mtybilly/Instruct2DS huggingface dataset:
Explore Spatio-Temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline
Authors: Kailai Zhou, Yibo Wang, Tao Lv, Yunqian Li, Linsen Chen, Qiu Shen, Xun Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We endeavor on a rarely explored task named Insubstan-tial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indistinct boundary; (2) similarity to surroundings; (3) absence in color. Accordingly, it is far more challenging to distinguish insubstantial objects in a single static frame and the collaborative representation of spatial and tempo-ral information is crucial. Thus, we construct an IOD-Video dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges. In addition, we develop a spatio-temporal aggregation framework for IOD, in which differ-ent backbones are deployed and a spatio-temporal aggregation loss (STAloss) is elaborately designed to leverage the consistency along the time axis. Experiments conducted on IOD-Video dataset demonstrate that spatio-temporal aggregation can significantly improve the performance of IOD. We hope our work will attract further researches into this valuable yet challenging task. The code will be available at: https://github.com/CalayZhou/IOD-Video.
OCSampler: Compressing Videos to One Clip With Single-Step Sampling
Authors: Jintao Lin, Haodong Duan, Kai Chen, Dahua Lin, Limin Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Videos incorporate rich semantics as well as redundant information. Seeking a compact yet effective video representation, e.g., sample informative frames from the entire video, is critical to efficient video recognition. There have been works that formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance. In this paper, we present a more efficient framework named OCSampler, which explores such a representation with one short clip. OCSampler designs a new paradigm of learning instance-specific video condensation policies to select frames only in a single step. Rather than picking up frames sequentially like previous methods, we simply process a whole sequence at once. Accordingly, these policies are derived from a light-weighted skim network together with a simple yet effective policy network. Moreover, we extend the proposed method with a frame number budget, enabling the framework to produce correct predictions in high confidence with as few frames as possible. Experiments on various benchmarks demonstrate the effectiveness of OCSampler over previous methods in terms of accuracy and efficiency. Specifically, it achieves 76.9% mAP and 21.7 GFLOPs on ActivityNet with an impressive throughput: 123.9 Video/s on a single TITAN Xp GPU.
Learning Bayesian Sparse Networks With Full Experience Replay for Continual Learning
Authors: Qingsen Yan, Dong Gong, Yuhang Liu, Anton van den Hengel, Javen Qinfeng Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. Despite their performance, they still suffer from interference across tasks which leads to catastrophic forgetting. To ameliorate this problem, we propose to only activate and select sparse neurons for learning current and past tasks at any stage. More parameters space and model capacity can thus be reserved for the future tasks. This minimizes the interference between parameters for different tasks. To do so, we propose a Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers. Full Experience Replay (FER) provides effective supervision in learning the sparse activations of the neurons in different layers. A loss-aware reservoir-sampling strategy is developed to maintain the memory buffer. The proposed method is agnostic as to the network structures and the task boundaries. Experiments on different datasets show that SNCL achieves state-of-the-art result for mitigating forgetting.
Graph-Based Spatial Transformer With Memory Replay for Multi-Future Pedestrian Trajectory Prediction
Authors: Lihuan Li, Maurice Pagnucco, Yang Song
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning. Besides generating a single future path, predicting multiple plausible future paths is becoming popular in some recent work on trajectory prediction. However, existing methods typically emphasize spatial interactions between pedestrians and surrounding areas but ignore the smoothness and temporal consistency of predictions. Our model aims to forecast multiple paths based on a historical trajectory by modeling multi-scale graph-based spatial transformers combined with a trajectory smoothing algorithm named "Memory Replay" utilizing a memory graph. Our method can comprehensively exploit the spatial information as well as correct the temporally inconsistent trajectories (e.g., sharp turns). We also propose a new evaluation metric named "Percentage of Trajectory Usage" to evaluate the comprehensiveness of diverse multi-future predictions. Our extensive experiments show that the proposed model achieves state-of-the-art performance on multi-future prediction and competitive results for single-future prediction. Code released at https://github.com/Jacobieee/ST-MR.
