang, shenghao and Zhang, Chong},title = {A Multi-Scale Guided Cascade Hourglass Network for Depth Completion},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},mont... | Depth completion, a task to estimate the dense depth map from sparse measurement under the guidance from the high-resolution image, is essential to many computer vision applications. Most previous methods building on fully convolutional networks can not handle diverse patterns in the depth map efficiently and effective... |
Zou_Silhouette_Guided_Point_Cloud_Reconstruction_beyond_Occlusion_WACV_2020_paper | Silhouette Guided Point Cloud Reconstruction beyond Occlusion | [
"Chuhang Zou",
"Derek Hoiem"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Zou_Silhouette_Guided_Point_Cloud_Reconstruction_beyond_Occlusion_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Zou_Silhouette_Guided_Point_Cloud_Reconstruction_beyond_Occlusion_WACV_2020_paper.pdf | null | 1907.12253 | title_snapshot | @InProceedings{Zou_2020_WACV,author = {Zou, Chuhang and Hoiem, Derek},title = {Silhouette Guided Point Cloud Reconstruction beyond Occlusion},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | One major challenge in 3D reconstruction is to infer the complete shape geometry from partial foreground occlusions. In this paper, we propose a method to reconstruct the complete 3D shape of an object from a single RGB image, with robustness to occlusion. Given the image and a silhouette of the visible region, our app... |
Kumar_Non-Rigid_Structure_from_Motion_Prior-Free_Factorization_Method_Revisited_WACV_2020_paper | Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited | [
"Suryansh Kumar"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kumar_Non-Rigid_Structure_from_Motion_Prior-Free_Factorization_Method_Revisited_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kumar_Non-Rigid_Structure_from_Motion_Prior-Free_Factorization_Method_Revisited_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Kumar_Non-Rigid_Structure_from_WACV_2020_supplemental.pdf | 1902.10274 | title_snapshot | @InProceedings{Kumar_2020_WACV,author = {Kumar, Suryansh},title = {Non-Rigid Structure from Motion: Prior-Free Factorization Method Revisited},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | A simple prior free factorization algorithm [??] is quite often cited work in the field of Non-Rigid Structure from Motion (NRSfM). The benefit of this work lies in its simplicity of implementation, strong theoretical justification to the motion and structure estimation, and its invincible originality. Despite this, t... |
Sun_PointGrow_Autoregressively_Learned_Point_Cloud_Generation_with_Self-Attention_WACV_2020_paper | PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention | [
"Yongbin Sun",
"Yue Wang",
"Ziwei Liu",
"Joshua Siegel",
"Sanjay Sarma"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Sun_PointGrow_Autoregressively_Learned_Point_Cloud_Generation_with_Self-Attention_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Sun_PointGrow_Autoregressively_Learned_Point_Cloud_Generation_with_Self-Attention_WACV_2020_paper.pdf | null | 1810.05591 | title_snapshot | @InProceedings{Sun_2020_WACV,author = {Sun, Yongbin and Wang, Yue and Liu, Ziwei and Siegel, Joshua and Sarma, Sanjay},title = {PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {M... | Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional dis... |
Qu_Depth_Completion_via_Deep_Basis_Fitting_WACV_2020_paper | Depth Completion via Deep Basis Fitting | [
"Chao Qu",
"Ty Nguyen",
"Camillo Taylor"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Qu_Depth_Completion_via_Deep_Basis_Fitting_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Qu_Depth_Completion_via_Deep_Basis_Fitting_WACV_2020_paper.pdf | null | 1912.10336 | title_snapshot | @InProceedings{Qu_2020_WACV,author = {Qu, Chao and Nguyen, Ty and Taylor, Camillo},title = {Depth Completion via Deep Basis Fitting},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements. We propose a novel approach that builds upon the strengths of modern deep learning techniques and classical fittin... |
Agrawal_High_Accuracy_Face_Geometry_Capture_using_a_Smartphone_Video_WACV_2020_paper | High Accuracy Face Geometry Capture using a Smartphone Video | [
"Shubham Agrawal",
"Anuj Pahuja",
"Simon Lucey"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Agrawal_High_Accuracy_Face_Geometry_Capture_using_a_Smartphone_Video_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Agrawal_High_Accuracy_Face_Geometry_Capture_using_a_Smartphone_Video_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Agrawal_High_Accuracy_Face_WACV_2020_supplemental.pdf | 2003.08583 | title_snapshot | @InProceedings{Agrawal_2020_WACV,author = {Agrawal, Shubham and Pahuja, Anuj and Lucey, Simon},title = {High Accuracy Face Geometry Capture using a Smartphone Video},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | What's the most accurate 3D model of your face you can obtain while sitting at your desk? We attempt to answer this question in our work. High fidelity face reconstructions have so far been limited to either studio settings or through expensive 3D scanners. On the other hand, unconstrained reconstruction methods are ty... |
Wang_FlowNet3D_Geometric_Losses_For_Deep_Scene_Flow_Estimation_WACV_2020_paper | FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation | [
"Zirui Wang",
"Shuda Li",
"Henry Howard-Jenkins",
"Victor Prisacariu",
"Min Chen"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Wang_FlowNet3D_Geometric_Losses_For_Deep_Scene_Flow_Estimation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Wang_FlowNet3D_Geometric_Losses_For_Deep_Scene_Flow_Estimation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Wang_FlowNet3D_Geometric_Losses_WACV_2020_supplemental.pdf | 1912.01438 | title_snapshot | @InProceedings{Wang_2020_WACV,author = {Wang, Zirui and Li, Shuda and Howard-Jenkins, Henry and Prisacariu, Victor and Chen, Min},title = {FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},y... | We present FlowNet3D++, a deep scene flow estimation network. Inspired by classical methods, FlowNet3D++ incorporates geometric constraints in the form of point-toplane distance and angular alignment between individual vectors in the flow field, into FlowNet3D. We demonstrate that the addition of these geometric loss t... |
Hart_Style_Transfer_for_Light_Field_Photography_WACV_2020_paper | Style Transfer for Light Field Photography | [
"David Hart",
"Bryan Morse",
"Jessica Greenland"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Hart_Style_Transfer_for_Light_Field_Photography_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Hart_Style_Transfer_for_Light_Field_Photography_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Hart_Style_Transfer_for_WACV_2020_supplemental.pdf | 2002.11220 | title_snapshot | @InProceedings{Hart_2020_WACV,author = {Hart, David and Morse, Bryan and Greenland, Jessica},title = {Style Transfer for Light Field Photography},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | As light field images continue to increase in use and application, it becomes necessary to adapt existing image processing methods to this unique form of photography. In this paper we explore methods for applying neural style transfer to light field images. Feed-forward style transfer networks provide fast, high-qualit... |
James_Fourier_Based_Pre-Processing_For_Seeing_Through_Water_WACV_2020_paper | Fourier Based Pre-Processing For Seeing Through Water | [
"Jerin Geo James",
"Ajit Rajwade"
] | https://openaccess.thecvf.com/content_WACV_2020/html/James_Fourier_Based_Pre-Processing_For_Seeing_Through_Water_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/James_Fourier_Based_Pre-Processing_For_Seeing_Through_Water_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/James_Fourier_Based_Pre-Processing_WACV_2020_supplemental.pdf | null | null | @InProceedings{James_2020_WACV,author = {James, Jerin Geo and Rajwade, Ajit},title = {Fourier Based Pre-Processing For Seeing Through Water},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Consider a scene submerged underneath a fluctuating water surface. Images of such a scene, when acquired from a camera in the air, exhibit significant spatial distortions. In this paper, we present a novel, computationally efficient pre-processing algorithm to correct a significant amount (~ 50%) of apparent distortio... |
Wang_DeOccNet_Learning_to_See_Through_Foreground_Occlusions_in_Light_Fields_WACV_2020_paper | DeOccNet: Learning to See Through Foreground Occlusions in Light Fields | [
"Yingqian Wang",
"Tianhao Wu",
"Jungang Yang",
"Longguang Wang",
"Wei An",
"Yulan Guo"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Wang_DeOccNet_Learning_to_See_Through_Foreground_Occlusions_in_Light_Fields_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Wang_DeOccNet_Learning_to_See_Through_Foreground_Occlusions_in_Light_Fields_WACV_2020_paper.pdf | null | 1912.04459 | title_snapshot | @InProceedings{Wang_2020_WACV,author = {Wang, Yingqian and Wu, Tianhao and Yang, Jungang and Wang, Longguang and An, Wei and Guo, Yulan},title = {DeOccNet: Learning to See Through Foreground Occlusions in Light Fields},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}... | Background objects occluded in some views of a light field (LF) camera can be seen by other views. Consequently, occluded surfaces are possible to be reconstructed from LF images. In this paper, we handle the LF de-occlusion (LF-DeOcc) problem using a deep encoder-decoder network (namely, DeOccNet). In our method, sub... |
Kawahara_Appearance_and_Shape_from_Water_Reflection_WACV_2020_paper | Appearance and Shape from Water Reflection | [
"Ryo Kawahara",
"Meng-Yu Kuo",
"Shohei Nobuhara",
"Ko Nishino"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kawahara_Appearance_and_Shape_from_Water_Reflection_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kawahara_Appearance_and_Shape_from_Water_Reflection_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Kawahara_Appearance_and_Shape_WACV_2020_supplemental.pdf | 1906.10284 | title_snapshot | @InProceedings{Kawahara_2020_WACV,author = {Kawahara, Ryo and Kuo, Meng-Yu and Nobuhara, Shohei and Nishino, Ko},title = {Appearance and Shape from Water Reflection},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | This paper introduces single-image geometric and appearance reconstruction from water reflection photography, i.e., images capturing direct and water-reflected real-world scenes. Water reflection offers an additional viewpoint to the direct sight, collectively forming a stereo pair. The water-reflected scene, however, ... |
Hessel_An_Extended_Exposure_Fusion_and_its_Application_to_Single_Image_WACV_2020_paper | An Extended Exposure Fusion and its Application to Single Image Contrast Enhancement | [
"Charles Hessel",
"Jean-Michel Morel"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Hessel_An_Extended_Exposure_Fusion_and_its_Application_to_Single_Image_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Hessel_An_Extended_Exposure_Fusion_and_its_Application_to_Single_Image_WACV_2020_paper.pdf | null | null | null | @InProceedings{Hessel_2020_WACV,author = {Hessel, Charles and Morel, Jean-Michel},title = {An Extended Exposure Fusion and its Application to Single Image Contrast Enhancement},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Exposure Fusion is a high dynamic range imaging technique fusing a bracketed exposure sequence into a high quality image. In this paper, we provide a refined version resolving its out-of-range artifact and its low-frequency halo. It improves on the original Exposure Fusion by augmenting contrast in all image parts. Fur... |
