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M2DAR: Multi-View Multi-Scale Driver Action recognition with vision Transformer

M2DAR: Multi-View Multi-Scale Driver Action Recognition with...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Ma, Yunsheng Yuan, Liangqi Abdelraouf, Amr Han, Kyungtae Gupta, Rohit Li, Zihao Wang, Ziran Purdue University College of Engineering United States Toyota Motor North America InfoTech Labs United States

ISBN: (纸本)9798350302493

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M2DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://***/PurdueDigitalTwin/M2DAR. © 2023 ieee.

关键词： Statistical tests

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OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

OPERA: Alleviating Hallucination in Multi-Modal Large Langua...

引用

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

作者： Hu, Qidong Dong, Xiaoyi Zhang, Pan Wang, Bin He, Conghui Wang, Jiaqi Lin, Dahua Zhang, Weiming Yu, Nenghai Univ Sci & Technol China Anhui Prov Key Lab Digital Secur Hefei Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Hallucination, posed as a pervasive challenge of multi-modal large language models (MLLMs), has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources, incurring inevitable additional costs. In this paper, we present OPERA, a novel MLLM decoding method grounded in an Overtrust Penalty and a Retrospection-Allocation strategy, serving as a nearly free lunch to alleviate the hallucination issue without additional data, knowledge, or training. Our approach begins with an interesting observation that, most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix, i.e., MLLMs tend to generate new tokens by focusing on a few summary tokens, but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation, OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue, along with a rollback strategy that retrospects the presence of summary tokens in the previ-ously generated tokens, and re-allocate the token selection if necessary. With extensive experiments, OPERA shows significant hallucination-mitigating performance on differ-ent MLLMs and metrics, proving its effectiveness and gen-erality. Our code is at: https://github. com/shikiw/OPERA.

关键词： Hallucination Large vision-language model LLM Multimodal LLM

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Efficient Conditional Pre-training for Transfer Learning

Efficient Conditional Pre-training for Transfer Learning

引用

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

作者： Chakraborty, Shuvam Uzkent, Burak Ayush, Kumar Tanmay, Kumar Sheehan, Evan Ermon, Stefano Stanford Univ Stanford CA 94305 USA IIT Kharagpur Kharagpur W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This strategy helps reduce dependence on the target dataset and improves convergence rate and generalization on the target task. Although pre-training on large-scale datasets is very useful for new methods or models, its foremost disadvantage is high training cost. To address this, we propose efficient filtering methods to select relevant subsets from the pre-training dataset. Additionally, we discover that lowering image resolutions in the pre-training step offers a great trade-off between cost and performance. We validate our techniques by pre-training on ImageNet in both the unsupervised and supervised settings and finetuning on a diverse collection of target datasets and tasks. Our proposed methods drastically reduce pre-training cost and provide strong performance boosts. Finally, we improve the current standard of ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.

关键词： Training computer vision Costs Image resolution Filtering Computational modeling Transfer learning

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Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding

Visual Programming for Zero-shot Open-Vocabulary 3D Visual G...

引用

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

作者： Yuan, Zhihao Ren, Jinke Feng, Chun-Mei Zhao, Hengshuang Cui, Shuguang Li, Zhen CUHKSZ FNii Shenzhen Peoples R China CUHKSZ SSE Shenzhen Peoples R China ASTAR IHPC Singapore Singapore HKU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

3D Visual Grounding (3DVG) aims at localizing 3D object based on textual descriptions. Conventional supervised methods for 3DVG often necessitate extensive annotations and a predefined vocabulary, which can be restrictive. To address this issue, we propose a novel visual programming approach for zero-shot open-vocabulary 3DVG, leveraging the capabilities of large language models (LLMs). Our approach begins with a unique dialog-based method, engaging with LLMs to establish a foundational understanding of zero-shot 3DVG. Building on this, we design a visual program that consists of three types of modules, i.e., view-independent, view-dependent, and functional modules. These modules, specifically tailored for 3D scenarios, work collaboratively to perform complex reasoning and inference. Furthermore, we develop an innovative language-object correlation module to extend the scope of existing 3D object detectors into open-vocabulary scenarios. Extensive experiments demonstrate that our zero-shot approach can outperform some supervised baselines, marking a significant stride towards effective 3DVG. Code is available at https://***/ZSVG3D.

关键词： Point Cloud vision and Language Visual Grounding

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Is Multimodal vision Supervision Beneficial to Language?

Is Multimodal Vision Supervision Beneficial to Language?

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Madasu, Avinash Lal, Vasudev Unc Chapel Hill Department of Computer Science United States Cognitive Computing Research Intel Labs United States

ISBN: (纸本)9798350302493

vision (image & video) - Language (VL) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks like image-retrieval, video-retrieval, visual question answering etc. These models are trained in an unsupervised way and greatly benefit from the complementary modality supervision. In this paper, we explore if the language representations trained using vision supervision perform better than vanilla language representations on Natural Language Understanding and commonsense reasoning benchmarks. We experiment with a diverse set of image-text models such as ALBEF, BLIP, METER and video-text models like ALPRO, Frozen in Time, VIOLET. We compare the performance of language representations of stand-alone text encoders of these models to the language representations of text encoders learnt through vision supervision. Our experiments suggest that vanilla language representations show superior performance on most of the tasks. These results shed light on the current drawbacks of the vision-language models. The code is available at https://***/avinashsai/MML © 2023 ieee.