Scanline Homographies for Rolling-Shutter Plane Absolute Pose
Authors: Fang Bai, Agniva Sengupta, Adrien Bartoli
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Cameras on portable devices are manufactured with a rolling-shutter (RS) mechanism, where the image rows (aka. scanlines) are read out sequentially. The unknown camera motions during the imaging process cause the so-called RS effects which are solved by motion assumptions in the literature. In this work, we give a solution to the absolute pose problem free of motion assumptions. We categorically demonstrate that the only requirement is motion smoothness instead of stronger constraints on the camera motion. To this end, we propose a novel mathematical abstraction for RS cameras observing a planar scene, called the scanline-homography, a 3x2 matrix with 5 DOFs. We establish the relationship between a scanline-homography and the corresponding plane-homography, a 3x3 matrix with 6 DOFs assuming the camera is calibrated. We estimate the scanline-homographies of an RS frame using a smooth image warp powered by B-Splines, and recover the plane-homographies afterwards to obtain the scanline-poses based on motion smoothness. We back our claims with various experiments. Code and new datasets: https://bitbucket.org/clermontferrand/planarscanlinehomography/src/master/.
TableFormer: Table Structure Understanding With Transformers
Authors: Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a non-trivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-to-end deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
Exemplar-Based Pattern Synthesis With Implicit Periodic Field Network
Authors: Haiwei Chen, Jiayi Liu, Weikai Chen, Shichen Liu, Yajie Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Synthesis of ergodic, stationary visual patterns is widely applicable in texturing, shape modeling, and digital content creation. The wide applicability of this technique thus requires the pattern synthesis approaches to be scalable, diverse, and authentic. In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements. To this end, we propose an implicit network based on generative adversarial network (GAN) and periodic encoding, thus calling our network the Implicit Periodic Field Network (IPFN). The design of IPFN ensures scalability: the implicit formulation directly maps the input coordinates to features, which enables synthesis of arbitrary size and is computationally efficient for 3D shape synthesis. Learning with a periodic encoding scheme encourages diversity: the network is constrained to model the inner statistics of the exemplar based on spatial latent codes in a periodic field. Coupled with continuously designed GAN training procedures, IPFN is shown to synthesize tileable patterns with smooth transitions and local variations. Last but not least, thanks to both the adversarial training technique and the encoded Fourier features, IPFN learns high-frequency functions that produce authentic, high-quality results. To validate our approach, we present novel experimental results on various applications in 2D texture synthesis and 3D shape synthesis.
Grounded Language-Image Pre-Training
Authors: Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, Jianfeng Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representations semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head.
Spectral Unsupervised Domain Adaptation for Visual Recognition
Authors: Jingyi Zhang, Jiaxing Huang, Zichen Tian, Shijian Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Though unsupervised domain adaptation (UDA) has achieved very impressive progress recently, it remains a great challenge due to missing target annotations and the rich discrepancy between source and target distributions. We propose Spectral UDA (SUDA), an effective and efficient UDA technique that works in the spectral space and can generalize across different visual recognition tasks. SUDA addresses the UDA challenges from two perspectives. First, it introduces a spectrum transformer (ST) that mitigates inter-domain discrepancies by enhancing domain-invariant spectra while suppressing domain-variant spectra of source and target samples simultaneously. Second, it introduces multi-view spectral learning that learns useful unsupervised representations by maximizing mutual information among multiple ST-generated spectral views of each target sample. Extensive experiments show that SUDA achieves superior accuracy consistently across different visual tasks in image classification, semantic segmentation, and object detection. Additionally, SUDA also works with the transformer-based network and achieves state-of-the-art performance on object detection.
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-Time Image Enhancement
Authors: Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, Ying Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it into a discretized 3D lattice. Previous works have made efforts to learn image-adaptive output color values of LUTs for flexible enhancement but neglect the importance of sampling strategy. They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform. Focusing on this problem, we present AdaInt (Adaptive Intervals Learning), a novel mechanism to achieve a more flexible sampling point allocation by adaptively learning the non-uniform sampling intervals in the 3D color space. In this way, a 3D LUT can increase its capability by conducting dense sampling in color ranges requiring highly non-linear transforms and sparse sampling for near-linear transforms. The proposed AdaInt could be implemented as a compact and efficient plug-and-play module for a 3D LUT-based method. To enable the end-to-end learning of AdaInt, we design a novel differentiable operator called AiLUT-Transform (Adaptive Interval LUT Transform) to locate input colors in the non-uniform 3D LUT and provide gradients to the sampling intervals. Experiments demonstrate that methods equipped with AdaInt can achieve state-of-the-art performance on two public benchmark datasets with a negligible overhead increase. Our source code is available at https://github.com/ImCharlesY/AdaInt.