Abuolaim_Online_Lens_Motion_Smoothing_for_Video_Autofocus_WACV_2020_paper | Online Lens Motion Smoothing for Video Autofocus | [
"Abdullah Abuolaim",
"Michael Brown"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Abuolaim_Online_Lens_Motion_Smoothing_for_Video_Autofocus_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Abuolaim_Online_Lens_Motion_Smoothing_for_Video_Autofocus_WACV_2020_paper.pdf | null | null | null | @InProceedings{Abuolaim_2020_WACV,author = {Abuolaim, Abdullah and Brown, Michael},title = {Online Lens Motion Smoothing for Video Autofocus},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Autofocus (AF) is the process of moving the camera's lens such that desired scene content is in focus. AF for single image capture is a well-studied research topic and most modern cameras have hardware support that allows quick lens movements to optimize image sharpness. How to best perform AF for video is less clea... |
Scheerlinck_Fast_Image_Reconstruction_with_an_Event_Camera_WACV_2020_paper | Fast Image Reconstruction with an Event Camera | [
"Cedric Scheerlinck",
"Henri Rebecq",
"Daniel Gehrig",
"Nick Barnes",
"Robert Mahony",
"Davide Scaramuzza"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Scheerlinck_Fast_Image_Reconstruction_with_an_Event_Camera_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Scheerlinck_Fast_Image_Reconstruction_with_an_Event_Camera_WACV_2020_paper.pdf | null | null | null | @InProceedings{Scheerlinck_2020_WACV,author = {Scheerlinck, Cedric and Rebecq, Henri and Gehrig, Daniel and Barnes, Nick and Mahony, Robert and Scaramuzza, Davide},title = {Fast Image Reconstruction with an Event Camera},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV... | Event cameras are powerful new sensors able to capture high dynamic range with microsecond temporal resolution and no motion blur. Their strength is detecting brightness changes (called events) rather than capturing direct brightness images; however, algorithms can be used to convert events into usable image representa... |
Liu_Self-Guided_Novel_View_Synthesis_via_Elastic_Displacement_Network_WACV_2020_paper | Self-Guided Novel View Synthesis via Elastic Displacement Network | [
"Yicun Liu",
"Jiawei Zhang",
"Ye Ma",
"Jimmy Ren"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Liu_Self-Guided_Novel_View_Synthesis_via_Elastic_Displacement_Network_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Liu_Self-Guided_Novel_View_Synthesis_via_Elastic_Displacement_Network_WACV_2020_paper.pdf | null | null | null | @InProceedings{Liu_2020_WACV,author = {Liu, Yicun and Zhang, Jiawei and Ma, Ye and Ren, Jimmy},title = {Self-Guided Novel View Synthesis via Elastic Displacement Network},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Synthesizing a novel view from different viewpoints has been an essential problem in 3D vision. Among a variety of view synthesis tasks, single image based view synthesis is particularly challenging. Recent works address this problem by a fixed number of image planes of discrete disparities, which tend to generate stru... |
Rangel_On_Scene_Flow_Computation_of_Gas_Structures__with_Optical_WACV_2020_paper | On Scene Flow Computation of Gas Structures with Optical Gas Imaging Cameras | [
"Johannes Rangel",
"Robert Schmoll",
"Andreas Kroll"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Rangel_On_Scene_Flow_Computation_of_Gas_Structures__with_Optical_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Rangel_On_Scene_Flow_Computation_of_Gas_Structures__with_Optical_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Rangel_On_Scene_Flow_WACV_2020_supplemental.pdf | null | null | @InProceedings{Rangel_2020_WACV,author = {Rangel, Johannes and Schmoll, Robert and Kroll, Andreas},title = {On Scene Flow Computation of Gas Structures with Optical Gas Imaging Cameras},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Gas leak inspection and gas leak quantification are nowadays of high relevance within the oil and gas industry as well as in many other industrial sectors. This has been driven by safety-related issues, economic losses and the considerable climate impact caused by such unwanted gas releases. Due to the latter, the effo... |
Yee_Fast_Deep_Stereo_with_2D_Convolutional_Processing_of_Cost_Signatures_WACV_2020_paper | Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures | [
"Kyle Yee",
"Ayan Chakrabarti"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Yee_Fast_Deep_Stereo_with_2D_Convolutional_Processing_of_Cost_Signatures_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Yee_Fast_Deep_Stereo_with_2D_Convolutional_Processing_of_Cost_Signatures_WACV_2020_paper.pdf | null | 1903.04939 | title_snapshot | @InProceedings{Yee_2020_WACV,author = {Yee, Kyle and Chakrabarti, Ayan},title = {Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Modern neural network-based algorithms are able to produce highly accurate depth estimates from stereo image pairs, nearly matching the reliability of measurements from more expensive depth sensors. However, this accuracy comes with a higher computational cost since these methods use network architectures designed to c... |
Kallwies_Triple-SGM_Stereo_Processing_using_Semi-Global_Matching_with_Cost_Fusion_WACV_2020_paper | Triple-SGM: Stereo Processing using Semi-Global Matching with Cost Fusion | [
"Jan Kallwies",
"Torsten Engler",
"Bianca Forkel",
"Hans-Joachim Wuensche"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kallwies_Triple-SGM_Stereo_Processing_using_Semi-Global_Matching_with_Cost_Fusion_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kallwies_Triple-SGM_Stereo_Processing_using_Semi-Global_Matching_with_Cost_Fusion_WACV_2020_paper.pdf | null | null | null | @InProceedings{Kallwies_2020_WACV,author = {Kallwies, Jan and Engler, Torsten and Forkel, Bianca and Wuensche, Hans-Joachim},title = {Triple-SGM: Stereo Processing using Semi-Global Matching with Cost Fusion},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {... | In this work, we propose an extension of the Semi-Global Matching framework for three images from a stereo rig consisting of a horizontal and vertical camera pair. After calculating the matching costs separately for both image pairs, these are merged at cost level using cubic spline interpolation. For cost values near ... |
Jiang_Optimizing_Through_Learned_Errors_for_Accurate_Sports_Field_Registration_WACV_2020_paper | Optimizing Through Learned Errors for Accurate Sports Field Registration | [
"Wei Jiang",
"Juan Camilo Gamboa Higuera",
"Baptiste Angles",
"Weiwei Sun",
"Mehrsan Javan",
"Kwang Moo Yi"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Jiang_Optimizing_Through_Learned_Errors_for_Accurate_Sports_Field_Registration_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Jiang_Optimizing_Through_Learned_Errors_for_Accurate_Sports_Field_Registration_WACV_2020_paper.pdf | null | 1909.08034 | title_snapshot | @InProceedings{Jiang_2020_WACV,author = {Jiang, Wei and Higuera, Juan Camilo Gamboa and Angles, Baptiste and Sun, Weiwei and Javan, Mehrsan and Yi, Kwang Moo},title = {Optimizing Through Learned Errors for Accurate Sports Field Registration},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of ... | We propose an optimization-based framework to register sports field templates onto broadcast videos. For accurate registration we go beyond the prevalent feed-forward paradigm. Instead, we propose to train a deep network that regresses the registration error, and then register images by finding the registration paramet... |
Zhu_Reference_Grid-assisted_Network_for_3D_Point_Signature_Learning_from_Point_WACV_2020_paper | Reference Grid-assisted Network for 3D Point Signature Learning from Point Clouds | [
"Jing Zhu",
"Yi Fang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Zhu_Reference_Grid-assisted_Network_for_3D_Point_Signature_Learning_from_Point_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Zhu_Reference_Grid-assisted_Network_for_3D_Point_Signature_Learning_from_Point_WACV_2020_paper.pdf | null | null | null | @InProceedings{Zhu_2020_WACV,author = {Zhu, Jing and Fang, Yi},title = {Reference Grid-assisted Network for 3D Point Signature Learning from Point Clouds},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Learning a robust 3D point signature from point clouds is an interesting but challenging task in the computer vision field due to the irregular and unordered structure characteristics of the point cloud data. In this paper, we propose to learn a 3D point signature by exploring the implicit relation between keypoints an... |
Fetzer_Stable_Intrinsic_Auto-Calibration_from_Fundamental_Matrices_of_Devices_with_Uncorrelated_WACV_2020_paper | Stable Intrinsic Auto-Calibration from Fundamental Matrices of Devices with Uncorrelated Camera Parameters | [
"Torben Fetzer",
"Gerd Reis",
"Didier Stricker"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Fetzer_Stable_Intrinsic_Auto-Calibration_from_Fundamental_Matrices_of_Devices_with_Uncorrelated_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Fetzer_Stable_Intrinsic_Auto-Calibration_from_Fundamental_Matrices_of_Devices_with_Uncorrelated_WACV_2020_paper.pdf | null | null | null | @InProceedings{Fetzer_2020_WACV,author = {Fetzer, Torben and Reis, Gerd and Stricker, Didier},title = {Stable Intrinsic Auto-Calibration from Fundamental Matrices of Devices with Uncorrelated Camera Parameters},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month =... | Auto-Calibration is an important task in computer vision and is necessary for many visual applications. Methods like photogrammetry, depth map estimation, metrology, augmented/mixed reality or odometry are strongly dependent on well calibrated devices. While classical calibration relies on tools like checkerboards or... |
Zhang_Deep_Image_Blending_WACV_2020_paper | Deep Image Blending | [
"Lingzhi Zhang",
"Tarmily Wen",
"Jianbo Shi"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Zhang_Deep_Image_Blending_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Zhang_Deep_Image_Blending_WACV_2020_paper.pdf | null | 1910.11495 | title_snapshot | @InProceedings{Zhang_2020_WACV,author = {Zhang, Lingzhi and Wen, Tarmily and Shi, Jianbo},title = {Deep Image Blending},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Image composition is an important operation to create visual content. Among image composition tasks, image blending aims to seamlessly blend an object from a source image onto a target image with lightly mask adjustment. A popular approach is Poisson image blending, which enforces the gradient domain smoothness in the ... |
Mokhayeri_Cross-Domain_Face_Synthesis_using_a_Controllable_GAN_WACV_2020_paper | Cross-Domain Face Synthesis using a Controllable GAN | [
"Fania Mokhayeri",
"Kaveh Kamali",
"Eric Granger"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Mokhayeri_Cross-Domain_Face_Synthesis_using_a_Controllable_GAN_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Mokhayeri_Cross-Domain_Face_Synthesis_using_a_Controllable_GAN_WACV_2020_paper.pdf | null | 1910.14247 | title_snapshot | @InProceedings{Mokhayeri_2020_WACV,author = {Mokhayeri, Fania and Kamali, Kaveh and Granger, Eric},title = {Cross-Domain Face Synthesis using a Controllable GAN},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | The performance of face recognition (FR) systems for video surveillance has been shown to improve when the design data is augmented through synthetic face generation. This is true, for instance, with pair-wise matchers (e.g., deep Siamese networks) that rely on a reference gallery, typically with one still image per in... |
