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Test of Time: Instilling Video-Language Models with a Sense of Time

Test of Time: Instilling Video-Language Models with a Sense ...

引用

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

作者： Bagad, Piyush Tapaswi, Makarand Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798350301298

Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language models to have a sense of time. In this paper, we consider a specific aspect of temporal understanding: consistency of time order as elicited by before/after relations. We establish that seven existing video-language models struggle to understand even such simple temporal relations. We then question whether it is feasible to equip these foundational models with temporal awareness without re-training them from scratch. Towards this, we propose a temporal adaptation recipe on top of one such model, VideoCLIP, based on post-pretraining on a small amount of video-text data. We conduct a zero-shot evaluation of the adapted models on six datasets for three downstream tasks which require varying degrees of time awareness. We observe encouraging performance gains especially when the task needs higher time awareness. Our work serves as a first step towards probing and instilling a sense of time in existing video-language models without the need for data and compute-intense training from scratch.

关键词： language reasoning vision

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Multimodal Attack Detection for Action recognition Models

Multimodal Attack Detection for Action Recognition Models

引用

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

作者： Mumcu, Furkan Yilmaz, Yasin Univ S Florida 4202 E Fowler Ave Tampa FL 33620 USA

ISBN: (纸本)9798350365474

Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show that action recognition models can be breached in many ways. Hence using these models in practice raises significant security concerns. However, there are very few works which focus on defending against or detecting attacks. In this work, we propose a novel universal detection method which is compatible with any action recognition model. In our extensive experiments, we show that our method consistently detects various attacks against different target models with high true positive rates while satisfying very low false positive rates. Tested against four state-of-the-art attacks targeting four action recognition models, the proposed detector achieves an average AUC of 0.911 over 16 test cases while the best performance achieved by the existing detectors is 0.645 average AUC. This 41.2% improvement is enabled by the robustness of the proposed detector to varying attack methods and target models. The lowest AUC achieved by our detector across the 16 test cases is 0.837 while the competing detector's performance drops as low as 0.211. We also show that the proposed detector is robust to varying attack strengths. In addition, we analyze our method's real-time performance with different hardware setups to demonstrate its potential as a practical defense mechanism.

关键词： Action recognition Models Adversarial machine learning attacks Attack detection

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How can objects help action recognition?

How can objects help action recognition?

引用

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

作者： Zhou, Xingyi Arnab, Anurag Sun, Chen Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Current state-of-the-art video models process a video clip as a long sequence of spatio-temporal tokens. However, they do not explicitly model objects, their interactions across the video, and instead process all the tokens in the video. In this paper, we investigate how we can use knowledge of objects to design better video models, namely to process fewer tokens and to improve recognition accuracy. This is in contrast to prior works which either drop tokens at the cost of accuracy, or increase accuracy whilst also increasing the computation required. First, we propose an object-guided token sampling strategy that enables us to retain a small fraction of the input tokens with minimal impact on accuracy. And second, we propose an object-aware attention module that enriches our feature representation with object information and improves overall accuracy. Our resulting model, ObjectViViT, achieves better performance when using fewer tokens than strong baselines. In particular, we match our baseline with 30%, 40%, and 60% of the input tokens on SomethingElse, Something-something v2, and Epic-Kitchens, respectively. When we use Object-ViViT to process the same number of tokens as our baseline, we improve by 0.6 to 4.2 points on these datasets.

关键词： Video: Action and event understanding

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Decoupling MaxLogit for Out-of-Distribution Detection