PatchFormer: An Efficient Point Transformer With Patch Attention
Authors: Cheng Zhang, Haocheng Wan, Xinyi Shen, Zizhao Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The point cloud learning community is witnesses a modeling shift from CNNs to Transformers, where pure Transformer architectures have achieved top accuracy on the major learning benchmarks. However, existing point Transformers are computationally expensive since they need to generate a large attention map, which has quadratic complexity (both in space and time) with respect to input size. To solve this shortcoming, we introduce patch-attention (PAT) to adaptively learn a much smaller set of bases upon which the attention maps are computed. By a weighted summation upon these bases, PAT not only captures the global shape context but also achieves linear complexity to input size. In addition, we propose a lightweight Multi-Scale Attention (MST) block to build attentions among features of different scales, providing the model with multi-scale features. Equipped with the PAT and MST, we construct our neural architecture called PatchFormer that integrates both modules into a joint framework for point cloud learning. Extensive experiments demonstrate that our network achieves comparable accuracy on general point cloud learning tasks with 9.2x speed-up than previous point Transformers.
Recurrent Glimpse-Based Decoder for Detection With Transformer
Authors: Zhe Chen, Jing Zhang, Dacheng Tao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Although detection with Transformer (DETR) is increasingly popular, its global attention modeling requires an extremely long training period to optimize and achieve promising detection performance. Alternative to existing studies that mainly develop advanced feature or embedding designs to tackle the training issue, we point out that the Region-of-Interest (RoI) based detection refinement can easily help mitigate the difficulty of training for DETR methods. Based on this, we introduce a novel REcurrent Glimpse-based decOder (REGO) in this paper. In particular, the REGO employs a multi-stage recurrent processing structure to help the attention of DETR gradually focus on foreground objects more accurately. In each processing stage, visual features are extracted as glimpse features from RoIs with enlarged bounding box areas of detection results from the previous stage. Then, a glimpse-based decoder is introduced to provide refined detection results based on both the glimpse features and the attention modeling outputs of the previous stage. In practice, REGO can be easily embedded in representative DETR variants while maintaining their fully end-to-end training and inference pipelines. In particular, REGO helps Deformable DETR achieve 44.8 AP on the MSCOCO dataset with only 36 training epochs, compared with the first DETR and the Deformable DETR that require 500 and 50 epochs to achieve comparable performance, respectively. Experiments also show that REGO consistently boosts the performance of different DETR detectors by up to 7% relative gain at the same setting of 50 training epochs. Code is available via https://github.com/zhechen/Deformable-DETR-REGO.
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction To Treat Diabetic Foot Ulcers
Authors: Han Joo Chae, Seunghwan Lee, Hyewon Son, Seungyeob Han, Taebin Lim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers during the surgery to treat diabetic foot ulcers (DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D image capturing, semantic segmentation, boundary-guided point-cloud processing, 3D model reconstruction, and 3D printable G-code generation, into a single system that can be used out of the box. We developed a multi-stage data preprocessing method to handle small and unbalanced DFU image datasets. AiD Regen's human-in-the-loop machine learning interface enables clinicians to not only create 3D regenerative patches with just a few touch interactions but also customize and confirm wound boundaries. As evidenced by our experiments, our model outperforms prior wound segmentation models and our reconstruction algorithm is capable of generating 3D wound models with compelling accuracy. We further conducted a case study on a real DFU patient and demonstrated the effectiveness of AiD Regen in treating DFU wounds.