Albiero_Does_Face_Recognition_Accuracy_Get_Better_With_Age_Deep_Face_WACV_2020_paper | Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No | [
"Vitor Albiero",
"Kevin Bowyer",
"Kushal Vangara",
"Michael King"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Albiero_Does_Face_Recognition_Accuracy_Get_Better_With_Age_Deep_Face_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Albiero_Does_Face_Recognition_Accuracy_Get_Better_With_Age_Deep_Face_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Albiero_Does_Face_Recognition_WACV_2020_supplemental.pdf | 1911.06396 | title_snapshot | @InProceedings{Albiero_2020_WACV,author = {Albiero, Vitor and Bowyer, Kevin and Vangara, Kushal and King, Michael},title = {Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March}... | Previous studies generally agree that face recognition accuracy is higher for older persons than for younger persons. But most previous studies were before the wave of deep learning matchers, and most considered accuracy only in terms of the verification rate for genuine pairs. This paper investigates accuracy for age ... |
Chen_Offset_Calibration_for_Appearance-Based_Gaze_Estimation_via_Gaze_Decomposition_WACV_2020_paper | Offset Calibration for Appearance-Based Gaze Estimation via Gaze Decomposition | [
"Zhaokang Chen",
"Bertram Shi"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Chen_Offset_Calibration_for_Appearance-Based_Gaze_Estimation_via_Gaze_Decomposition_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Chen_Offset_Calibration_for_Appearance-Based_Gaze_Estimation_via_Gaze_Decomposition_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Chen_Offset_Calibration_for_WACV_2020_supplemental.pdf | 1905.04451 | title_snapshot | @InProceedings{Chen_2020_WACV,author = {Chen, Zhaokang and Shi, Bertram},title = {Offset Calibration for Appearance-Based Gaze Estimation via Gaze Decomposition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Appearance-based gaze estimation provides relatively unconstrained gaze tracking. However, subject-independent models achieve limited accuracy partly due to individual variations. To improve estimation, we propose a gaze decomposition method that enables low complexity calibration, i.e., using calibration data collecte... |
Venkatesh_Detecting_Morphed_Face_Attacks_Using_Residual_Noise_from_Deep_Multi-Scale_WACV_2020_paper | Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-Scale Context Aggregation Network | [
"Sushma Venkatesh",
"Raghavendra Ramachandra",
"Kiran Raja",
"Luuk Spreeuwers",
"Raymond Veldhuis",
"Christoph Busch"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Venkatesh_Detecting_Morphed_Face_Attacks_Using_Residual_Noise_from_Deep_Multi-Scale_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Venkatesh_Detecting_Morphed_Face_Attacks_Using_Residual_Noise_from_Deep_Multi-Scale_WACV_2020_paper.pdf | null | null | null | @InProceedings{Venkatesh_2020_WACV,author = {Venkatesh, Sushma and Ramachandra, Raghavendra and Raja, Kiran and Spreeuwers, Luuk and Veldhuis, Raymond and Busch, Christoph},title = {Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-Scale Context Aggregation Network},booktitle = {Proceedings of the IEE... | The evolving deployment of face recognition system has raised concerns regarding the vulnerability of those systems to various attacks. The morphed face attack involves two different face images via morphing to obtain an attack image face image similar to both original images. The obtained morphed image can easily be v... |
Dias_Gaze_Estimation_for_Assisted_Living_Environments_WACV_2020_paper | Gaze Estimation for Assisted Living Environments | [
"Philipe Ambrozio Dias",
"Damiano Malafronte",
"Henry Medeiros",
"Francesca Odone"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Dias_Gaze_Estimation_for_Assisted_Living_Environments_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Dias_Gaze_Estimation_for_Assisted_Living_Environments_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Dias_Gaze_Estimation_for_WACV_2020_supplemental.pdf | 1909.09225 | title_snapshot | @InProceedings{Dias_2020_WACV,author = {Dias, Philipe Ambrozio and Malafronte, Damiano and Medeiros, Henry and Odone, Francesca},title = {Gaze Estimation for Assisted Living Environments},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Effective assisted living environments must be able to perform inferences on how their occupants interact with one another as well as with surrounding objects. To accomplish this goal using a vision-based automated approach, multiple tasks such as pose estimation, object segmentation and gaze estimation must be address... |
Banerjee_On_Hallucinating_Context_and_Background_Pixels_from_a_Face_Mask_WACV_2020_paper | On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs | [
"Sandipan Banerjee",
"Walter Scheirer",
"Kevin Bowyer",
"Patrick Flynn"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Banerjee_On_Hallucinating_Context_and_Background_Pixels_from_a_Face_Mask_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Banerjee_On_Hallucinating_Context_and_Background_Pixels_from_a_Face_Mask_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Banerjee_On_Hallucinating_Context_WACV_2020_supplemental.pdf | 1811.07104 | title_snapshot | @InProceedings{Banerjee_2020_WACV,author = {Banerjee, Sandipan and Scheirer, Walter and Bowyer, Kevin and Flynn, Patrick},title = {On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}... | We propose a multi-scale GAN model to hallucinate realistic context (forehead, hair, neck, clothes) and background pixels automatically from a single input face mask, without any user supervision. Instead of swapping a face on to an existing picture, our model directly generates realistic context and background pixels ... |
Kaur_EyeGAN_Gaze-Preserving_Mask-Mediated_Eye_Image_Synthesis_WACV_2020_paper | EyeGAN: Gaze-Preserving, Mask-Mediated Eye Image Synthesis | [
"Harsimran Kaur",
"Roberto Manduchi"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kaur_EyeGAN_Gaze-Preserving_Mask-Mediated_Eye_Image_Synthesis_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kaur_EyeGAN_Gaze-Preserving_Mask-Mediated_Eye_Image_Synthesis_WACV_2020_paper.pdf | null | null | null | @InProceedings{Kaur_2020_WACV,author = {Kaur, Harsimran and Manduchi, Roberto},title = {EyeGAN: Gaze-Preserving, Mask-Mediated Eye Image Synthesis},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Automatic synthesis of realistic eye images with prescribed gaze direction is important for multiple application domains. We introduce EyeGAN, an algorithm to generate eye images in the style of a desired target domain, that inherit annotations available in images from a source domain. EyeGAN takes in input ternary mas... |
Dabouei_Boosting_Deep_Face_Recognition_via_Disentangling_Appearance_and_Geometry_WACV_2020_paper | Boosting Deep Face Recognition via Disentangling Appearance and Geometry | [
"Ali Dabouei",
"Fariborz Taherkhani",
"Sobhan Soleymani",
"Jeremy Dawson",
"Nasser Nasrabadi"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Dabouei_Boosting_Deep_Face_Recognition_via_Disentangling_Appearance_and_Geometry_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Dabouei_Boosting_Deep_Face_Recognition_via_Disentangling_Appearance_and_Geometry_WACV_2020_paper.pdf | null | 2001.04559 | title_snapshot | @InProceedings{Dabouei_2020_WACV,author = {Dabouei, Ali and Taherkhani, Fariborz and Soleymani, Sobhan and Dawson, Jeremy and Nasrabadi, Nasser},title = {Boosting Deep Face Recognition via Disentangling Appearance and Geometry},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Visio... | In this paper, we propose a framework for disentangling the appearance and geometry representations in the face recognition task. To provide supervision for this aim, we generate geometrically identical faces by incorporating spatial transformations. We demonstrate that the proposed approach enhances the performance of... |
Iranmanesh_Robust_Facial_Landmark_Detection_via_Aggregation_on_Geometrically_Manipulated_Faces_WACV_2020_paper | Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces | [
"Seyed Mehdi Iranmanesh",
"Ali Dabouei",
"Sobhan Soleymani",
"Hadi Kazemi",
"Nasser Nasrabadi"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Iranmanesh_Robust_Facial_Landmark_Detection_via_Aggregation_on_Geometrically_Manipulated_Faces_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Iranmanesh_Robust_Facial_Landmark_Detection_via_Aggregation_on_Geometrically_Manipulated_Faces_WACV_2020_paper.pdf | null | 2001.03113 | title_snapshot | @InProceedings{Iranmanesh_2020_WACV,author = {Iranmanesh, Seyed Mehdi and Dabouei, Ali and Soleymani, Sobhan and Kazemi, Hadi and Nasrabadi, Nasser},title = {Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of... | In this work, we present a practical approach to the problem of facial landmark detection. The proposed method can deal with large shape and appearance variations under the rich shape deformation. To handle the shape variations we equip our method with the aggregation of manipulated face images. The proposed framework ... |
Shalev_End_to_End_Lip_Synchronization_with_a_Temporal_AutoEncoder_WACV_2020_paper | End to End Lip Synchronization with a Temporal AutoEncoder | [
"Yoav Shalev",
"Lior Wolf"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Shalev_End_to_End_Lip_Synchronization_with_a_Temporal_AutoEncoder_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Shalev_End_to_End_Lip_Synchronization_with_a_Temporal_AutoEncoder_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Shalev_End_to_End_WACV_2020_supplemental.pdf | 2203.16224 | title_snapshot | @InProceedings{Shalev_2020_WACV,author = {Shalev, Yoav and Wolf, Lior},title = {End to End Lip Synchronization with a Temporal AutoEncoder},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | We study the problem of syncing the lip movement in a video with the audio stream. Our solution finds an optimal alignment using a dual-domain recurrent neural network that is trained on synthetic data we generate by dropping and duplicating video frames. Once the alignment is found, we modify the video in order to syn... |
Chowdhury_Can_a_CNN_Automatically_Learn_the_Significance_of_Minutiae_Points_WACV_2020_paper | Can a CNN Automatically Learn the Significance of Minutiae Points for Fingerprint Matching? | [
"Anurag Chowdhury",
"Simon Kirchgasser",
"Andreas Uhl",
"Arun Ross"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Chowdhury_Can_a_CNN_Automatically_Learn_the_Significance_of_Minutiae_Points_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Chowdhury_Can_a_CNN_Automatically_Learn_the_Significance_of_Minutiae_Points_WACV_2020_paper.pdf | null | null | null | @InProceedings{Chowdhury_2020_WACV,author = {Chowdhury, Anurag and Kirchgasser, Simon and Uhl, Andreas and Ross, Arun},title = {Can a CNN Automatically Learn the Significance of Minutiae Points for Fingerprint Matching?},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV... | Most automated fingerprint recognition systems use minutiae points for comparing fingerprints. In the parlance of Computer Vision, minutiae can be viewed as handcrafted features, i.e., features that have been proposed by human experts for the task of fingerprint recognition. In this work, we raise the following questio... |
Gong_AutoToon_Automatic_Geometric_Warping_for_Face_Cartoon_Generation_WACV_2020_paper | AutoToon: Automatic Geometric Warping for Face Cartoon Generation | [
"Julia Gong",
"Yannick Hold-Geoffroy",
