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NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

引用

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

作者： Taehoon Kim Pyunghwan Ahn Sangyun Kim Sihaeng Lee Mark Marsden Alessandra Sala Seung Hwan Kim Bohyung Han Kyoung Mu Lee Honglak Lee Kyounghoon Bae Xiangyu Wu Yi Gao Hailiang Zhang Yang Yang Weili Guo Jianfeng Lu Youngtaek Oh Jae Won Cho Dong-Jin Kim In So Kweon Junmo Kim Wooyoung Kang Won Young Jhoo Byungseok Roh Jonghwan Mun Solgil Oh Kenan Emir Ak Gwang-Gook Lee Yan Xu Mingwei Shen Kyomin Hwang Wonsik Shin Kamin Lee Wonhark Park Dongkwan Lee Nojun Kwak Yujin Wang Yimu Wang Tiancheng Gu Xingchang Lv Mingmao Sun

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project 1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Training Adaptation models Visualization computer vision Computational modeling conferences Training data

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Open-Vocabulary Object Detection Using Captions

Open-Vocabulary Object Detection Using Captions

引用

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

作者： Zareian, Alireza Dela Rosa, Kevin Hu, Derek Hao Chang, Shih-Fu Snap Inc Seattle WA 98121 USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665445092

Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection.

关键词： Location awareness Training Deep learning computer vision Costs Annotations Object detection

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Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection

Equalization Loss v2: A New Gradient Balance Approach for Lo...

引用

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

作者： Tan, Jingru Lu, Xin Zhang, Gang Yin, Changqing Li, Quanquan Tongji Univ Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the disjointed optimization of representation and classifier might lead to suboptimal results. However, end-to-end training methods, like equalization loss (EQL), still perform worse than decoupled training methods. In this paper, we reveal the main issue in long-tailed object detection is the unbalanced gradients between positives and negatives, and find that EQL does not solve it well. To address the problem of Unbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that rebalances the training process for each category independently and equally. Extensive experiments are performed on the challenging LVIS benchmark. EQI, v2 outperforms origin EQI, by about 4 points overall AP with 14 similar to 18 points improvements on the rare categories. More importantly, it also surpasses decoupled training methods. Without further tuning for the Open Images dataset, EQL v2 improves EQL by 7.3 points AP showing strong generalization ability. Codes have been released at https://***/tztztztztz/eq1v2

关键词： Training computer vision Codes Object detection Benchmark testing Boosting pattern recognition

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Scene-aware Generative Network for Human Motion Synthesis

Scene-aware Generative Network for Human Motion Synthesis

引用

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

作者： Wang, Jingbo Yan, Sijie Dai, Bo Lin, Dahua Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Ctr Perceptual & Interact Intelligence Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We revisit human motion synthesis, a task useful in various real-world applications, in this paper. Whereas a number of methods have been developed previously for this task, they are often limited in two aspects: 1) focus on the poses while leaving the location movement behind, and 2) ignore the impact of the environment on the human motion. In this paper, we propose a new framework, with the interaction between the scene and the human motion taken into account. Considering the uncertainty of human motion, we formulate this task as a generative task, whose objective is to generate plausible human motion conditioned on both the scene and the human's initial position. This framework factorizes the distribution of human motions into a distribution of movement trajectories conditioned on scenes and that of body pose dynamics conditioned on both scenes and trajectories. We further derive a GAN-based learning approach, with discriminators to enforce the compatibility between the human motion and the contextual scene as well as the 3D- to-2D projection constraints. We assess the effectiveness of the proposed method on two challenging datasets, which cover both synthetic and real-world environments.

关键词： computer vision Uncertainty Semantics Dynamics Trajectory pattern recognition Task analysis

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Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

Greedy Hierarchical Variational Autoencoders for Large-Scale...

引用

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

作者： Wu, Bohan Nair, Suraj Martin-Martin, Roberto Li Fei-Fei Finn, Chelsea Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on large and diverse datasets. To address this underfitting challenge, we first observe that the ability to train larger video prediction models is often bottlenecked by the memory constraints of GPUs or TPUs. In parallel, deep hierarchical latent variable models can produce higher quality predictions by capturing the multi-level stochasticity of future observations, but end-to-end optimization of such models is notably difficult. Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction. We introduce Greedy Hierarchical Variational Autoencoders (GHVAEs), a method that learns highfidelity video predictions by greedily training each level of a hierarchical autoencoder. In comparison to state-of-the-art models, GHVAEs provide 17-55% gains in prediction performance on four video datasets, a 35-40% higher success rate on real robot tasks, and can improve performance monotonically by simply adding more modules.

关键词： Training Visualization Memory management Stacking Predictive models Planning pattern recognition

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Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place recognition

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descript...

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

作者： Hausler, Stephen Garg, Sourav Xu, Ming Milford, Michael Fischer, Tobias Queensland Univ Technol QUT Ctr Robot Brisbane Qld Australia

ISBN: (纸本)9781665445092

Visual Place recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. This paper introduces Patch-NetVLAD, which provides a novel formulation for combining the advantages of both local and global descriptor methods by deriving patch-level features from NetVLAD residuals. Unlike the fixed spatial neighborhood regime of existing local keypoint features, our method enables aggregation and matching of deep-learned local features defined over the feature-space grid. We further introduce a multi-scale fusion of patch features that have complementary scales (i.e. patch sizes) via an integral feature space and show that the fused features are highly invariant to both condition (season, structure, and illumination) and viewpoint (translation and rotation) changes. Patch-NetVLAD achieves state-of-the-art visual place recognition results in computationally limited scenarios, validated on a range of challenging real-world datasets, including winning the Facebook Mapillary Visual Place recognition Challenge at ECCV2020. It is also adaptable to user requirements, with a speed-optimised version operating over an order of magnitude faster than the state-of-the-art. By combining superior performance with improved computational efficiency in a configurable framework, Patch-NetVLAD is well suited to enhance both stand-alone place recognition capabilities and the overall performance of SLAM systems.