SimMIM: A Simple Framework for Masked Image Modeling
Authors: Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, Han Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: This paper presents SimMIM, a simple framework for masked image modeling. We have simplified recently proposed relevant approaches, without the need for special designs, such as block-wise masking and tokenization via discrete VAE or clustering. To investigate what makes a masked image modeling task learn good representations, we systematically study the major components in our framework, and find that the simple designs of each component have revealed very strong representation learning performance: 1) random masking of the input image with a moderately large masked patch size (e.g., 32) makes a powerful pre-text task; 2) predicting RGB values of raw pixels by direct regression performs no worse than the patch classification approaches with complex designs; 3) the prediction head can be as light as a linear layer, with no worse performance than heavier ones. Using ViT-B, our approach achieves 83.8% top-1 fine-tuning accuracy on ImageNet-1K by pre-training also on this dataset, surpassing previous best approach by +0.6%. When applied to a larger model with about 650 million parameters, SwinV2-H, it achieves 87.1% top-1 accuracy on ImageNet-1K using only ImageNet-1K data. We also leverage this approach to address the data-hungry issue faced by large-scale model training, that a 3B model (SwinV2-G) is successfully trained to achieve state-of-the-art accuracy on four representative vision benchmarks using 40x less labeled data than that in previous practice (JFT-3B). The code is available at https://github.com/microsoft/SimMIM.
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion
Authors: Yuyan Li, Yuliang Guo, Zhixin Yan, Xinyu Huang, Ye Duan, Liu Ren
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: A well-known challenge in applying deep-learning methods to omnidirectional images is spherical distortion. In dense regression tasks such as depth estimation, where structural details are required, using a vanilla CNN layer on the distorted 360 image results in undesired information loss. In this paper, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue. Our pipeline transforms a 360 image into less-distorted perspective patches (i.e. tangent images) to obtain patch-wise predictions via CNN, and then merge the patch-wise results for final output. To handle the discrepancy between patch-wise predictions which is a major issue affecting the merging quality, we propose a new framework with the following key components. First, we propose a geometry-aware feature fusion mechanism that combines 3D geometric features with 2D image features to compensate for the patch-wise discrepancy. Second, we employ the self-attention-based transformer architecture to conduct a global aggregation of patch-wise information, which further improves the consistency. Last, we introduce an iterative depth refinement mechanism, to further refine the estimated depth based on the more accurate geometric features. Experiments show that our method greatly mitigates the distortion issue, and achieves state-of-the-art performances on several 360 monocular depth estimation benchmark datasets.
Label Matching Semi-Supervised Object Detection
Authors: Binbin Chen, Weijie Chen, Shicai Yang, Yunyi Xuan, Jie Song, Di Xie, Shiliang Pu, Mingli Song, Yueting Zhuang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training. Despite the promising results, the label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training. In this paper, we delve into this problem and propose a simple yet effective LabelMatch framework from two different yet complementary perspectives, i.e., distribution-level and instance-level. For the former one, it is reasonable to approximate the class distribution of the unlabeled data from that of the labeled data according to Monte Carlo Sampling. Guided by this weakly supervision cue, we introduce a re-distribution mean teacher, which leverages adaptive label-distribution-aware confidence thresholds to generate unbiased pseudo labels to drive student learning. For the latter one, there exists an overlooked label assignment ambiguity problem across teacher-student models. To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly. Experiments on both MS-COCO and PASCAL-VOC datasets demonstrate the considerable superiority of our proposed framework to other state-of-the-arts. Code will be available at https://github.com/HIK-LAB/SSOD.
RegionCLIP: Region-Based Language-Image Pretraining
Authors: Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, Jianfeng Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to unsatisfactory performance due to a major domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans. To mitigate this issue, we propose a new method called RegionCLIP that significantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts. Our method leverages a CLIP model to match image regions with template captions, and then pretrains our model to align these region-text pairs in the feature space. When transferring our pretrained model to the open-vocabulary object detection task, our method outperforms the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and LVIS datasets, respectively. Further, the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets. Our code is available at https://github.com/microsoft/RegionCLIP.
Video Frame Interpolation Transformer
Authors: Zhihao Shi, Xiangyu Xu, Xiaohong Liu, Jun Chen, Ming-Hsuan Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field. To address these issues, we propose a Transformer-based video interpolation framework that allows content-aware aggregation weights and considers long-range dependencies with the self-attention operations. To avoid the high computational cost of global self-attention, we introduce the concept of local attention into video interpolation and extend it to the spatial-temporal domain. Furthermore, we propose a space-time separation strategy to save memory usage, which also improves performance. In addition, we develop a multi-scale frame synthesis scheme to fully realize the potential of Transformers. Extensive experiments demonstrate the proposed model performs favorably against the state-of-the-art methods both quantitatively and qualitatively on a variety of benchmark datasets. The code and models are released at https://github.com/zhshi0816/ Video-Frame-Interpolation-Transformer.
