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ZEETAD: Adapting Pretrained vision-Language Model for Zero-Shot End-to-End Temporal Action Detection

ZEETAD: Adapting Pretrained Vision-Language Model for Zero-S...

引用

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

作者： Phan, Thinh Vo, Khoa Let, Duy Doretto, Gianfranco Adjeroht, Donald Le, Ngan Univ Arkansas AICV Lab Fayetteville AR 72701 USA FPT Software AI Ctr Hanoi Vietnam West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9798350318920;9798350318937

Temporal action detection (TAD) involves the localization and classification of action instances within untrimmed videos. While standard TAD follows fully supervised learning with closed-set setting on large training data, recent zero-shot TAD methods showcase the promising open-set setting by leveraging large-scale contrastive visual-language (ViL) pretrained models. However, existing zero-shot TAD methods have limitations on how to properly construct the strong relationship between two interdependent tasks of localization and classification and adapt ViL model to video understanding. In this work, we present ZEE-TAD, featuring two modules: dual-localization and zero-shot proposal classification. The former is a Transformer-based module that detects action events while selectively collecting crucial semantic embeddings for later recognition. The latter one, CLIP-based module, generates semantic embeddings from text and frame inputs for each temporal unit. Additionally, we enhance discriminative capability on unseen classes by minimally updating the frozen CLIP encoder with lightweight adapters. Extensive experiments on THUMOS14 and ActivityNet-1.3 datasets demonstrate our approach's superior performance in zero-shot TAD and effective knowledge transfer from ViL models to unseen action categories. Code is available at https://***/UARK-AICV/ZEETAD.

关键词： Algorithms Algorithms Video recognition and understanding vision + language and/or other modalities

来源：评论

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Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection

Attentive Prototypes for Source-free Unsupervised Domain Ada...

引用

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

作者： Hegde, Deepti Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350318920;9798350318937

3D object detection networks tend to be biased towards the data they are trained on. It has been demonstrated that the evaluation on datasets captured in different locations, conditions or with sensors of different specifications than that of the training (source) data results in a drop in model performance due to the domain gap with the test (or target) data. Current methods for adapting to the target domain data either assume access to source data during training, which may not be available due to privacy or memory concerns, or require a sequence of LiDAR frames as an input. We propose a single-frame approach for source-free, unsupervised domain adaptation of LiDAR-based 3D object detectors that uses class prototypes to mitigate the effect of pseudo-label noise. Addressing the limitations of traditional feature aggregation methods for prototype computation in the presence of noisy labels, we utilize a transformer module to identify outlier regions that correspond to incorrect, over-confident annotations, and compute an attentive class prototype. The losses associated with noisy pseudo-labels are down-weighed in the process of self-training. We demonstrate our approach on two recent object detectors and show that our method outperforms recent source-free domain adaptation works as well as those that leverage source information during training. The code will be made available.

关键词： 3D computer vision Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Masked Event Modeling: Self-Supervised Pretraining for Event Cameras

Masked Event Modeling: Self-Supervised Pretraining for Event...

引用

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

作者： Klenk, Simon Bonello, David Koestler, Lukas Araslanov, Nikita Cremers, Daniel Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany

ISBN: (纸本)9798350318920;9798350318937

Event cameras asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range. However, annotation of event data is a costly and laborious process, which limits the use of deep learning methods for classification and other semantic tasks with the event modality. To reduce the dependency on labeled event data, we introduce Masked Event Modeling (MEM), a self-supervised framework for events. Our method pretrains a neural network on unlabeled events, which can originate from any event camera recording. Subsequently, the pretrained model is finetuned on a downstream task, leading to a consistent improvement of the task accuracy. For example, our method reaches state-of-the-art classification accuracy across three datasets, N-ImageNet, N-Cars, and N-Caltech101, increasing the top-1 accuracy of previous work by significant margins. When tested on real-world event data, MEM is even superior to supervised RGB-based pretraining. The models pretrained with MEM are also label-efficient and generalize well to the dense task of semantic image segmentation.