"Jingwan Lu"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Gong_AutoToon_Automatic_Geometric_Warping_for_Face_Cartoon_Generation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Gong_AutoToon_Automatic_Geometric_Warping_for_Face_Cartoon_Generation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Gong_AutoToon_Automatic_Geometric_WACV_2020_supplemental.pdf | 2004.02377 | title_snapshot | @InProceedings{Gong_2020_WACV,author = {Gong, Julia and Hold-Geoffroy, Yannick and Lu, Jingwan},title = {AutoToon: Automatic Geometric Warping for Face Cartoon Generation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects' unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promi... |
Kalarot_Component_Attention_Guided_Face_Super-Resolution_Network_CAGFace_WACV_2020_paper | Component Attention Guided Face Super-Resolution Network: CAGFace | [
"Ratheesh Kalarot",
"Tao Li",
"Fatih Porikli"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kalarot_Component_Attention_Guided_Face_Super-Resolution_Network_CAGFace_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kalarot_Component_Attention_Guided_Face_Super-Resolution_Network_CAGFace_WACV_2020_paper.pdf | null | 1910.08761 | title_snapshot | @InProceedings{Kalarot_2020_WACV,author = {Kalarot, Ratheesh and Li, Tao and Porikli, Fatih},title = {Component Attention Guided Face Super-Resolution Network: CAGFace},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | To make the best use of the underlying structure of faces, the collective information through face datasets and the intermediate estimates during the upsampling process, here we introduce a fully convolutional multi-stage neural network for 4x super-resolution for face images. We implicitly impose facial component-wise... |
Chen_Nonparametric_Structure_Regularization_Machine_for_2D_Hand_Pose_Estimation_WACV_2020_paper | Nonparametric Structure Regularization Machine for 2D Hand Pose Estimation | [
"Yifei Chen",
"Haoyu Ma",
"Deying Kong",
"Xiangyi Yan",
"Jianbao Wu",
"Wei Fan",
"Xiaohui Xie"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Chen_Nonparametric_Structure_Regularization_Machine_for_2D_Hand_Pose_Estimation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Chen_Nonparametric_Structure_Regularization_Machine_for_2D_Hand_Pose_Estimation_WACV_2020_paper.pdf | null | 2001.08869 | title_snapshot | @InProceedings{Chen_2020_WACV,author = {Chen, Yifei and Ma, Haoyu and Kong, Deying and Yan, Xiangyi and Wu, Jianbao and Fan, Wei and Xie, Xiaohui},title = {Nonparametric Structure Regularization Machine for 2D Hand Pose Estimation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer V... | Hand pose estimation is more challenging than body pose estimation due to severe articulation, self-occlusion and high dexterity of the hand. Current approaches often rely on a popular body pose algorithm, such as the Convolutional Pose Machine (CPM), to learn 2D keypoint features. These algorithms cannot adequately ad... |
Gu_3D_Hand_Pose_Estimation_with_Disentangled_Cross-Modal_Latent_Space_WACV_2020_paper | 3D Hand Pose Estimation with Disentangled Cross-Modal Latent Space | [
"Jiajun Gu",
"Zhiyong Wang",
"Wanli Ouyang",
"weichen zhang",
"Jiafeng Li",
"Li Zhuo"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Gu_3D_Hand_Pose_Estimation_with_Disentangled_Cross-Modal_Latent_Space_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Gu_3D_Hand_Pose_Estimation_with_Disentangled_Cross-Modal_Latent_Space_WACV_2020_paper.pdf | null | null | null | @InProceedings{Gu_2020_WACV,author = {Gu, Jiajun and Wang, Zhiyong and Ouyang, Wanli and zhang, weichen and Li, Jiafeng and Zhuo, Li},title = {3D Hand Pose Estimation with Disentangled Cross-Modal Latent Space},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month =... | Estimating 3D hand pose from a single RGB image is a challenging task because of its ill-posed nature (i.e., depth ambiguity). Recently, various generative-based approaches have been proposed to predict the 3D joints by learning a unified latent space between two modalities (i.e., RGB image and 3D joints). However, pr... |
Li_Robust_Template-Based_Non-Rigid_Motion_Tracking_Using_Local_Coordinate_Regularization_WACV_2020_paper | Robust Template-Based Non-Rigid Motion Tracking Using Local Coordinate Regularization | [
"Wei Li",
"Shang Zhao",
"Xiao Xiao",
"James Hahn"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Li_Robust_Template-Based_Non-Rigid_Motion_Tracking_Using_Local_Coordinate_Regularization_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Li_Robust_Template-Based_Non-Rigid_Motion_Tracking_Using_Local_Coordinate_Regularization_WACV_2020_paper.pdf | null | null | null | @InProceedings{Li_2020_WACV,author = {Li, Wei and Zhao, Shang and Xiao, Xiao and Hahn, James},title = {Robust Template-Based Non-Rigid Motion Tracking Using Local Coordinate Regularization},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020... | In this paper, we propose our template-based non-rigid registration algorithm to address the misalignments in the frame-to-frame motion tracking with single or multiple commodity depth cameras. We analyze the deformation in the local coordinates of neighboring nodes and use this differential representation to formulate... |
Chen_DGGAN_Depth-image_Guided_Generative_Adversarial_Networks_forDisentangling_RGB_and_Depth_WACV_2020_paper | DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation | [
"Liangjian Chen",
"Shih-Yao Lin",
"Yusheng Xie",
"Yen-Yu Lin",
"Wei Fan",
"Xiaohui Xie"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Chen_DGGAN_Depth-image_Guided_Generative_Adversarial_Networks_forDisentangling_RGB_and_Depth_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Chen_DGGAN_Depth-image_Guided_Generative_Adversarial_Networks_forDisentangling_RGB_and_Depth_WACV_2020_paper.pdf | null | 2012.03197 | title_snapshot | @InProceedings{Chen_2020_WACV,author = {Chen, Liangjian and Lin, Shih-Yao and Xie, Yusheng and Lin, Yen-Yu and Fan, Wei and Xie, Xiaohui},title = {DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation},booktitle = {Proceedings of the IEEE/CVF Winter ... | Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images. State-of-the-art estimators ad-dress this problem by regularizing3D hand pose estimationmodels during training to enforc... |
Zhang_Multiview_Supervision_By_Registration_WACV_2020_paper | Multiview Supervision By Registration | [
"Yilun Zhang",
"Hyun Soo Park"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Zhang_Multiview_Supervision_By_Registration_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Zhang_Multiview_Supervision_By_Registration_WACV_2020_paper.pdf | null | 1811.11251 | title_snapshot | @InProceedings{Zhang_2020_WACV,author = {Zhang, Yilun and Park, Hyun Soo},title = {Multiview Supervision By Registration},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | This paper presents a semi-supervised learning framework to train a keypoint detector using multiview image streams given the limited labeled data (typically <4%). We leverage the complementary relationship between multiview geometry and visual tracking to provide three types of supervisionary signals to utilize the un... |
Huang_DeepFuse_An_IMU-Aware_Network_for_Real-Time_3D_Human_Pose_Estimation_WACV_2020_paper | DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image | [
"Fuyang Huang",
"Ailing Zeng",
"Minhao Liu",
"Qiuxia Lai",
"Qiang Xu"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Huang_DeepFuse_An_IMU-Aware_Network_for_Real-Time_3D_Human_Pose_Estimation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Huang_DeepFuse_An_IMU-Aware_Network_for_Real-Time_3D_Human_Pose_Estimation_WACV_2020_paper.pdf | null | 1912.04071 | title_snapshot | @InProceedings{Huang_2020_WACV,author = {Huang, Fuyang and Zeng, Ailing and Liu, Minhao and Lai, Qiuxia and Xu, Qiang},title = {DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV... | In this paper, we propose a two-stage fully 3D network, namely DeepFuse, to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply. The first stage is designed for pure vision estimation. To preserve data primitiveness of multi-view inputs, the vision stage... |
Lathuiliere_Attention-based_Fusion_for_Multi-source_Human_Image_Generation_WACV_2020_paper | Attention-based Fusion for Multi-source Human Image Generation | [
"Stephane Lathuiliere",
"Enver Sangineto",
"Aliaksandr Siarohin",
"Nicu Sebe"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Lathuiliere_Attention-based_Fusion_for_Multi-source_Human_Image_Generation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Lathuiliere_Attention-based_Fusion_for_Multi-source_Human_Image_Generation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Lathuiliere_Attention-based_Fusion_for_WACV_2020_supplemental.pdf | 1905.02655 | title_snapshot | @InProceedings{Lathuiliere_2020_WACV,author = {Lathuiliere, Stephane and Sangineto, Enver and Siarohin, Aliaksandr and Sebe, Nicu},title = {Attention-based Fusion for Multi-source Human Image Generation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March... | We present a generalization of the person-image generation task, in which a human image is generated conditioned on a target pose and a set X of source appearance images. In this way, we can exploit multiple, possibly complementary images of the same person which are usually available at training and at testing time. T... |
Buizza_Real-Time_Multi-Person_Pose_Tracking_using_Data_Assimilation_WACV_2020_paper | Real-Time Multi-Person Pose Tracking using Data Assimilation | [
"Caterina Buizza",
"Tobias Fischer",
"Yiannis Demiris"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Buizza_Real-Time_Multi-Person_Pose_Tracking_using_Data_Assimilation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Buizza_Real-Time_Multi-Person_Pose_Tracking_using_Data_Assimilation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Buizza_Real-Time_Multi-Person_Pose_WACV_2020_supplemental.pdf | null | null | @InProceedings{Buizza_2020_WACV,author = {Buizza, Caterina and Fischer, Tobias and Demiris, Yiannis},title = {Real-Time Multi-Person Pose Tracking using Data Assimilation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | We propose a framework for the integration of data assimilation and machine learning methods in human pose estimation, with the aim of enabling any pose estimation method to be run in real-time, whilst also increasing consistency and accuracy. Data assimilation and machine learning are complementary methods: the former... |
Zou_Reducing_Footskate_in_Human_Motion_Reconstruction_with_Ground_Contact_Constraints_WACV_2020_paper | Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints | [
"Yuliang Zou",
"Jimei Yang",
"Duygu Ceylan",
"Jianming Zhang",
"Federico Perazzi",
"Jia-Bin Huang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Zou_Reducing_Footskate_in_Human_Motion_Reconstruction_with_Ground_Contact_Constraints_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Zou_Reducing_Footskate_in_Human_Motion_Reconstruction_with_Ground_Contact_Constraints_WACV_2020_paper.pdf | null | null | null | @InProceedings{Zou_2020_WACV,author = {Zou, Yuliang and Yang, Jimei and Ceylan, Duygu and Zhang, Jianming and Perazzi, Federico and Huang, Jia-Bin},title = {Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Co... | In this paper, we aim to reduce the footskate artifacts when reconstructing human dynamics from monocular RGB videos. Recent work has made substantial progress in improving the temporal smoothness of the reconstructed motion trajectories. Their results, however, still suffer from severe foot skating and slippage artifa... |