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation
Authors: Cheng-Kun Yang, Ji-Jia Wu, Kai-Syun Chen, Yung-Yu Chuang, Yen-Yu Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We address weakly supervised point cloud segmentation by proposing a new model, MIL-derived transformer, to mine additional supervisory signals. First, the transformer model is derived based on multiple instance learning (MIL) to explore pair-wise cloud-level supervision, where two clouds of the same category yield a positive bag while two of different classes produce a negative bag. It leverages not only individual cloud annotations but also pair-wise cloud semantics for model optimization. Second, Adaptive global weighted pooling (AdaGWP) is integrated into our transformer model to replace max pooling and average pooling. It introduces learnable weights to re-scale logits in the class activation maps. It is more robust to noise while discovering more complete foreground points under weak supervision. Third, we perform point subsampling and enforce feature equivariance between the original and subsampled point clouds for regularization. The proposed method is end-to-end trainable and is general because it can work with different backbones with diverse types of weak supervision signals, including sparsely annotated points and cloud-level labels. The experiments show that it achieves state-of-the-art performance on the S3DIS and ScanNet benchmarks.
Fast Light-Weight Near-Field Photometric Stereo
Authors: Daniel Lichy, Soumyadip Sengupta, David W. Jacobs
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We introduce the first end-to-end learning-based solution to near-field Photometric Stereo (PS), where the light sources are close to the object of interest. This setup is especially useful for reconstructing large immobile objects. Our method is fast, producing a mesh from 52 512x384 resolution images in about 1 second on a commodity GPU, thus potentially unlocking several AR/VR applications. Existing approaches rely on optimization coupled with a far-field PS network operating on pixels or small patches. Using optimization makes these approaches slow and memory intensive (requiring 17GB GPU and 27GB of CPU memory) while using only pixels or patches makes them highly susceptible to noise and calibration errors. To address these issues, we develop a recursive multi-resolution scheme to estimate surface normal and depth maps of the whole image at each step. The predicted depth map at each scale is then used to estimate 'per-pixel lighting' for the next scale. This design makes our approach almost 45x faster and 2 degrees more accurate (11.3 vs. 13.3 degrees Mean Angular Error) than the state-of-the-art near-field PS reconstruction technique, which uses iterative optimization.
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark
Authors: Jiachen Li, Bin Wang, Shiqiang Zhu, Xin Cao, Fan Zhong, Wenxuan Chen, Te Li, Jason Gu, Xueying Qin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use binocular data to construct a new benchmark for monocular textureless 3D object tracking. The proposed method requires no markers, and the cameras only need to be synchronous, relatively fixed as cross-view and calibrated. Based on our object-centered model, we jointly optimize the object pose by minimizing shape re-projection constraints in all views, which greatly improves the accuracy compared with the single-view approach, and is even more accurate than the depth-based method. Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes. The annotation error is guaranteed to be less than 2mm, according to both theoretical analysis and validation experiments. We re-evaluate the state-of-the-art 3D object tracking methods with our dataset, reporting their performance ranking in real scenes. Our BCOT benchmark and code can be found at https://ar3dv.github.io/BCOT-Benchmark/.
Omni-DETR: Omni-Supervised Object Detection With Transformers
Authors: Pei Wang, Zhaowei Cai, Hao Yang, Gurumurthy Swaminathan, Nuno Vasconcelos, Bernt Schiele, Stefano Soatto
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for object detection. This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection. Under this unified architecture, different types of weak labels can be leveraged to generate accurate pseudo labels, by a bipartite matching based filtering mechanism, for the model to learn. In the experiments, Omni-DETR has achieved state-of-the-art results on multiple datasets and settings. And we have found that weak annotations can help to improve detection performance and a mixture of them can achieve a better trade-off between annotation cost and accuracy than the standard complete annotation. These findings could encourage larger object detection datasets with mixture annotations. The code is available at https://github.com/amazon-research/omni-detr.
