Reconstructing 3D Human Pose by Watching Humans in the Mirror

In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person’s image through a mirror. Compared to general scenarios of 3D pose estimation from a single view, the mirror reflection provides an additional view for resolving the depth ambiguity. We develop an optimization-based approach that exploits mirror symmetry constraints for accurate 3D pose reconstruction. We also provide a method to estimate the surface normal of the mirror from vanishing points in the single image. To validate the proposed approach, we collect a large-scale dataset named Mirrored-Human, which covers a large variety of human subjects, poses and backgrounds. The experiments demonstrate that, when trained on Mirrored-Human with our reconstructed 3D poses as pseudo ground-truth, the accuracy and generalizability of existing single-view 3D pose estimators can be largely improved. The code and dataset are available at https://zju3dv.github.io/Mirrored-Human/.

2021: Qi Fang, Qing Shuai, Junting Dong, H. Bao, Xiaowei Zhou

https://arxiv.org/pdf/2104.00340v1.pdf

A Conversational Paradigm for Program Synthesis

Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled.

2022: Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Haiquan Wang, Yingbo Zhou, S. Savarese, Caiming Xiong

Ranked #1 on Program Synthesis on HumanEval

https://arxiv.org/pdf/2203.13474v3.pdf

Masked Siamese Networks for Label-Efficient Learning

We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containing randomly masked patches to the representation of the original unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures, while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance, on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy, and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark.

2022: Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael G. Rabbat, Nicolas Ballas

https://arxiv.org/pdf/2204.07141v1.pdf

Multiface: A Dataset for Neural Face Rendering

In this work, we present Multiface, a new multi-view, high-resolution human face dataset collected from 13 identities at Reality Labs Research for neural face rendering. We introduce Mugsy, a large scale multi-camera apparatus to capture high-resolution synchronized videos of a facial performance. The goal of Multiface is to close the gap in accessibility to high quality data in the academic community and to enable research in VR telepresence. Along with the release of the dataset, we conduct ablation studies on the influence of different model architectures toward the model’s interpolation capacity of novel viewpoint and expressions. With a conditional VAE model serving as our baseline, we found that adding spatial bias, texture warp field, and residual connections improves performance on novel view synthesis.

2022: Cheng-hsin Wuu, N. Zheng, Scott Ardisson, Rohan Bali, Danielle Belko, Eric Brockmeyer, L. Evans, Timothy Godisart, Hyowon Ha, Alexander Hypes, Taylor Koska, Steven Krenn, Stephen Lombardi, Xi Luo, Kevyn Mcphail, Laura Millerschoen, Michal Perdoch, Mark Pitts, Alexander Richard, Jason M. Saragih, Junko Saragih, Takaaki Shiratori, Tomas Simon, Matt Stewart, Autumn Trimble, Xinshuo Weng, David Whitewolf, Chenglei Wu, Shoou-I Yu, Yaser Sheikh

Ranked #1 on Novel View Synthesis on 10,000 People - Human Pose Recognition Data (using extra training data)

https://arxiv.org/pdf/2207.11243v1.pdf

OCR-free Document Understanding Transformer

Understanding document images ( e.g. , invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document . Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs. Although such OCR-based approaches have shown promising performance, they suffer from 1) high computational costs for using OCR; 2) inflexibility of OCR models on languages or types of documents; 3) OCR error propagation to the subsequent process. To address these issues, in this paper, we introduce a novel OCR-free VDU model named Donut , which stands for Do cume n t u nderstanding t ransformer. As the first step in OCR-free VDU research, we propose a simple architecture ( i.e. , Transformer) with a pre-training objective ( i.e., cross-entropy loss). Donut is conceptually simple yet effective.

2021: Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park

https://arxiv.org/pdf/2111.15664v2.pdf

OpenXAI: Towards a Transparent Evaluation of Model Explanations

While several types of post hoc explanation methods (e.g., feature attribution methods) have been proposed in recent literature, there is little to no work on systematically benchmarking these methods in an efficient and transparent manner. Here, we introduce OpenXAI, a comprehensive and extensible opensource framework for evaluating and benchmarking post hoc explanation methods. OpenXAI comprises of the following key components: (i) a flexible synthetic data generator and a collection of diverse real-world datasets, pre-trained models, and state-of-the-art feature attribution methods, (ii) open-source implementations of twenty-two quantitative metrics for evaluating faithfulness, stability (robustness), and fairness of explanation methods, and (iii) the first ever public XAI leaderboards to benchmark explanations. OpenXAI is easily extensible, as users can readily evaluate custom explanation methods and incorporate them into our leaderboards. Overall, OpenXAI provides an automated end-to-end pipeline that not only simplifies and standardizes the evaluation of post hoc explanation methods, but also promotes transparency and reproducibility in benchmarking these methods. OpenXAI datasets and data loaders, implementations of state-of-the-art explanation methods and evaluation metrics, as well as leaderboards are publicly available at https://open-xai.github.io/.

2022: Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, I. Puri, M. Zitnik, Himabindu Lakkaraju

https://arxiv.org/pdf/2206.11104v1.pdf

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. Moreover, we train YOLOv7 only on MS COCO dataset from scratch without using any other datasets or pre-trained weights.

2022: Chien-Yao Wang, Alexey Bochkovskiy, H. Liao

https://arxiv.org/pdf/2207.02696v1.pdf

Matryoshka Representations for Adaptive Deployment

Our contribution is Matryoshka Representation Learning ( MRL ) which encodes information at different granularities and allows a single embedding to adapt to the computational constraints of downstream tasks. MRL minimally modifies existing representation learning pipelines and imposes no additional cost during inference and deployment. MRL learns coarse-to-fine representations that are at least as accurate and rich as independently trained low-dimensional representations.2022: Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, S. Kakade, Prateek Jain, Ali Farhadihttps://arxiv.org/pdf/2205.13147v2.pdf

Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs

We propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31 × 31, in contrast to commonly used 3 × 3. RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g ., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias.

2022: Xiaohan Ding, X. Zhang, Yi Zhou, Jungong Han, Guiguang Ding, Jian Sun

https://arxiv.org/pdf/2203.06717v4.pdf

More ConvNets in the 2020s: Scaling up Kernels Beyond 51×51 using Sparsity

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local but large attention mechanism, showing appealing performance and efficiency. We propose Sparse Large Kernel Network ( SLaK ), a pure CNN architecture equipped with 51 × 51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as typical downstream tasks.

2022: S. Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Q. Xiao, Boqian Wu, Mykola Pechenizkiy, D. Mocanu, Zhangyang Wang

https://arxiv.org/pdf/2207.03620v1.pdf