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

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OOD Aware Supervised Contrastive Learning

OOD Aware Supervised Contrastive Learning

引用

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

作者： Seifi, Soroush Reino, Daniel Olmeda Chumerin, Nikolay Aljundi, Rahaf Toyota Motor Europe Leuven Belgium

ISBN: (纸本)9798350318920;9798350318937

Out-of-Distribution (OOD) detection is a crucial problem for the safe deployment of machine learning models identifying samples that fall outside of the training distribution, i.e. in-distribution data (ID). Most OOD works focus on the classification models trained with Cross Entropy (CE) and attempt to fix its inherent issues. In this work we leverage powerful representation learned with Supervised Contrastive (SupCon) training and propose a holistic approach to learn a classifier robust to OOD data. We extend SupCon loss with two additional contrast terms. The first term pushes auxiliary OOD representations away from ID representations without imposing any constraints on similarities among auxiliary data. The second term pushes OOD features far from the existing class prototypes, while pushing ID representations closer to their corresponding class prototype. When auxiliary OOD data is not available, we propose feature mixing techniques to efficiently generate pseudo-OOD features. Our solution is simple and efficient and acts as a natural extension of the closed-set supervised contrastive representation learning. We compare against different OOD detection methods on the common benchmarks and show state-of-the-art results.

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

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Offline-to-Online Knowledge Distillation for Video Instance Segmentation

Offline-to-Online Knowledge Distillation for Video Instance ...

引用

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

作者： Kim, Hojin Lee, Seunghun Kang, Hyeon Im, Sunghoon SI Analyt Daejeon South Korea DGIST Dept Elect Engn Comp Sci Daegu South Korea DGIST Daegu South Korea

ISBN: (纸本)9798350318920;9798350318937

In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model to an online model for consistent prediction. Unlike previous methods that have adopted either an online or offline model, our single online model takes advantage of both models by distilling offline knowledge. To transfer knowledge correctly, we propose query filtering and association (QFA), which filters irrelevant queries to exact instances. Our KD with QFA increases the robustness of feature matching by encoding object-centric features from a single frame supplemented by long-range global information. We also propose a simple data augmentation scheme for knowledge distillation in the VIS task that fairly transfers the knowledge of all classes into the online model. Extensive experiments show that our method significantly improves the performance in video instance segmentation, especially for challenging datasets, including long, dynamic sequences. Our method also achieves state-of-the-art performance on YTVIS-21, YTVIS-22, and OVIS datasets, with mAP scores of 46.1%, 43.6%, and 31.1%, respectively.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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USDN: A Unified Sample-wise Dynamic Network with Mixed-Precision and Early-Exit

USDN: A Unified Sample-wise Dynamic Network with Mixed-Preci...

引用

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

作者： Jeon, Ji-Ye Nguyen, Xuan Truong Ryu, Soojung Lee, Hyuk-Jae Seoul Natl Univ Seoul South Korea SK Telecom Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

To reduce computation in deep neural network inference, a promising approach is to design a network with multiple internal classifiers (ICs) and adaptively select an execution path based on the complexity of a given input. However, quantizing an input-adaptive network, a must-do task for network deployment on edge devices, is a non-trivial task due to jointly allocating its computation budget along with network layers and IC locations. In this paper, we propose Unified Sample-wise Dynamic Network (USDN) with a mixed-precision and early-exit framework that obtains both the optimal location of ICs and layer-wise bit configurations under a given computation budget. The proposed USDN comprises multiple groups of layers, with each group representing a varying degree of complexity for input samples. Experimental results demonstrate that our approach reduces computational cost of the previous work by 12.78% while achieving higher accuracy on ImageNet dataset.

关键词： Algorithms Algorithms and algorithms Applications Embedded sensing / real-time techniques formulations Image recognition and understanding Machine learning architectures

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Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud

Registered and Segmented Deformable Object Reconstruction fr...

引用

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

作者： Henrichi, Pit Gyenes, Balazs Scheikli, Paul Maria Neumann, Gerhard Mathis-Ullrich, Franziska FAU Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany

ISBN: (纸本)9798350318920;9798350318937

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a system that can recognize and locate these segments in sensor data of deformed real world objects. This is normally done using deformable object registration, which is problem specific and complex to tune. Recent methods utilize neural occupancy functions to improve deformable object registration by registering to an object reconstruction. Going one step further, we propose a system that in addition to reconstruction learns segmentation of the reconstructed object. As the resulting output already contains the information about the segments, we can skip the registration process. Tested on a variety of deformable objects in simulation and the real world, we demonstrate that our method learns to robustly find these segments. We also introduce a simple sampling algorithm to generate better training data for occupancy learning.