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching
Authors: Donghun Kang, Hyeonjoong Jang, Jungeon Lee, Chong-Min Kyung, Min H. Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Omnidirectional cameras have been used widely to better understand surrounding environments. They are often configured as stereo to estimate depth. However, due to the optics of the fisheye lens, conventional epipolar geometry is inapplicable directly to omnidirectional camera images. Intermediate formats of omnidirectional images, such as equirectangular images, have been used. However, stereo matching performance on these image formats has been lower than the conventional stereo due to severe image distortion near pole regions. In this paper, to address the distortion problem of omnidirectional images, we devise a novel subdivision scheme of a spherical geodesic grid. This enables more isotropic patch sampling of spherical image information in the omnidirectional camera space. Our spherical geodesic grid is tessellated with an equal-arc subdivision, making the cell sizes and in-between distances as uniform as possible, i.e., the arc length of the spherical grid cell's edges is well regularized. Also, our uniformly tessellated coordinates in a 2D image can be transformed into spherical coordinates via one-to-one mapping, allowing for analytical forward/backward transformation. Our uniform tessellation scheme achieves a higher accuracy of stereo matching than the traditional cylindrical and cubemap-based approaches, reducing the memory footage required for stereo matching by 20 %.
High-Resolution Image Synthesis With Latent Diffusion Models
Authors: Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve new state of the art scores for image inpainting and class-conditional image synthesis and highly competitive performance on various tasks, including unconditional image generation, text-to-image synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations
Authors: Junhao Dong, Yuan Wang, Jian-Huang Lai, Xiaohua Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Few-Shot Image Classification (FSIC) aims to recognize novel image classes with limited data, which is significant in practice. In this paper, we consider the FSIC problem in the case of adversarial examples. This is an extremely challenging issue because current deep learning methods are still vulnerable when handling adversarial examples, even with massive labeled training samples. For this problem, existing works focus on training a network in the meta-learning fashion that depends on numerous sampled few-shot tasks. In comparison, we propose a simple but effective baseline through directly learning generalizable representations without tedious task sampling, which is robust to unforeseen adversarial FSIC tasks. Specifically, we introduce an adversarial-aware mechanism to establish auxiliary supervision via feature-level differences between legitimate and adversarial examples. Furthermore, we design a novel adversarial-reweighted training manner to alleviate the imbalance among adversarial examples. The feature purifier is also employed as post-processing for adversarial features. Moreover, our method can obtain generalizable representations to remain superior transferability, even facing cross-domain adversarial examples. Extensive experiments show that our method can significantly outperform state-of-the-art adversarially robust FSIC methods on two standard benchmarks.
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
Authors: Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, Shang Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a classification model. However, CAM-based methods usually focus on the most discriminative parts of an object (i.e., incomplete localization problem). In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background. To address it, we propose Class RE-Activation Mapping (CREAM), a novel clustering-based approach to boost the activation values of the integral object regions. To this end, we introduce class-specific foreground and background context embeddings as cluster centroids. A CAM-guided momentum preservation strategy is developed to learn the context embeddings during training. At the inference stage, the re-activation mapping is formulated as a parameter estimation problem under Gaussian Mixture Model, which can be solved by deriving an unsupervised Expectation-Maximization based soft-clustering algorithm. By simply integrating CREAM into various WSOL approaches, our method significantly improves their performance. CREAM achieves the state-of-the-art performance on CUB, ILSVRC and OpenImages benchmark datasets. Code is available at https://github.com/JLRepo/CREAM.
Semi-Weakly-Supervised Learning of Complex Actions From Instructional Task Videos
Authors: Yuhan Shen, Ehsan Elhamifar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We address the problem of action segmentation in instructional task videos with a small number of weakly-labeled training videos and a large number of unlabeled videos, which we refer to as Semi-Weakly-Supervised Learning (SWSL) of actions. We propose a general SWSL framework that can efficiently learn from both types of videos and can leverage any of the existing weakly-supervised action segmentation methods. Our key observation is that the distance between the transcript of an unlabeled video and those of the weakly-labeled videos from the same task is small yet often nonzero. Therefore, we develop a Soft Restricted Edit (SRE) loss to encourage small variations between the predicted transcripts of unlabeled videos and ground-truth transcripts of the weakly-labeled videos of the same task. To compute the SRE loss, we develop a flexible transcript prediction (FTP) method that uses the output of the action classifier to find both the length of the transcript and the sequence of actions occurring in an unlabeled video. We propose an efficient learning scheme in which we alternate between minimizing our proposed loss and generating pseudo-transcripts for unlabeled videos. By experiments on two benchmark datasets, we demonstrate that our approach can significantly improve the performance by using unlabeled videos, especially when the number of weakly-labeled videos is small.
