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Object-centric Video Representation for Long-term Action Anticipation

Object-centric Video Representation for Long-term Action Ant...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Ce Fu, Changcheng Wang, Shijie Agarwal, Nakul Lee, Kwonjoon Choi, Chiho Sun, Chen Brown Univ Providence RI 02912 USA Honda Res Inst San Jose CA USA Samsung Suwon South Korea

ISBN: (纸本)9798350318920;9798350318937

This paper focuses on building object-centric representations for long-term action anticipation in videos. Our key motivation is that objects provide important cues to recognize and predict human-object interactions, especially when the predictions are longer term, as an observed "background" object could be used by the human actor in the future. We observe that existing object-based video recognition frameworks either assume the existence of indomain supervised object detectors or follow a fully weakly-supervised pipeline to infer object locations from action labels. We propose to build object-centric video representations by leveraging visual-language pretrained models. This is achieved by "object prompts", an approach to extract task-specific object-centric representations from general-purpose pretrained models without finetuning. To recognize and predict human-object interactions, we use a Transformer-based neural architecture which allows the "retrieval" of relevant objects for action anticipation at various time scales. We conduct extensive evaluations on the Ego4D, 50Salads, and EGTEA Gaze+ benchmarks. Both quantitative and qualitative results confirm the effectiveness of our proposed method. Our code is available at ***/brown-palm/ObjectPrompt.

关键词： Algorithms Video recognition and understanding

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Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions

Leveraging Synthetic Data to Learn Video Stabilization Under...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kerim, Abdulrahman Ramos, Washington L. S. Marcolino, Leandro Soriano Nascimento, Erickson R. Jiang, Richard Univ Lancaster Lancaster England Univ Creat Arts Farnham Surrey England Univ Fed Minas Gerais Belo Horizonte MG Brazil Microsoft Redmond WA USA

ISBN: (纸本)9798350318920;9798350318937

Stabilization plays a central role in improving the quality of videos. However, current methods perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather video stabilization algorithm that dispenses real data for training, relying solely on synthetic data. Our approach leverages specially generated synthetic data to avoid the feature extraction issues faced by current methods. To achieve this, we present a novel data generator to produce the required training data with an automatic ground-truth extraction procedure. We also propose a new dataset, VSAC105Real, and compare our method to five recent video stabilization algorithms using two benchmarks. Our method generalizes well on real-world videos across all weather conditions and does not require large-scale synthetic training data. Implementations for our proposed video stabilization algorithm, generator, and datasets are available at https://***/A-Kerim/SyntheticData4VideoStabilization WACV 2024.

关键词： Algorithms Video recognition and understanding

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Bag of Tricks for Fully Test-Time Adaptation

Bag of Tricks for Fully Test-Time Adaptation

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Mounsaveng, Saypraseuth Chiaroni, Florent Boudiaf, Malik Pedersoli, Marco Ben Ayed, Ismail ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350318920;9798350318937

Fully Test-Time Adaptation (TTA), which aims at adapting models to data drifts, has recently attracted wide interest. Numerous tricks and techniques have been proposed to ensure robust learning on arbitrary streams of unlabeled data. However, assessing the true impact of each individual technique and obtaining a fair comparison still constitutes a significant challenge. To help consolidate the community's knowledge, we present a categorization of selected orthogonal TTA techniques, including small batch normalization, stream rebalancing, reliable sample selection, and network confidence calibration. We meticulously dissect the effect of each approach on different scenarios of interest. Through our analysis, we shed light on trade-offs induced by those techniques between accuracy, the computational power required, and model complexity. We also uncover the synergy that arises when combining techniques and are able to establish new state-of-the-art results.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Adaptive Zone-aware Hierarchical Planner for vision-Language Navigation

Adaptive Zone-aware Hierarchical Planner for Vision-Language...