Kundu_Unsupervised_Cross-Dataset_Adaptation_via_Probabilistic_Amodal_3D_Human_Pose_Completion_WACV_2020_paper | Unsupervised Cross-Dataset Adaptation via Probabilistic Amodal 3D Human Pose Completion | [
"Jogendra Nath Kundu",
"Rahul M V",
"Jay Patravali",
"Venkatesh Babu RADHAKRISHNAN"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kundu_Unsupervised_Cross-Dataset_Adaptation_via_Probabilistic_Amodal_3D_Human_Pose_Completion_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kundu_Unsupervised_Cross-Dataset_Adaptation_via_Probabilistic_Amodal_3D_Human_Pose_Completion_WACV_2020_paper.pdf | null | null | null | @InProceedings{Kundu_2020_WACV,author = {Kundu, Jogendra Nath and V, Rahul M and Patravali, Jay and RADHAKRISHNAN, Venkatesh Babu},title = {Unsupervised Cross-Dataset Adaptation via Probabilistic Amodal 3D Human Pose Completion},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Visi... | Despite remarkable success of supervised deep learning models for 3D human pose estimation, performance of such models is mostly limited to constrained laboratory settings. Such models not only exhibit an alarming level of dataset bias but also fail to operate on unconstrained videos in the presence of external variati... |
Hwang_Lightweight_3D_Human_Pose_Estimation_Network_Training_Using_Teacher-Student_Learning_WACV_2020_paper | Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning | [
"Dong-Hyun Hwang",
"Suntae Kim",
"Nicolas Monet",
"Hideki Koike",
"Soonmin Bae"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Hwang_Lightweight_3D_Human_Pose_Estimation_Network_Training_Using_Teacher-Student_Learning_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Hwang_Lightweight_3D_Human_Pose_Estimation_Network_Training_Using_Teacher-Student_Learning_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Hwang_Lightweight_3D_Human_WACV_2020_supplemental.pdf | 2001.05097 | title_snapshot | @InProceedings{Hwang_2020_WACV,author = {Hwang, Dong-Hyun and Kim, Suntae and Monet, Nicolas and Koike, Hideki and Bae, Soonmin},title = {Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (W... | We present MoVNect, a lightweight deep neural network to capture 3D human pose using a single RGB camera. To improve the overall performance of the model, we apply the teacher-student learning method based knowledge distillation to 3D human pose estimation. Real-time post-processing makes the CNN output yield temporal... |
Kwak_Detecting_the_Starting_Frame_of_Actions_in_Video_WACV_2020_paper | Detecting the Starting Frame of Actions in Video | [
"Iljung Kwak",
"Jian-Zhong Guo",
"Adam Hantman",
"David Kriegman",
"Kristin Branson"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kwak_Detecting_the_Starting_Frame_of_Actions_in_Video_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kwak_Detecting_the_Starting_Frame_of_Actions_in_Video_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Kwak_Detecting_the_Starting_WACV_2020_supplemental.pdf | 1906.03340 | title_snapshot | @InProceedings{Kwak_2020_WACV,author = {Kwak, Iljung and Guo, Jian-Zhong and Hantman, Adam and Kriegman, David and Branson, Kristin},title = {Detecting the Starting Frame of Actions in Video},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {20... | In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a crowd begins to applaud. Key frame localization is a largely overlooked and important action-recognition problem, for example in the field of... |
Das_Looking_deeper_into_Time_for_Activities_of_Daily_Living_Recognition_WACV_2020_paper | Looking deeper into Time for Activities of Daily Living Recognition | [
"Srijan Das",
"Monique Thonnat",
"Francois Bremond"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Das_Looking_deeper_into_Time_for_Activities_of_Daily_Living_Recognition_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Das_Looking_deeper_into_Time_for_Activities_of_Daily_Living_Recognition_WACV_2020_paper.pdf | null | null | null | @InProceedings{Das_2020_WACV,author = {Das, Srijan and Thonnat, Monique and Bremond, Francois},title = {Looking deeper into Time for Activities of Daily Living Recognition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | In this paper, we introduce a new approach for Activities of Daily Living (ADL) recognition. In order to discriminate between activities with similar appearance and motion, we focus on their temporal structure. Actions with subtle and similar motion are hard to disambiguate since long-range temporal information is har... |
Li_Weakly-Supervised_Multi-Person_Action_Recognition_in_360circ_Videos_WACV_2020_paper | Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos | [
"Junnan Li",
"Jianquan Liu",
"Wong Yongkang",
"Shoji Nishimura",
"Mohan Kankanhalli"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Li_Weakly-Supervised_Multi-Person_Action_Recognition_in_360circ_Videos_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Li_Weakly-Supervised_Multi-Person_Action_Recognition_in_360circ_Videos_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Li_Weakly-Supervised_Multi-Person_Action_WACV_2020_supplemental.pdf | 2002.03266 | title_snapshot | @InProceedings{Li_2020_WACV,author = {Li, Junnan and Liu, Jianquan and Yongkang, Wong and Nishimura, Shoji and Kankanhalli, Mohan},title = {Weakly-Supervised Multi-Person Action Recognition in 360$^{\circ}$ Videos},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},mon... | The recent development of commodity 360^ \circ cameras have enabled a single video to capture an entire scene, which endows promising potentials in surveillance scenarios. However, research in omnidirectional video analysis has lagged behind the hardware advances. In this work, we address the important problem of acti... |
Piergiovanni_Learning_Multimodal_Representations_for_Unseen_Activities_WACV_2020_paper | Learning Multimodal Representations for Unseen Activities | [
"AJ Piergiovanni",
"Michael Ryoo"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Piergiovanni_Learning_Multimodal_Representations_for_Unseen_Activities_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Piergiovanni_Learning_Multimodal_Representations_for_Unseen_Activities_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Piergiovanni_Learning_Multimodal_Representations_WACV_2020_supplemental.pdf | 1806.08251 | title_snapshot | @InProceedings{Piergiovanni_2020_WACV,author = {Piergiovanni, AJ and Ryoo, Michael},title = {Learning Multimodal Representations for Unseen Activities},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | We present a method to learn a joint multimodal representation space that enables recognition of unseen activities in videos. We first compare the effect of placing various constraints on the embedding space using paired text and video data. We also propose a method to improve the joint embedding space using an adversa... |
Ulutan_Actor_Conditioned_Attention_Maps_for_Video_Action_Detection_WACV_2020_paper | Actor Conditioned Attention Maps for Video Action Detection | [
"Oytun Ulutan",
"Swati Rallapalli",
"Mudhakar Srivatsa",
"Carlos Torres",
"B. S. Manjunath"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Ulutan_Actor_Conditioned_Attention_Maps_for_Video_Action_Detection_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Ulutan_Actor_Conditioned_Attention_Maps_for_Video_Action_Detection_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Ulutan_Actor_Conditioned_Attention_WACV_2020_supplemental.pdf | 1812.11631 | title_snapshot | @InProceedings{Ulutan_2020_WACV,author = {Ulutan, Oytun and Rallapalli, Swati and Srivatsa, Mudhakar and Torres, Carlos and Manjunath, B. S.},title = {Actor Conditioned Attention Maps for Video Action Detection},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month ... | While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context. The surrounding context provides essential information for understanding actions. To this end, we propose to replace region of interest(RoI) pooling with an attention module, which ranks each spa... |
Fernando_Weakly_Supervised_Gaussian_Networks_for_Action_Detection_WACV_2020_paper | Weakly Supervised Gaussian Networks for Action Detection | [
"Basura Fernando",
"Cheston Tan",
"Hakan Bilen"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Fernando_Weakly_Supervised_Gaussian_Networks_for_Action_Detection_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Fernando_Weakly_Supervised_Gaussian_Networks_for_Action_Detection_WACV_2020_paper.pdf | null | 1904.07774 | title_snapshot | @InProceedings{Fernando_2020_WACV,author = {Fernando, Basura and Tan, Cheston and Bilen, Hakan},title = {Weakly Supervised Gaussian Networks for Action Detection},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Detecting temporal extents of human actions in videos is a challenging computer vision problem that requires detailed manual supervision including frame-level labels. This expensive annotation process limits deploying action detectors to a limited number of categories. We propose a novel method, called WSGN, that learn... |
Islam_Weakly_Supervised_Temporal_Action_Localization_Using_Deep_Metric_Learning_WACV_2020_paper | Weakly Supervised Temporal Action Localization Using Deep Metric Learning | [
"Ashraful Islam",
"Richard Radke"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Islam_Weakly_Supervised_Temporal_Action_Localization_Using_Deep_Metric_Learning_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Islam_Weakly_Supervised_Temporal_Action_Localization_Using_Deep_Metric_Learning_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Islam_Weakly_Supervised_Temporal_WACV_2020_supplemental.pdf | 2001.07793 | title_snapshot | @InProceedings{Islam_2020_WACV,author = {Islam, Ashraful and Radke, Richard},title = {Weakly Supervised Temporal Action Localization Using Deep Metric Learning},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Temporal action localization is an important step towards video understanding. Most current action localization methods depend on untrimmed videos with full temporal annotations of action instances. However, it is expensive and time-consuming to annotate both action labels and temporal boundaries of videos. To this end... |
Asghari-Esfeden_Dynamic_Motion_Representation_for_Human_Action_Recognition_WACV_2020_paper | Dynamic Motion Representation for Human Action Recognition | [
"Sadjad Asghari-Esfeden",
"Mario Sznaier",
"Octavia Camps"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Asghari-Esfeden_Dynamic_Motion_Representation_for_Human_Action_Recognition_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Asghari-Esfeden_Dynamic_Motion_Representation_for_Human_Action_Recognition_WACV_2020_paper.pdf | null | null | null | @InProceedings{Asghari-Esfeden_2020_WACV,author = {Asghari-Esfeden, Sadjad and Sznaier, Mario and Camps, Octavia},title = {Dynamic Motion Representation for Human Action Recognition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Despite the advances in Human Activity Recognition, the ability to exploit the dynamics of human body motion in videos has yet to be achieved. In numerous recent works, researchers have used appearance and motion as independent inputs to infer the action that is taking place in a specific video. In this paper, we highl... |
Kae_Image_to_Video_Domain_Adaptation_Using_Web_Supervision_WACV_2020_paper | Image to Video Domain Adaptation Using Web Supervision | [
"Andrew Kae",