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Authors: Jiahao Lu, Xi Sheryl Zhang, Tianli Zhao, Xiangyu He, Jian Cheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Federated learning frameworks typically require collaborators to share their local gradient updates of a common model instead of sharing training data to preserve privacy. However, prior works on Gradient Leakage Attacks showed that private training data can be revealed from gradients. So far almost all relevant works base their attacks on fully-connected or convolutional neural networks. Given the recent overwhelmingly rising trend of adapting Transformers to solve multifarious vision tasks, it is highly important to investigate the privacy risk of vision transformers. In this paper, we analyse the gradient leakage risk of self-attention based mechanism in both theoretical and practical manners. Particularly, we propose APRIL - Attention PRIvacy Leakage, which poses a strong threat to self-attention inspired models such as ViT. Showing how vision Transformers are at the risk of privacy leakage via gradients, we urge the significance of designing privacy-safer Transformer models and defending schemes.
Text Spotting Transformers
Authors: Xiang Zhang, Yongwen Su, Subarna Tripathi, Zhuowen Tu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild. TESTR builds upon a single encoder and dual decoders for the joint text-box control point regression and character recognition. Other than most existing literature, our method is free from Region-of-Interest operations and heuristics-driven post-processing procedures; TESTR is particularly effective when dealing with curved text-boxes where special cares are needed for the adaptation of the traditional bounding-box representations. We show our canonical representation of control points suitable for text instances in both Bezier curve and polygon annotations. In addition, we design a bounding-box guided polygon detection (box-to-polygon) process. Experiments on curved and arbitrarily shaped datasets demonstrate state-of-the-art performances of the proposed TESTR algorithm.
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
Authors: Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Though neural radiance fields ("NeRF") have demonstrated impressive view synthesis results on objects and small bounded regions of space, they struggle on "unbounded" scenes, where the camera may point in any direction and content may exist at any distance. In this setting, existing NeRF-like models often produce blurry or low-resolution renderings (due to the unbalanced detail and scale of nearby and distant objects), are slow to train, and may exhibit artifacts due to the inherent ambiguity of the task of reconstructing a large scene from a small set of images. We present an extension of mip-NeRF (a NeRF variant that addresses sampling and aliasing) that uses a non-linear scene parameterization, online distillation, and a novel distortion-based regularizer to overcome the challenges presented by unbounded scenes. Our model, which we dub "mip-NeRF 360" as we target scenes in which the camera rotates 360 degrees around a point, reduces mean-squared error by 57% compared to mip-NeRF, and is able to produce realistic synthesized views and detailed depth maps for highly intricate, unbounded real-world scenes.
VALHALLA: Visual Hallucination for Machine Translation
Authors: Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu (Richard) Chen, Rogerio S. Feris, David Cox, Nuno Vasconcelos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Designing better machine translation systems by considering auxiliary inputs such as images has attracted much attention in recent years. While existing methods show promising performance over the conventional text-only translation systems, they typically require paired text and image as input during inference, which limits their applicability to real-world scenarios. In this paper, we introduce a visual hallucination framework, called VALHALLA, which requires only source sentences at inference time and instead uses hallucinated visual representations for multimodal machine translation. In particular, given a source sentence an autoregressive hallucination transformer is used to predict a discrete visual representation from the input text, and the combined text and hallucinated representations are utilized to obtain the target translation. We train the hallucination transformer jointly with the translation transformer using standard backpropagation with cross-entropy losses while being guided by an additional loss that encourages consistency between predictions using either ground-truth or hallucinated visual representations. Extensive experiments on three standard translation datasets with a diverse set of language pairs demonstrate the effectiveness of our approach over both text-only baselines and state-of-the-art methods. Our codes and models will be publicly available. Project page: http://www.svcl.ucsd.edu/projects/valhalla.