引用

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

作者： Gao, Chen Peng, Xingyu Yan, Mi Wang, He Yang, Lirong Ren, Haibing Li, Hongsheng Liu, Si Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China Peking Univ CFCS Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Meituan Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

The task of vision-Language Navigation (VLN) is for an embodied agent to reach the global goal according to the instruction. Essentially, during navigation, a series of subgoals need to be adaptively set and achieved, which is naturally a hierarchical navigation process. However, previous methods leverage a single-step planning scheme, i.e., directly performing navigation action at each step, which is unsuitable for such a hierarchical navigation process. In this paper, we propose an Adaptive Zone-aware Hierarchical Planner (AZHP) to explicitly divides the navigation process into two heterogeneous phases, i.e., sub-goal setting via zone partition/selection (high-level action) and subgoal executing (low-level action), for hierarchical planning. Specifically, AZHP asynchronously performs two levels of action via the designed State-Switcher Module (SSM). For high-level action, we devise a Scene-aware adaptive Zone Partition (SZP) method to adaptively divide the whole navigation area into different zones on-the-fly. Then the Goal-oriented Zone Selection (GZS) method is proposed to select a proper zone for the current sub-goal. For low-level action, the agent conducts navigation-decision multi-steps in the selected zone. Moreover, we design a Hierarchical RL (HRL) strategy and auxiliary losses with curriculum learning to train the AZHP framework, which provides effective supervision signals for each stage. Extensive experiments demonstrate the superiority of our proposed method, which achieves state-of-the-art performance on three VLN benchmarks (REVERIE, SOON, R2R).

关键词： language reasoning vision

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Patch-based Selection and Refinement for Early Object Detection

Patch-based Selection and Refinement for Early Object Detect...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Tianyi Kasichainula, Kishore Zhuo, Yaoxin Li, Baoxin Seo, Jae-Sun Cao, Yu Univ Minnesota Minneapolis MN 55455 USA Arizona State Univ Tempe AZ USA Cornell Tech New York NY USA

ISBN: (纸本)9798350318920;9798350318937

Early object detection (OD) is a crucial task for the safety of many dynamic systems. Current OD algorithms have limited success for small objects at a long distance. To improve the accuracy and efficiency of such a task, we propose a novel set of algorithms that divide the image into patches, select patches with objects at various scales, elaborate the details of a small object, and detect it as early as possible. Our approach is built upon a transformer-based network and integrates the diffusion model to improve the detection accuracy. As demonstrated on BDD100K, our algorithms enhance the mAP for small objects from 1.03 to 8.93, and reduce the data volume in computation by more than 77%.

关键词： 3D Algorithms Algorithms etc. Generative models for image Image recognition and understanding video

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SimA: Simple Softmax-free Attention for vision Transformers

SimA: Simple Softmax-free Attention for Vision Transformers

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Koohpayegani, Soroush Abbasi Pirsiavash, Hamed Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9798350318920;9798350318937

Recently, vision transformers have become very popular. However, deploying them in many applications is computationally expensive partly due to the Softmax layer in the attention block. We introduce a simple yet effective, Softmax-free attention block, SimA, which normalizes query and key matrices with simple l(1)-norm instead of using Softmax layer. Then, the attention block in SimA is a simple multiplication of three matrices, so SimA can dynamically change the ordering of the computation at the test time to achieve linear computation on the number of tokens or the number of channels. We empirically show that SimA applied to three SOTA variations of transformers, DeiT, XCiT, and CvT, results in on-par accuracy compared to the SOTA models, without any need for Softmax layer. Interestingly, changing SimA from multi-head to single-head has only a small effect on the accuracy, which further simplifies the attention block. Moreover, we show that SimA is much faster on small edge devices, e.g., Raspberry Pi, which we believe is due to higher complexity of Softmax layer on those devices. The code is available here: https://***/UCDvision/sima

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Training Strategies for vision Transformers for Object Detection

Training Strategies for Vision Transformers for Object Detec...

引用

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

作者： Singh, Apoorv Motional Boston MA 02210 USA

ISBN: (纸本)9798350302493

vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy, and not so much on the inference-time budget. For a safety critical system like autonomous driving, real-time inference at the on-board compute is an absolute necessity. This keeps our object detection algorithm under a very tight run-time budget. In this paper, we evaluated a variety of strategies to optimize on the inference-time of vision transformers based object detection methods keeping a close-watch on any performance variations. Our chosen metric for these strategies is accuracy-runtime joint optimization. Moreover, for actual inference-time analysis we profile our strategies with float32 and float16 precision with TensorRT module. This is the most common format used by the industry for deployment of their Machine Learning networks on the edge devices. We showed that our strategies are able to improve inference-time by 63% at the cost of performance drop of mere 3% for our problem-statement defined in Sec. 3. These strategies brings down vision Transformers detectors [3, 15, 18, 19, 36] inference-time even less than traditional single-image based CNN detectors like FCOS [17, 25, 33]. We recommend practitioners use these techniques to deploy Transformers based hefty multi-view networks on a budge-constrained robotic platform.