"Yale Song"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Kae_Image_to_Video_Domain_Adaptation_Using_Web_Supervision_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Kae_Image_to_Video_Domain_Adaptation_Using_Web_Supervision_WACV_2020_paper.pdf | null | 1908.01449 | title_snapshot | @InProceedings{Kae_2020_WACV,author = {Kae, Andrew and Song, Yale},title = {Image to Video Domain Adaptation Using Web Supervision},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Training deep neural networks typically requires large amounts of labeled data which may be scarce or expensive to obtain for a particular target domain. As an alternative, we can leverage webly-supervised data (i.e. results from a public search engine) which are relatively plentiful but may contain noisy results. In t... |
Ghosh_Stacked_Spatio-Temporal_Graph_Convolutional_Networks_for_Action_Segmentation_WACV_2020_paper | Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation | [
"Pallabi Ghosh",
"Yi Yao",
"Larry Davis",
"Ajay Divakaran"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Ghosh_Stacked_Spatio-Temporal_Graph_Convolutional_Networks_for_Action_Segmentation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Ghosh_Stacked_Spatio-Temporal_Graph_Convolutional_Networks_for_Action_Segmentation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Ghosh_Stacked_Spatio-Temporal_Graph_WACV_2020_supplemental.pdf | 1811.10575 | title_snapshot | @InProceedings{Ghosh_2020_WACV,author = {Ghosh, Pallabi and Yao, Yi and Davis, Larry and Divakaran, Ajay},title = {Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2... | We propose novel Stacked Spatio-Temporal Graph Convolutional Networks (Stacked-STGCN) for action segmentation, i.e., predicting and localizing a sequence of actions over long videos. We extend the Spatio-Temporal Graph Convolutional Network (STGCN) originally proposed for skeleton-based action recognition to enable nod... |
Li_Global_Co-occurrence_Feature_Learning_and_Active_Coordinate_System_Conversion_for_WACV_2020_paper | Global Co-occurrence Feature Learning and Active Coordinate System Conversion for Skeleton-based Action Recognition | [
"Sheng Li",
"Tingting Jiang",
"Tiejun Huang",
"Yonghong Tian"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Li_Global_Co-occurrence_Feature_Learning_and_Active_Coordinate_System_Conversion_for_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Li_Global_Co-occurrence_Feature_Learning_and_Active_Coordinate_System_Conversion_for_WACV_2020_paper.pdf | null | null | null | @InProceedings{Li_2020_WACV,author = {Li, Sheng and Jiang, Tingting and Huang, Tiejun and Tian, Yonghong},title = {Global Co-occurrence Feature Learning and Active Coordinate System Conversion for Skeleton-based Action Recognition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer V... | Skeleton-based action recognition has attracted more and more attention in recent years. Besides, the rapid development of deep learning has greatly improved the performance.However, the current exploration of action cooccurrence is still not comprehensive enough. Most existing works only mine co-occurrence features fr... |
Bo_Few-Shot_Learning_of_Video_Action_Recognition_Only_Based_on_Video_WACV_2020_paper | Few-Shot Learning of Video Action Recognition Only Based on Video Contents | [
"Yang Bo",
"Yangdi Lu",
"Wenbo He"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Bo_Few-Shot_Learning_of_Video_Action_Recognition_Only_Based_on_Video_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Bo_Few-Shot_Learning_of_Video_Action_Recognition_Only_Based_on_Video_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Bo_Few-Shot_Learning_of_WACV_2020_supplemental.pdf | null | null | @InProceedings{Bo_2020_WACV,author = {Bo, Yang and Lu, Yangdi and He, Wenbo},title = {Few-Shot Learning of Video Action Recognition Only Based on Video Contents},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | The success of video action recognition based on Deep Neural Networks (DNNs) is highly dependent on a large number of manually labeled videos. In this paper, we introduce a supervised learning approach to recognize video actions with very few training videos. Specifically, we propose Temporal Attention Vectors (TAVs) w... |
Chen_Action_Segmentation_with_Mixed_Temporal_Domain_Adaptation_WACV_2020_paper | Action Segmentation with Mixed Temporal Domain Adaptation | [
"Min-Hung Chen",
"Baopu Li",
"Yingze Bao",
"Ghassan AlRegib"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Chen_Action_Segmentation_with_Mixed_Temporal_Domain_Adaptation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Chen_Action_Segmentation_with_Mixed_Temporal_Domain_Adaptation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Chen_Action_Segmentation_with_WACV_2020_supplemental.pdf | 2104.07461 | title_snapshot | @InProceedings{Chen_2020_WACV,author = {Chen, Min-Hung and Li, Baopu and Bao, Yingze and AlRegib, Ghassan},title = {Action Segmentation with Mixed Temporal Domain Adaptation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | The main progress for action segmentation comes from densely-annotated data for fully-supervised learning. Since manual annotation for frame-level actions is timeconsuming and challenging, we propose to exploit auxiliary unlabeled videos, which are much easier to obtain, by shaping this problem as a domain adaptation (... |
Rashid_Action_Graphs_Weakly-supervised_Action_Localization_with_Graph_Convolution_Networks_WACV_2020_paper | Action Graphs: Weakly-supervised Action Localization with Graph Convolution Networks | [
"Maheen Rashid",
"Hedvig Kjellstrom",
"Yong Jae Lee"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Rashid_Action_Graphs_Weakly-supervised_Action_Localization_with_Graph_Convolution_Networks_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Rashid_Action_Graphs_Weakly-supervised_Action_Localization_with_Graph_Convolution_Networks_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Rashid_Action_Graphs_Weakly-supervised_WACV_2020_supplemental.pdf | 2002.01449 | title_snapshot | @InProceedings{Rashid_2020_WACV,author = {Rashid, Maheen and Kjellstrom, Hedvig and Lee, Yong Jae},title = {Action Graphs: Weakly-supervised Action Localization with Graph Convolution Networks},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {... | We present a method for weakly-supervised action localization based on graph convolutions. In order to find and classify video time segments that correspond to relevant action classes, a system must be able to both identify discriminative time segments in each video, and identify the full extent of each action. Achievi... |
Stroud_D3D_Distilled_3D_Networks_for_Video_Action_Recognition_WACV_2020_paper | D3D: Distilled 3D Networks for Video Action Recognition | [
"Jonathan Stroud",
"David Ross",
"Chen Sun",
"Jia Deng",
"Rahul Sukthankar"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Stroud_D3D_Distilled_3D_Networks_for_Video_Action_Recognition_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Stroud_D3D_Distilled_3D_Networks_for_Video_Action_Recognition_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Stroud_D3D_Distilled_3D_WACV_2020_supplemental.pdf | 1812.08249 | title_snapshot | @InProceedings{Stroud_2020_WACV,author = {Stroud, Jonathan and Ross, David and Sun, Chen and Deng, Jia and Sukthankar, Rahul},title = {D3D: Distilled 3D Networks for Video Action Recognition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {20... | State-of-the-art methods for action recognition commonly use two networks: the spatial stream, which takes RGB frames as input, and the temporal stream, which takes optical flow as input. In recent work, both streams are 3D Convolutional Neural Networks, which extract features using spatiotemporal filters. These filter... |
Cho_Self-Attention_Network_for_Skeleton-based_Human_Action_Recognition_WACV_2020_paper | Self-Attention Network for Skeleton-based Human Action Recognition | [
"Sangwoo Cho",
"Muhammad Maqbool",
"Fei Liu",
"Hassan Foroosh"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Cho_Self-Attention_Network_for_Skeleton-based_Human_Action_Recognition_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Cho_Self-Attention_Network_for_Skeleton-based_Human_Action_Recognition_WACV_2020_paper.pdf | null | 1912.08435 | title_snapshot | @InProceedings{Cho_2020_WACV,author = {Cho, Sangwoo and Maqbool, Muhammad and Liu, Fei and Foroosh, Hassan},title = {Self-Attention Network for Skeleton-based Human Action Recognition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Skeleton-based action recognition has recently attracted a lot of attention. Researchers are coming up with new approaches for extracting spatio-temporal relations and making considerable progress on large-scale skeleton based datasets. Most of the architectures being proposed are based upon recurrent neu... |
Huang_Long-Short_Graph_Memory_Network__for_Skeleton-based_Action_Recognition_WACV_2020_paper | Long-Short Graph Memory Network for Skeleton-based Action Recognition | [
"Junqin Huang",
"zhenhuan huang",
"Xiang Xiang",
"Xuan Gong",
"Baochang Zhang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Huang_Long-Short_Graph_Memory_Network__for_Skeleton-based_Action_Recognition_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Huang_Long-Short_Graph_Memory_Network__for_Skeleton-based_Action_Recognition_WACV_2020_paper.pdf | null | null | null | @InProceedings{Huang_2020_WACV,author = {Huang, Junqin and huang, zhenhuan and Xiang, Xiang and Gong, Xuan and Zhang, Baochang},title = {Long-Short Graph Memory Network for Skeleton-based Action Recognition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {M... | Current studies have shown the effectiveness of long short-term memory network (LSTM) for skeleton-based human action recognition in capturing temporal and spatial features of the skeleton sequence. Nevertheless, it still remains challenging for LSTM to extract the latent structural dependency among nodes. In this pape... |
Miki_Weakly_Supervised_Graph_Convolutional_Neural_Network_for_Human_Action_Localization_WACV_2020_paper | Weakly Supervised Graph Convolutional Neural Network for Human Action Localization | [
"Daisuke Miki",
"Shi Chen",
"Kazuyuki Demachi"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Miki_Weakly_Supervised_Graph_Convolutional_Neural_Network_for_Human_Action_Localization_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Miki_Weakly_Supervised_Graph_Convolutional_Neural_Network_for_Human_Action_Localization_WACV_2020_paper.pdf | null | null | null | @InProceedings{Miki_2020_WACV,author = {Miki, Daisuke and Chen, Shi and Demachi, Kazuyuki},title = {Weakly Supervised Graph Convolutional Neural Network for Human Action Localization},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Skeleton-based human action recognition from video sequences is currently an active topic of research. Conventionally, human action recognition is performed after conducting feature extraction on a given spatial-temporal representation of a human pose by using statistical methods or deep learning methods. The spatial a... |
LORRE_Temporal_Contrastive_Pretraining_for_Video_Action_Recognition_WACV_2020_paper | Temporal Contrastive Pretraining for Video Action Recognition | [
"Guillaume LORRE",
"Jaonary Rabarisoa",
"Astrid Orcesi",
"Samia Ainouz",
"Stephane Canu"
] | https://openaccess.thecvf.com/content_WACV_2020/html/LORRE_Temporal_Contrastive_Pretraining_for_Video_Action_Recognition_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/LORRE_Temporal_Contrastive_Pretraining_for_Video_Action_Recognition_WACV_2020_paper.pdf | null | null | null | @InProceedings{LORRE_2020_WACV,author = {LORRE, Guillaume and Rabarisoa, Jaonary and Orcesi, Astrid and Ainouz, Samia and Canu, Stephane},title = {Temporal Contrastive Pretraining for Video Action Recognition},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = ... | In this paper, we propose a self-supervised method for video representation learning based on Contrastive Predictive Coding (CPC) [27]. Previously, CPC has been used to learn representations for different signals (audio, text or image). It benefits from the use of an autoregressive modeling and contrastive estimation t... |