Decoupling MaxLogit for Out-of-Distribution Detection

引用

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

作者： Zhang, Zihan Xiang, Xiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

ISBN: (纸本)9798350301298

In machine learning, it is often observed that standard training outputs anomalously high confidence for both indistribution (ID) and out-of-distribution (OOD) data. Thus, the ability to detect OOD samples is critical to the model deployment. An essential step for OOD detection is post-hoc scoring. MaxLogit is one of the simplest scoring functions which uses the maximum logits as OOD score. To provide a new viewpoint to study the logit-based scoring function, we reformulate the logit into cosine similarity and logit norm and propose to use MaxCosine and MaxNorm. We empirically find that MaxCosine is a core factor in the effectiveness of MaxLogit. And the performance of MaxLogit is encumbered by MaxNorm. To tackle the problem, we propose the Decoupling MaxLogit (DML) for flexibility to balance MaxCosine and MaxNorm. To further embody the core of our method, we extend DML to DML+ based on the new insights that fewer hard samples and compact feature space are the key components to make logit-based methods effective. We demonstrate the effectiveness of our logit-based OOD detection methods on CIFAR-10, CIFAR-100 and ImageNet and establish state-of-the-art performance.

关键词： detection recognition: Categorization retrieval

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Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction

Joint Appearance and Motion Learning for Efficient Rolling S...

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

作者： Fan, Bin Mao, Yuxin Dai, Yuchao Wan, Zhexiong Liu, Qi Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9798350301298

Rolling shutter correction (RSC) is becoming increasingly popular for RS cameras that are widely used in commercial and industrial applications. Despite the promising performance, existing RSC methods typically employ a two-stage network structure that ignores intrinsic information interactions and hinders fast inference. In this paper, we propose a single-stage encoder-decoder-based network, named JAMNet, for efficient RSC. It first extracts pyramid features from consecutive RS inputs, and then simultaneously refines the two complementary information (i.e., global shutter appearance and undistortion motion field) to achieve mutual promotion in a joint learning decoder. To inject sufficient motion cues for guiding joint learning, we introduce a transformer-based motion embedding module and propose to pass hidden states across pyramid levels. Moreover, we present a new data augmentation strategy "vertical flip + inverse order" to release the potential of the RSC datasets. Experiments on various benchmarks show that our approach surpasses the state-of-the-art methods by a large margin, especially with a 4.7 dB PSNR leap on real-world RSC. Code is available at https://***/GitCVfb/JAMNet.

关键词： Low-level vision

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DoNet: Deep De-overlapping Network for Cytology Instance Segmentation

DoNet: Deep De-overlapping Network for Cytology Instance Seg...

引用

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

作者： Jiang, Hao Zhang, Rushan Zhou, Yanning Wang, Yumeng Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tencent AI Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei. In this work, we proposed a De-overlapping Network (DoNet) in a decompose-and-recombined strategy. A Dual-path Region Segmentation Module (DRM) explicitly decomposes the cell clusters into intersection and complement regions, followed by a Semantic Consistency-guided Recombination Module (CRM) for integration. To further introduce the containment relationship of the nucleus in the cytoplasm, we design a Mask-guided Region Proposal Strategy (MRP) that integrates the cell attention maps for inner-cell instance prediction. We validate the proposed approach on ISBI2014 and CPS datasets. Experiments show that our proposed DoNet significantly outperforms other state-of-the-art (SOTA) cell instance segmentation methods. The code is available at https://***/DeepDoNet/DoNet.

关键词： cell microscopy Medical and biological vision

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AIGeN: An Adversarial Approach for Instruction Generation in VLN

AIGeN: An Adversarial Approach for Instruction Generation in...

引用

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

作者： Rawal, Niyati Bigazzi, Roberto Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9798350365474

In the last few years, the research interest in vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance by exploiting synthetic training data. In this work, we propose AIGeN, a novel architecture inspired by Generative Adversarial Networks (GANs) that produces meaningful and well-formed synthetic instructions to improve navigation agents' performance. The model is composed of a Transformer decoder (GPT-2) and a Transformer encoder (BERT). During the training phase, the decoder generates sentences for a sequence of images describing the agent's path to a particular point while the encoder discriminates between real and fake instructions. Experimentally, we evaluate the quality of the generated instructions and perform extensive ablation studies. Additionally, we generate synthetic instructions for 217K trajectories using AIGeN on Habitat-Matterport 3D Dataset (HM3D) and show an improvement in the performance of an off-the-shelf VLN method. The validation analysis of our proposal is conducted on REVERIE and R2R and highlights the promising aspects of our proposal, achieving state-of-the-art performance.