关键词： Signal encoding

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Unsupervised Anomaly Detection from Time-of-Flight Depth Images

Unsupervised Anomaly Detection from Time-of-Flight Depth Ima...

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

作者： Schneider, Pascal Rambach, Jason Mirbach, Bruno Stricker, Didier German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration into the loss function. Training is done unsupervised using normal sequences without need for any additional annotations. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask and demonstrate its beneficial effect on the anomaly detection performance through evaluation on a large public dataset, for which we are also the first ones to present results on.

关键词： Training Optical losses computer vision Cameras Transformers Sensors Task analysis

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Cross Transferring Activity recognition to Word Level Sign Language Detection

Cross Transferring Activity Recognition to Word Level Sign L...

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

作者： Radhakrishnan, Srijith Mohan, Nikhil C. Varma, Manisimha Varma, Jaithra Pai, Smitha N. Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Data Sci & Comp Applicat Manipal 576104 Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8x8 ResNet 101 as a robust and suitable architecture for the task of WSLR.

关键词： computer vision conferences Transfer learning Lighting Gesture recognition computer architecture Assistive technologies

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Motion Aware Double Attention Network for Dynamic Scene Deblurring

Motion Aware Double Attention Network for Dynamic Scene Debl...

引用

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

作者： Yang, Dan Yamac, Mehmet Huawei Technol Oy Finland Co Ltd Helsinki Finland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Motion deblurring in dynamic scenes is a challenging task when the blurring is caused by one or a combination of various reasons such as moving objects, camera movement, etc. Since event cameras can detect changes in intensity with a low latency, necessary motion information is inherently captured in event data, which could be quite useful for deblurring standard camera images. The degradation intensity does not show homogeneity across an image due to factors like object depth, speed, etc. We propose a two-branch network structure, Motion Aware Double Attention Network (MADANet), that pays special attention to areas with high blur. As part of the network, event data is first used by the high blur region segmentation module that creates a probability-like score for areas exhibiting high relative motion to the camera. Then, the event data is also injected to feature maps in the main body, where there is a second attention mechanism available for each branch. The effective usage of event data and two-level attention mechanisms makes the network very compact. During the experiment, it was shown that the proposed network could achieve state-of-the-art performance not only on the benchmark dataset from GoPro, but also on two newly collected datasets, one of which contains real event data.

关键词： Degradation Image segmentation Motion segmentation Dynamics Data visualization Cameras pattern recognition

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Visual Domain Bridge: A source-free domain adaptation for cross-domain few-shot learning

Visual Domain Bridge: A source-free domain adaptation for cr...

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

作者： Yazdanpanah, Moslem Moradi, Parham Univ Kurdistan Erbil Iraq

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the covariate shift, deep neural networks performance always degrades when applied to novel domains. In order to mitigate this problem, domain adaptation techniques require samples from target data during the feature extraction training, which is not always applicable in real-world scenarios. Batch Normalization is a known component of computer vision models, aiming at reducing the training-time covariate shift. However, facing distribution shift results in an internal state mismatch inside the Batch-Norm layers during the inference time. In favor of alleviating the induced mismatch, this paper proposes a source-free, lightweight and straightforward approach by introducing the "Visual Domain Bridge" concept reducing the BatchNorm's internal mismatch in the cross-domain settings. Compared to the other BatchNorm-based source-free domain adaptation techniques such as AdaBN and Prediction-BN, our method formed a new state-of-the-art cross-domain few-shot fine-tuning method neglecting extra augmentations;while improving the performance in near-domain settings too. The proposed method can integrate with other domain adaptation methods and enhance their performance requiring just a few lines of modification in the BatchNorm's implementation. Implementations are available in https://***/MosyMosy/VDB

关键词： Bridges Training Deep learning Visualization computer vision conferences Neural networks

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Network Amplification with Efficient MACs Allocation

Network Amplification with Efficient MACs Allocation

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

作者： Liu, Chuanjian Han, Kai Xiao, An Nie, Ying Zhang, Wei Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracies, such as EfficientNet and RegNet. These works try to enlarge the network architecture with one unified rule by sampling and statistical methods. However, the rule is not prospective to the design of large networks because it is obtained from the experience of researchers on small network architectures. In this paper, we propose to enlarge the capacity of CNN models by fine-grained MACs allocation for the width, depth and resolution on the stage level. In particular, starting from a base small model, we gradually add extra channels, layers or resolution by using a dynamic programming manner. With step-by-step modifying the computations on different stages, the enlarged network will be equipped with optimal allocation and utilization of MACs. On EfficientNet, our method consistently outperforms the performance of the original scaling method. In particular, the proposed method is used to enlarge models sourced by GhostNet, we achieve state-of-the-art 80.9% and 84.3% ImageNet top-1 accuracies under the setting of 600M and 4.4B MACs, respectively.

关键词： Statistical analysis Computational modeling Heuristic algorithms Neural networks Network architecture pattern recognition Dynamic programming

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