Shuang_Fine-Grained_Motion_Representation_For_Template-Free_Visual_Tracking_WACV_2020_paper | Fine-Grained Motion Representation For Template-Free Visual Tracking | [
"Kai Shuang",
"Yuheng Huang",
"Yue Sun",
"Zhun Cai",
"Hao Guo"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Shuang_Fine-Grained_Motion_Representation_For_Template-Free_Visual_Tracking_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Shuang_Fine-Grained_Motion_Representation_For_Template-Free_Visual_Tracking_WACV_2020_paper.pdf | null | null | null | @InProceedings{Shuang_2020_WACV,author = {Shuang, Kai and Huang, Yuheng and Sun, Yue and Cai, Zhun and Guo, Hao},title = {Fine-Grained Motion Representation For Template-Free Visual Tracking},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {20... | The object tracking task requires tracking the arbitrary target in consecutive video frames. Recently, several attempts have been made to develop the template-free models to attain generality. However, the current template-free paradigm only estimates the displacement to approximate the motion of the object. The displa... |
Song_Adaptive_Aggregation_of_Arbitrary_Online_Trackers_with_a_Regret_Bound_WACV_2020_paper | Adaptive Aggregation of Arbitrary Online Trackers with a Regret Bound | [
"Heon Song",
"Daiki Suehiro",
"Seiichi Uchida"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Song_Adaptive_Aggregation_of_Arbitrary_Online_Trackers_with_a_Regret_Bound_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Song_Adaptive_Aggregation_of_Arbitrary_Online_Trackers_with_a_Regret_Bound_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Song_Adaptive_Aggregation_of_WACV_2020_supplemental.pdf | 2009.09237 | title_judge | @InProceedings{Song_2020_WACV,author = {Song, Heon and Suehiro, Daiki and Uchida, Seiichi},title = {Adaptive Aggregation of Arbitrary Online Trackers with a Regret Bound},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | We propose an online visual-object tracking method that is robust even in an adversarial environment, where various disturbances may occur on the target appearance, etc. The proposed method is based on a delayed-Hedge algorithm for aggregating multiple arbitrary online trackers with adaptive weights. The robustness in ... |
Styles_Multiple_Object_Forecasting_Predicting_Future_Object_Locations_in_Diverse_Environments_WACV_2020_paper | Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments | [
"Oliver Styles",
"Victor Sanchez",
"Tanaya Guha"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Styles_Multiple_Object_Forecasting_Predicting_Future_Object_Locations_in_Diverse_Environments_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Styles_Multiple_Object_Forecasting_Predicting_Future_Object_Locations_in_Diverse_Environments_WACV_2020_paper.pdf | null | 1909.11944 | title_snapshot | @InProceedings{Styles_2020_WACV,author = {Styles, Oliver and Sanchez, Victor and Guha, Tanaya},title = {Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2... | This paper introduces the problem of multiple object forecasting (MOF), in which the goal is to predict future bounding boxes of tracked objects. In contrast to existing works on object trajectory forecasting which primarily consider the problem from a birds-eye perspective, we formulate the problem from an object-leve... |
Feng_Real-time_Visual_Object_Tracking_with_Natural_Language_Description_WACV_2020_paper | Real-time Visual Object Tracking with Natural Language Description | [
"Qi Feng",
"Vitaly Ablavsky",
"Qinxun Bai",
"Guorong Li",
"Stan Sclaroff"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Feng_Real-time_Visual_Object_Tracking_with_Natural_Language_Description_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Feng_Real-time_Visual_Object_Tracking_with_Natural_Language_Description_WACV_2020_paper.pdf | null | 1907.11751 | title_snapshot | @InProceedings{Feng_2020_WACV,author = {Feng, Qi and Ablavsky, Vitaly and Bai, Qinxun and Li, Guorong and Sclaroff, Stan},title = {Real-time Visual Object Tracking with Natural Language Description},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},yea... | In this work, we argue that conditioning on the natural language (NL) description of a target provides information for longer-term invariance, and thus helps cope with typical tracking challenges. However, deriving a formulation to combine the strengths of appearance-based tracking with the language modality is not str... |
Qiu_Inverse_Rectification_for_Efficient_Procam_Pattern_Correspondence_WACV_2020_paper | Inverse Rectification for Efficient Procam Pattern Correspondence | [
"Yubo Qiu",
"Jonathon Malcolm",
"Sheikh Ziauddin",
"Michael Greenspan",
"Abhay Vatoo"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Qiu_Inverse_Rectification_for_Efficient_Procam_Pattern_Correspondence_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Qiu_Inverse_Rectification_for_Efficient_Procam_Pattern_Correspondence_WACV_2020_paper.pdf | null | null | null | @InProceedings{Qiu_2020_WACV,author = {Qiu, Yubo and Malcolm, Jonathon and Ziauddin, Sheikh and Greenspan, Michael and Vatoo, Abhay},title = {Inverse Rectification for Efficient Procam Pattern Correspondence},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {... | A method called inverse rectification, is proposed which facilitates the establishment of correspondences across a projected pattern and an acquired image. A pattern of features comprising vertical dashes is warped by the inverse of the rectifying homography of the projector-camera pair, prior to projection. This warpi... |
Li_Graph_Networks_for_Multiple_Object_Tracking_WACV_2020_paper | Graph Networks for Multiple Object Tracking | [
"Jiahe Li",
"Xu Gao",
"Tingting Jiang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Li_Graph_Networks_for_Multiple_Object_Tracking_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Li_Graph_Networks_for_Multiple_Object_Tracking_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Li_Graph_Networks_for_WACV_2020_supplemental.pdf | null | null | @InProceedings{Li_2020_WACV,author = {Li, Jiahe and Gao, Xu and Jiang, Tingting},title = {Graph Networks for Multiple Object Tracking},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Multiple object tracking (MOT) task requires reasoning the states of all targets and associating these targets in a global way. However, existing MOT methods mostly focus on the local relationship among objects and ignore the global relationship. Some methods formulate the MOT problem as a graph optimization problem. H... |
Ma_Training_with_Noise_Adversarial_Network_A_Generalization_Method_for_Object_WACV_2020_paper | Training with Noise Adversarial Network: A Generalization Method for Object Detection on Sonar Image | [
"Qixiang Ma",
"Longyu Jiang",
"Wenxue Yu",
"Rui Jin",
"Zhixiang Wu",
"Fangjin Xu"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Ma_Training_with_Noise_Adversarial_Network_A_Generalization_Method_for_Object_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Ma_Training_with_Noise_Adversarial_Network_A_Generalization_Method_for_Object_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Ma_Training_with_Noise_WACV_2020_supplemental.pdf | null | null | @InProceedings{Ma_2020_WACV,author = {Ma, Qixiang and Jiang, Longyu and Yu, Wenxue and Jin, Rui and Wu, Zhixiang and Xu, Fangjin},title = {Training with Noise Adversarial Network: A Generalization Method for Object Detection on Sonar Image},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of C... | Object detection tasks for sonar image confront two major challenges, scarcity of dataset and perturbation of noise, which cause overfitting to models. The state-of-the-art object detection designed for optical images cannot address the issues because of the inherent differentiation between the optical image and sonar ... |
Su_Active_Adversarial_Domain_Adaptation_WACV_2020_paper | Active Adversarial Domain Adaptation | [
"Jong-Chyi Su",
"Yi-Hsuan Tsai",
"Kihyuk Sohn",
"Buyu Liu",
"Subhransu Maji",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Su_Active_Adversarial_Domain_Adaptation_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Su_Active_Adversarial_Domain_Adaptation_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Su_Active_Adversarial_Domain_WACV_2020_supplemental.pdf | 1904.07848 | title_snapshot | @InProceedings{Su_2020_WACV,author = {Su, Jong-Chyi and Tsai, Yi-Hsuan and Sohn, Kihyuk and Liu, Buyu and Maji, Subhransu and Chandraker, Manmohan},title = {Active Adversarial Domain Adaptation},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = ... | We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains. The former uses a domain discrimina... |
Hsu_Progressive_Domain_Adaptation_for_Object_Detection_WACV_2020_paper | Progressive Domain Adaptation for Object Detection | [
"Han-Kai Hsu",
"Chun-Han Yao",
"Yi-Hsuan Tsai",
"Wei-Chih Hung",
"Hung-Yu Tseng",
"Maneesh Singh",
"Ming-Hsuan Yang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Hsu_Progressive_Domain_Adaptation_for_Object_Detection_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Hsu_Progressive_Domain_Adaptation_for_Object_Detection_WACV_2020_paper.pdf | null | 1910.11319 | title_snapshot | @InProceedings{Hsu_2020_WACV,author = {Hsu, Han-Kai and Yao, Chun-Han and Tsai, Yi-Hsuan and Hung, Wei-Chih and Tseng, Hung-Yu and Singh, Maneesh and Yang, Ming-Hsuan},title = {Progressive Domain Adaptation for Object Detection},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Visi... | Recent deep learning methods for object detection rely on a large amount of bounding box annotations. Collecting these annotations is laborious and costly, yet supervised models do not generalize well when testing on images from a different distribution. Domain adaptation provides a solution by adapting existing labels... |
Taha_Boosting_Standard_Classification_Architectures_Through_a_Ranking_Regularizer_WACV_2020_paper | Boosting Standard Classification Architectures Through a Ranking Regularizer | [
"Ahmed Taha",
"Yi-Ting Chen",
"Teruhisa Misu",
"Abhinav Shrivastava",
"Larry Davis"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Taha_Boosting_Standard_Classification_Architectures_Through_a_Ranking_Regularizer_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Taha_Boosting_Standard_Classification_Architectures_Through_a_Ranking_Regularizer_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Taha_Boosting_Standard_Classification_WACV_2020_supplemental.pdf | 1901.08616 | title_snapshot | @InProceedings{Taha_2020_WACV,author = {Taha, Ahmed and Chen, Yi-Ting and Misu, Teruhisa and Shrivastava, Abhinav and Davis, Larry},title = {Boosting Standard Classification Architectures Through a Ranking Regularizer},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}... | We employ triplet loss as a feature embedding regularizer to boost classification performance. Standard architectures, like ResNet and Inception, are extended to support both losses with minimal hyper-parameter tuning. This promotes generality while fine-tuning pretrained networks. Triplet loss is a powerful surrogate ... |
Chen_Overlap_Sampler_for_Region-Based_Object_Detection_WACV_2020_paper | Overlap Sampler for Region-Based Object Detection | [