关键词： Visualization computer vision Simultaneous localization and mapping Social networking (online) Autonomous systems Face recognition Lighting

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The New Agronomists: Language Models are Experts in Crop Management

The New Agronomists: Language Models are Experts in Crop Man...

引用

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

作者： Jing Wu Zhixin Lai Suiyao Chen Ran Tao Pan Zhao Naira Hovakimyan University of Illinois at Urbana-Champaign Cornell University University of South Florida University of Alabama

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimizing these practices remains a complex and multifaceted challenge. In response, previous studies have explored using reinforcement learning with crop simulators, typically employing simple neural-network-based reinforcement learning (RL) agents. Building on this foundation, this paper introduces a more advanced intelligent crop management system. This system uniquely combines RL, a language model (LM), and crop simulations facilitated by the Decision Support System for Agrotechnology Transfer (DSSAT). We utilize deep RL, specifically a deep Q-network, to train management policies that process numerous state variables from the simulator as observations. A novel aspect of our approach is the conversion of these state variables into more informative language, facilitating the language model’s capacity to understand states and explore optimal management practices. The empirical results reveal that the LM exhibits superior learning capabilities. Through simulation experiments with maize crops in Florida (US) and Zaragoza (Spain), the LM not only achieves state-of-the-art performance under various evaluation metrics but also demonstrates a remarkable improvement of over 49% in economic profit, coupled with reduced environmental impact when compared to baseline methods. Our code is available at https://***/jingwu6/LM_AG.

关键词： Decision support systems Profitability Noise Green products Crops Deep reinforcement learning pattern recognition

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Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes

Synthesizing Long-Term 3D Human Motion and Interaction in 3D...

引用

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

作者： Wang, Jiashun Xu, Huazhe Xu, Jingwei Liu, Sifei Wang, Xiaolong Univ Calif San Diego San Diego CA 92093 USA Univ Calif Berkeley Berkeley CA USA Shanghai Jiao Tong Univ Shanghai Peoples R China NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordance. On the other hand, affordance reasoning (e.g., standing on the floor or sitting on the chair) has mainly been studied with static human pose and gestures, and it has rarely been addressed with human motion. In this paper, we propose to bridge human motion synthesis and scene affordance reasoning. We present a hierarchical generative framework to synthesize long-term 3D human motion conditioning on the 3D scene structure. Building on this framework, we further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis. Our experiments show significant improvements over previous approaches on generating natural and physically plausible human motion in a scene.

关键词： Graphics Geometry computer vision Three-dimensional displays Affordances Computational modeling Cognition

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Orthogonal Over-Parameterized Training

Orthogonal Over-Parameterized Training

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

作者： Liu, Weiyang Lin, Rongmei Liu, Zhen Rehg, James M. Paull, Liam Xiong, Li Song, Le Weller, Adrian Univ Cambridge Cambridge England Max Planck Inst Intelligent Syst Stuttgart Germany Emory Univ Atlanta GA 30322 USA Univ Montreal Mila Montreal PQ Canada Georgia Inst Technol Atlanta GA 30332 USA Alan Turing Inst London England

ISBN: (纸本)9781665445092

The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great importance. We propose a novel orthogonal over-parameterized training (OPT) framework that can provably minimize the hyperspherical energy which characterizes the diversity of neurons on a hypersphere. By maintaining the minimum hyperspherical energy during training, OPT can greatly improve the empirical generalization. Specifically, OPT fixes the randomly initialized weights of the neurons and learns an orthogonal transformation that applies to these neurons. We consider multiple ways to learn such an orthogonal transformation, including unrolling orthogonalization algorithms, applying orthogonal parameterization, and designing orthogonality-preserving gradient descent. For better scalability, we propose the stochastic OPT which performs orthogonal transformation stochastically for partial dimensions of neurons. Interestingly, OPT reveals that learning a proper coordinate system for neurons is crucial to generalization. We provide some insights on why OPT yields better generalization. Extensive experiments validate the superiority of OPT over the standard training.

关键词： Training computer vision Scalability Neurons Optimized production technology computer architecture pattern recognition

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DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervis...

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

作者： Wu, Xinyi Wu, Zhenyao Guo, Hao Ju, Lili Wang, Song Univ South Carolina Columbia SC 29208 USA Farsee2 Technol Ltd Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotations. In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmentation without using labeled nighttime image data. It employs an adversarial training with a labeled daytime dataset and an unlabeled dataset that contains coarsely aligned day-night image pairs. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object categories on a daytime image as a pseudo supervision to segment its counterpart nighttime image. We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects. The proposed DANNet is the first one-stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night image transfer models as a separate pre-processing stage. Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our method achieves state-of-the-art performance for nighttime semantic segmentation.

关键词： Training Bridges Image segmentation computer vision Annotations Semantics Neural networks

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