关键词： 3D computer vision Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Time to Shine: Fine-Tuning Object Detection Models with Synthetic Adverse Weather Images

Time to Shine: Fine-Tuning Object Detection Models with Synt...

引用

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

作者： Rothmeier, Thomas Huber, Werner Knoll, Alois C. Univ Appl Sci Ingolstadt Ingolstadt Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9798350318920;9798350318937

The detection of vehicles, pedestrians, and obstacles plays an important role in the decision-making process of autonomous vehicles. While existing methods achieve high detection accuracy under good environmental conditions, they often fail in adverse weather conditions due to limited visibility, blurred contours, and low contrast. These "edge-case" scenarios are not well represented in existing datasets and are not handled properly by object detection algorithms. In our work, we propose a novel approach to synthesising photorealistic and highly diverse scenarios that can be used to fine-tune object detection algorithms in adverse weather conditions such as snow, fog, and rain. The approach uses the Midjourney text-to-image model to create accurate synthetic images of desired weather conditions. Our experiments show that training with our dataset significantly improves detection accuracy in harsh weather conditions. Our results are compared to baseline models and models fine-tuned on augmented clear weather images.

关键词： Algorithms Algorithms Datasets and evaluations Image recognition and understanding

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Unsupervised Graphic Layout Grouping with Transformers

Unsupervised Graphic Layout Grouping with Transformers

引用

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

作者： Zhu, Jialiang Huang, Danqing Wang, Chunyu Cheng, Mingxi Li, Ji Hu, Han Geng, Xin Guo, Baining Southeast Univ Nanjing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Graphic design conveys messages through the combination of text, images and other visual elements. Unstructured designs such as overloaded social media graphics may fail to communicate their intended messages effectively. To address this issue, layout grouping offers a solution by organizing design elements into perceptual groups. While most methods rely on heuristic Gestalt principles, they often lack the context modeling ability needed to handle complex layouts. In this work, we reformulate the layout grouping task as a set prediction problem. It uses Transformers to learn a set of group tokens at various hierarchies, enabling it to reason the membership of the elements more effectively. The self-attention mechanism in Transformers boosts its context modeling ability, which enables it to handle complex layouts more accurately. To reduce annotation costs, we also propose an unsupervised learning strategy that pre-trains on noisy pseudo-labels induced by a novel heuristic algorithm. This approach then bootstraps to self-refine the noisy labels, further improving the accuracy of our model. Our extensive experiments demonstrate the effectiveness of our method, which outperforms existing state-of-the-art approaches in terms of accuracy and efficiency.

关键词： accountable Algorithms Algorithms ethical computer vision Explainable fair Image recognition and understanding privacy-preserving

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Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector

Debiasing, calibrating, and improving Semi-supervised Learni...

引用

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

作者： Nguyen, Khanh-Binh Sungkyunkwan Univ Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Recent studies on semi-supervised learning (SSL) have achieved great success. Despite their promising performance, current state-of-the-art methods tend toward increasingly complex designs at the cost of introducing more network components and additional training procedures. In this paper, we propose a simple method named Ensemble Projectors Aided for Semi-supervised Learning (EPASS), which focuses mainly on improving the learned embeddings to boost the performance of the existing contrastive joint-training semi-supervised learning frameworks. Unlike standard methods, where the learned embeddings from one projector are stored in memory banks to be used with contrastive learning, EPASS stores the ensemble embeddings from multiple projectors in memory banks. As a result, EPASS improves generalization, strengthens feature representation, and boosts performance. For instance, EPASS improves strong baselines for semi-supervised learning by 39.47%/31.39%/24.70% top-1 error rate, while using only 100k/1%/10% of labeled data for SimMatch, and achieves 40.24%/32.64%/25.90% top-1 error rate for CoMatch on the ImageNet dataset. These improvements are consistent across methods, network architectures, and datasets, proving the general effectiveness of the proposed methods. Code is available at https://***/beandkay/EPASS.

关键词： accountable Algorithms Algorithms Algorithms and algorithms ethical computer vision Explainable fair formulations Image recognition and understanding Machine learning architectures privacy-preserving

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