"Joya Chen",
"Bin Luo",
"Qi Wu",
"Jia Chen",
"Xuezheng Peng"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Chen_Overlap_Sampler_for_Region-Based_Object_Detection_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Chen_Overlap_Sampler_for_Region-Based_Object_Detection_WACV_2020_paper.pdf | null | null | null | @InProceedings{Chen_2020_WACV,author = {Chen, Joya and Luo, Bin and Wu, Qi and Chen, Jia and Peng, Xuezheng},title = {Overlap Sampler for Region-Based Object Detection},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | The top accuracy of object detection to date is led by region-based approaches, where the per-region stage is responsible for recognizing proposals generated by the region proposal network. In that stage, sampling heuristics (e.g., OHEM, IoU-balanced sampling) is always applied to select a part of examples during train... |
Ujjwal_A_one-and-half_stage_pedestrian_detector_WACV_2020_paper | A one-and-half stage pedestrian detector | [
"Ujjwal Ujjwal",
"Aziz Dziri",
"Bertrand Leroy",
"Francois Bremond"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Ujjwal_A_one-and-half_stage_pedestrian_detector_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Ujjwal_A_one-and-half_stage_pedestrian_detector_WACV_2020_paper.pdf | null | null | null | @InProceedings{Ujjwal_2020_WACV,author = {Ujjwal, Ujjwal and Dziri, Aziz and Leroy, Bertrand and Bremond, Francois},title = {A one-and-half stage pedestrian detector},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Pedestrian detection is a specific instance of the more general problem of object detection in computer vision. A balance between detection accuracy and speed is a desirable trait for pedestrian detection systems in many applications such as self-driving cars. In this paper, we follow the wisdom of " and ... |
Shen_Model-Agnostic_Metric_for_Zero-Shot_Learning_WACV_2020_paper | Model-Agnostic Metric for Zero-Shot Learning | [
"Jiayi Shen",
"Haochen Wang",
"Anran Zhang",
"Qiang Qiu",
"Xiantong Zhen",
"Xianbin Cao"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Shen_Model-Agnostic_Metric_for_Zero-Shot_Learning_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Shen_Model-Agnostic_Metric_for_Zero-Shot_Learning_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Shen_Model-Agnostic_Metric_for_WACV_2020_supplemental.pdf | null | null | @InProceedings{Shen_2020_WACV,author = {Shen, Jiayi and Wang, Haochen and Zhang, Anran and Qiu, Qiang and Zhen, Xiantong and Cao, Xianbin},title = {Model-Agnostic Metric for Zero-Shot Learning},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {... | Zero-shot Learning (ZSL) aims to learn a classifier to recognize unseen categories without training samples. Most ZSL works based on embedding models handle the visual space and the semantic space through a common metric space and then apply a simple nearest neighbor search which directly leads to the hubness problem, ... |
Gwilliam_Intelligent_Image_Collection_Building_the_Optimal_Dataset_WACV_2020_paper | Intelligent Image Collection: Building the Optimal Dataset | [
"Matthew Gwilliam",
"Ryan Farrell"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Gwilliam_Intelligent_Image_Collection_Building_the_Optimal_Dataset_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Gwilliam_Intelligent_Image_Collection_Building_the_Optimal_Dataset_WACV_2020_paper.pdf | null | null | null | @InProceedings{Gwilliam_2020_WACV,author = {Gwilliam, Matthew and Farrell, Ryan},title = {Intelligent Image Collection: Building the Optimal Dataset},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Key recognition tasks such as fine-grained visual categorization (FGVC) have benefited from increasing attention among computer vision researchers. The development and evaluation of new approaches relies heavily on benchmark datasets; such datasets are generally built primarily with categories that have images readily ... |
Lo_Internet_of_Things_IoT_Discovery_Using_Deep_Neural_Networks_WACV_2020_paper | Internet of Things (IoT) Discovery Using Deep Neural Networks | [
"Ephraim Lo",
"JoHannah Kohl"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Lo_Internet_of_Things_IoT_Discovery_Using_Deep_Neural_Networks_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Lo_Internet_of_Things_IoT_Discovery_Using_Deep_Neural_Networks_WACV_2020_paper.pdf | null | null | null | @InProceedings{Lo_2020_WACV,author = {Lo, Ephraim and Kohl, JoHannah},title = {Internet of Things (IoT) Discovery Using Deep Neural Networks},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | We present a novel approach to Internet of Things (IoT) discovery using Deep Neural Network (DNN) based object detection. Traditional methods of IoT discovery are based on either manual or automated monitoring of predetermined channel frequencies. Our method takes the spectrogram images that a human analyst visually sc... |
Jang_Propose-and-Attend_Single_Shot_Detector_WACV_2020_paper | Propose-and-Attend Single Shot Detector | [
"Ho-Deok Jang",
"Sanghyun Woo",
"Philipp Benz",
"Jinsun Park",
"In So Kweon"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Jang_Propose-and-Attend_Single_Shot_Detector_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Jang_Propose-and-Attend_Single_Shot_Detector_WACV_2020_paper.pdf | null | 1907.12736 | title_snapshot | @InProceedings{Jang_2020_WACV,author = {Jang, Ho-Deok and Woo, Sanghyun and Benz, Philipp and Park, Jinsun and Kweon, In So},title = {Propose-and-Attend Single Shot Detector},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | We present a simple yet effective prediction module for a one-stage detector. The main process is conducted in a coarse-to-fine manner. First, the module roughly adjusts the default boxes to well capture the extent of target objects in an image. Second, given the adjusted boxes, the module aligns the receptive field of... |
Lin_Local_Binary_Pattern_Networks_WACV_2020_paper | Local Binary Pattern Networks | [
"Jeng-Hau Lin",
"Justin Lazarow",
"Andrew Yang",
"Dezhi Hong",
"Rajesh Gupta",
"Zhuowen Tu"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Lin_Local_Binary_Pattern_Networks_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Lin_Local_Binary_Pattern_Networks_WACV_2020_paper.pdf | null | 1803.07125 | title_snapshot | @InProceedings{Lin_2020_WACV,author = {Lin, Jeng-Hau and Lazarow, Justin and Yang, Andrew and Hong, Dezhi and Gupta, Rajesh and Tu, Zhuowen},title = {Local Binary Pattern Networks},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | Emerging edge devices such as sensor nodes are increasingly being tasked with non-trivial tasks related to sensor data processing and even application-level inferences from this sensor data. These devices are, however, extraordinarily resource-constrained in terms of CPU power (often Cortex M0-3 class CPUs), available ... |
Singh_Leveraging_Filter_Correlations_for_Deep_Model_Compression_WACV_2020_paper | Leveraging Filter Correlations for Deep Model Compression | [
"Pravendra Singh",
"Vinay Kumar Verma",
"Piyush Rai",
"Vinay Namboodiri"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Singh_Leveraging_Filter_Correlations_for_Deep_Model_Compression_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Singh_Leveraging_Filter_Correlations_for_Deep_Model_Compression_WACV_2020_paper.pdf | null | 1811.10559 | title_snapshot | @InProceedings{Singh_2020_WACV,author = {Singh, Pravendra and Verma, Vinay Kumar and Rai, Piyush and Namboodiri, Vinay},title = {Leveraging Filter Correlations for Deep Model Compression},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},month = {March},year = {2020}} | We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise correlations and drops one of the filters from each such pair. However, instead of discarding one of the filters from each such pair nai... |
Chou_360-Indoor_Towards_Learning_Real-World_Objects_in_360deg_Indoor_Equirectangular_Images_WACV_2020_paper | 360-Indoor: Towards Learning Real-World Objects in 360deg Indoor Equirectangular Images | [
"Shih-Han Chou",
"Cheng Sun",
"Wen-Yen Chang",
"Wan-Ting Hsu",
"Min Sun",
"Jianlong Fu"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Chou_360-Indoor_Towards_Learning_Real-World_Objects_in_360deg_Indoor_Equirectangular_Images_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Chou_360-Indoor_Towards_Learning_Real-World_Objects_in_360deg_Indoor_Equirectangular_Images_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Chou_360-Indoor_Towards_Learning_WACV_2020_supplemental.pdf | 1910.01712 | title_judge | @InProceedings{Chou_2020_WACV,author = {Chou, Shih-Han and Sun, Cheng and Chang, Wen-Yen and Hsu, Wan-Ting and Sun, Min and Fu, Jianlong},title = {360-Indoor: Towards Learning Real-World Objects in 360deg Indoor Equirectangular Images},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Comput... | While there are several widely used object detection datasets, current computer vision algorithms are still limited in conventional images. Such images narrow our vision in a restricted region. On the other hand, 360deg images provide a thorough sight. In this paper, our goal is to provide a standard dataset to facilit... |
Zhang_Regularize_Expand_and_Compress_NonExpansive_Continual_Learning_WACV_2020_paper | Regularize, Expand and Compress: NonExpansive Continual Learning | [
"Jie Zhang",
"Junting Zhang",
"Shalini Ghosh",
"Dawei Li",
"Jingwen Zhu",
"Heming Zhang",
"Yalin Wang"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Zhang_Regularize_Expand_and_Compress_NonExpansive_Continual_Learning_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Zhang_Regularize_Expand_and_Compress_NonExpansive_Continual_Learning_WACV_2020_paper.pdf | null | 1903.08362 | title_judge | @InProceedings{Zhang_2020_WACV,author = {Zhang, Jie and Zhang, Junting and Ghosh, Shalini and Li, Dawei and Zhu, Jingwen and Zhang, Heming and Wang, Yalin},title = {Regularize, Expand and Compress: NonExpansive Continual Learning},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vi... | Continual learning (CL), the problem of lifelong learning where tasks arrive in sequence, has attracted increasing attention in the computer vision community lately. The goal of CL is to learn new tasks while maintaining the performance on the previously learned tasks. There are two major obstacles for CL of deep neura... |
Beery_Synthetic_Examples_Improve_Generalization_for_Rare_Classes_WACV_2020_paper | Synthetic Examples Improve Generalization for Rare Classes | [
"Sara Beery",
"Yang Liu",
"Dan Morris",
"Jim Piavis",
"Ashish Kapoor",
"Neel Joshi",
"Markus Meister",
"Pietro Perona"
] | https://openaccess.thecvf.com/content_WACV_2020/html/Beery_Synthetic_Examples_Improve_Generalization_for_Rare_Classes_WACV_2020_paper.html | https://openaccess.thecvf.com/content_WACV_2020/papers/Beery_Synthetic_Examples_Improve_Generalization_for_Rare_Classes_WACV_2020_paper.pdf | https://openaccess.thecvf.com/content_WACV_2020/supplemental/Beery_Synthetic_Examples_Improve_WACV_2020_supplemental.pdf | 1904.05916 | title_snapshot | @InProceedings{Beery_2020_WACV,author = {Beery, Sara and Liu, Yang and Morris, Dan and Piavis, Jim and Kapoor, Ashish and Joshi, Neel and Meister, Markus and Perona, Pietro},title = {Synthetic Examples Improve Generalization for Rare Classes},booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of... | The ability to detect and classify rare occurrences in images has important applications -- for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars. Few-shot learning is an open problem: current computer vision syste... |
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