关键词： Generative Adversarial Networks Text Generation vision-and-Language Navigation

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DVMSR: Distillated vision Mamba for Efficient Super-Resolution

DVMSR: Distillated Vision Mamba for Efficient Super-Resoluti...

引用

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

作者： Lei, Xiaoyan Zhang, Wenlong Cao, Weifeng Zhengzhou Univ Light Ind Zhengzhou Peoples R China HongKong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Efficient Image Super-Resolution (SR) aims to accelerate SR network inference by minimizing computational complexity and network parameters while preserving performance. Existing state-of-the-art Efficient Image Super-Resolution methods are based on convolutional neural networks. Few attempts have been made with Mamba to harness its long-range modeling capability and efficient computational complexity, which have shown impressive performance on high-level vision tasks. In this paper, we propose DVMSR, a novel lightweight Image SR network that incorporates vision Mamba and a distillation strategy. The network of DVMSR consists of three modules: feature extraction convolution, multiple stacked Residual State Space Blocks (RSSBs), and a reconstruction module. Specifically, the deep feature extraction module is composed of several residual state space blocks (RSSB), each of which has several vision Mamba Moudles(ViMM) together with a residual connection. To achieve efficiency improvement while maintaining comparable performance, we employ a distillation strategy to the vision Mamba network for superior performance. Specifically, we leverage the rich representation knowledge of teacher network as additional supervision for the output of lightweight student networks. Extensive experiments have demonstrated that our proposed DVMSR can outperform state-of-the-art efficient SR methods in terms of model parameters while maintaining the performance of both PSNR and SSIM. The source code is available at https://***/nathan66666/***

关键词： Efficient Image Super-Resolution vision Mamba

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Zero-Shot Dual-Lens Super-Resolution

Zero-Shot Dual-Lens Super-Resolution

引用

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

作者： Xu, Ruikang Yao, Mingde Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

The asymmetric dual-lens configuration is commonly available on mobile devices nowadays, which naturally stores a pair of wide-angle and telephoto images of the same scene to support realistic super-resolution (SR). Even on the same device, however, the degradation for modeling realistic SR is image-specific due to the unknown acquisition process (e.g., tiny camera motion). In this paper, we propose a zero-shot solution for dual-lens SR (ZeDuSR), where only the dual-lens pair at test time is used to learn an image-specific SR model. As such, ZeDuSR adapts itself to the current scene without using external training data, and thus gets rid of generalization difficulty. However, there are two major challenges to achieving this goal: 1) dual-lens alignment while keeping the realistic degradation, and 2) effective usage of highly limited training data. To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair. Extensive experiments validate the superiority of ZeDuSR over existing solutions on both synthesized and realworld dual-lens datasets. The implementation code is available at https://***/XrKang/ZeDuSR.

关键词： Low-level vision

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Deep Random Projector: Accelerated Deep Image Prior

Deep Random Projector: Accelerated Deep Image Prior

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

作者： Li, Taihui Wang, Hengkang Zhuang, Zhong Sun, Ju Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA Univ Minnesota Elect & Comp Engn Minneapolis MN USA

ISBN: (纸本)9798350301298

Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data. However, the resulting optimization process is often very slow, inevitably hindering DIP's practical usage for time-sensitive scenarios. In this paper, we focus on IR, and propose two crucial modifications to DIP that help achieve substantial speedup: 1) optimizing the DIP seed while freezing randomly-initialized network weights, and 2) reducing the network depth. In addition, we reintroduce explicit priors, such as sparse gradient prior-encoded by total-variation regularization, to preserve the DIP peak performance. We evaluate the proposed method on three IR tasks, including image denoising, image super-resolution, and image inpainting, against the original DIP and variants, as well as the competing metaDIP that uses meta-learning to learn good initializers with extra data. Our method is a clear winner in obtaining competitive restoration quality in a minimal amount of time. Our code is available at https://***/sun-umn/Deep-Random-Projector.

关键词： vision applications and systems

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