关键词： Autonomous vehicles

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Activity-based Early Autism Diagnosis Using A Multi-Dataset Supervised Contrastive Learning Approach

Activity-based Early Autism Diagnosis Using A Multi-Dataset ...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rani, Asha Verma, Yashaswi IIT Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9798350318920;9798350318937

Autism Spectrum Disorder (ASD) is a neurological disorder. Its primary symptoms include difficulty in verbal/non-verbal communication and rigid/repetitive behavior. Traditional methods of autism diagnosis require multiple visits to a human specialist. However, this process is generally time-consuming and may result in a delayed (early) intervention. In this paper, we present a data-driven approach to automate autism diagnosis using video clips of subjects performing simple activities recorded in a weakly constrained environment. This task is particularly challenging since the available training data is small, videos from the two categories ("ASD" and "Control") are generally perceptually indistinguishable, and there is no clear understanding of what features would be beneficial in this task. To address these, we present a novel multi-dataset supervised contrastive learning technique to learn discriminative features simultaneously from multiple video datasets with significantly diverse distributions. Extensive empirical analyses demonstrate the promise of our approach compared to competing techniques on this challenging task.

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations Machine learning architectures Video recognition and understanding

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CLRerNet: Improving Confidence of Lane Detection with LaneIoU

CLRerNet: Improving Confidence of Lane Detection with LaneIo...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Honda, Hiroto Uchida, Yusuke GO Inc Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

autonomous driving and driver assistance systems. Modern deep lane detection methods with anchor-based lane representation exhibit excellent performance on lane detection benchmarks. Through preliminary oracle experiments, we firstly disentangle the lane representation components to determine the direction of our approach. We show that correct lane positions are already among the predictions of an existing anchor-based detector, and the confidence scores that accurately represent intersection-over-union (IoU) with ground truths are the most beneficial. Based on the finding, we propose LaneIoU that better correlates with the metric, by taking the local lane angles into consideration. We develop a novel detector coined CLRerNet featuring LaneIoU for the target assignment cost and loss functions aiming at the improved quality of confidence scores. Through careful and fair benchmark including cross validation, we demonstrate that CLRerNet outperforms the state-of-the-art by a large margin - enjoying F1 score of 81.43% compared with 80.47% of the existing method on CULane, and 86.47% compared with 86.10% on CurveLanes. Code and models are available at https: //***/hirotomusiker/CLRerNet.

关键词： Algorithms Applications Autonomous Driving Image recognition and understanding

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Guided Cluster Aggregation: A Hierarchical Approach to Generalized Category Discovery

Guided Cluster Aggregation: A Hierarchical Approach to Gener...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Otholt, Jona Meinel, Christoph Yang, Haojin Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9798350318920;9798350318937

Despite advances in image recognition, recognizing novel categories in unlabeled data remains challenging for machine learning methods, even though humans can perform this task with ease. A recently developed setting to tackle this problem is Generalized Category Discovery (GCD), in which the task is to, given a labeled dataset, classify an unlabeled dataset, where the unlabeled dataset contains both known classes and novel classes that do not appear in the labeled data. Existing GCD methods mostly focus on learning strong image representations, on which they then apply a clustering algorithm such as k-means. Despite obtaining good performance, they do not fully exploit the potential of the learned features due to the simple nature of the clustering mechanism. To address this issue, we make use of the fact that local neighborhoods in self-supervised feature spaces are highly homogeneous. We leverage this observation to develop Guided Cluster Aggregation (GCA), a hierarchical approach that first groups the data into small clusters of high purity, then aggregates them into larger clusters. Experiments show that GCA outperforms semi-supervised k-means in most cases, especially in fine-grained classification tasks. Code available at https://***/J-L-O/guidedcluster-aggregation.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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