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共 11281 条记录，以下是671-680 订阅

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QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

QAttn: Efficient GPU Kernels for mixed-precision Vision Tran...

引用

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

作者： Kluska, Piotr Castello, Adrian Scheidegger, Florian Malossi, A. Cristiano I. Quintana-Orti, Enrique S. IBM Res Europe Ruschlikon Switzerland Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9798350365474

vision Transformers have demonstrated outstanding performance in computer vision tasks. Nevertheless, this superior performance for large models comes at the expense of increasing memory usage for storing the parameters and intermediate activations. To accelerate model inference, in this work we develop and evaluate integer and mixed-precision kernels in Triton for the efficient execution of two fundamental building blocks of transformers -linear layer and attention- on graphics processing units (GPUs). On an NVIDIA A100 GPU, our kernel implementations of vision Transformers achieve a throughput speedup of up to 7x compared with reference kernels in PyTorch floating-point single precision (FP32). Additionally, the accuracy for the ViT Large model top-1 drops by less than one percent on the ImageNet1K classification task. We also observe up to 6x increased throughput by applying our kernels to the Segment Anything Model image encoder while keeping the mIOU close to the FP32 reference on the COCO2017 dataset for static and dynamic quantization. Furthermore, our kernels demonstrate improved speed to the TensorRT INT8 linear layer, and we improve the throughput of base FP16 (half precision) Triton attention on average by up to 19 +/- 4.01%. We have open-sourced the QAtnn framework, which is tightly integrated with the PyTorch quantization workflow https://***/IBM/qattn.

关键词： compression instance segmentation object classification quantization vision transformers

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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

Benchmarking Self-Supervised Learning on Diverse Pathology D...

引用

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

作者： Kang, Mingu Song, Heon Park, Seonwook Yoo, Donggeun Pereira, Sergio Lunit Inc Seoul South Korea

ISBN: (纸本)9798350301298

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning (SSL) has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements. We release the pre-trained model weights(1).

关键词： cell microscopy Medical and biological vision

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AVFormer: Injecting vision into Frozen Speech Models for Zero-Shot AV-ASR

AVFormer: Injecting Vision into Frozen Speech Models for Zer...

引用

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

作者： Seo, Paul Hongsuck Nagrani, Arsha Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain of interest). We present AVFormer, a simple method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We do this by (i) injecting visual embeddings into a frozen ASR model using lightweight trainable adaptors. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. (ii) We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively;and finally (iii) we show that our model achieves state of the art zero-shot results on three different AV-ASR benchmarks (How2, VisSpeech and Ego4D), while also crucially preserving decent performance on traditional audio-only speech recognition benchmarks (LibriSpeech). Qualitative results show that our model effectively leverages visual information for robust speech recognition.

关键词： Multi-modal learning

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ABCD : Arbitrary Bitwise Coefficient for De-quantization

ABCD : Arbitrary Bitwise Coefficient for De-quantization

引用

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

作者： Han, Woo Kyoung Lee, Byeonghun Park, Sang Hyun Jin, Kyong Hwan Daegu Gyeongbuk Inst Sci & Technol DGIST Daegu South Korea

ISBN: (纸本)9798350301298

Modern displays and contents support more than 8bits image and video. However, bit-starving situations such as compression codecs make low bit-depth (LBD) images (<8bits), occurring banding and blurry artifacts. Previous bit depth expansion (BDE) methods still produce unsatisfactory high bit-depth (HBD) images. To this end, we propose an implicit neural function with a bit query to recover de-quantized images from arbitrarily quantized inputs. We develop a phasor estimator to exploit the information of the nearest pixels. Our method shows superior performance against prior BDE methods on natural and animation images. We also demonstrate our model on YouTube UGC datasets for de-banding. Our source code is available at https://***/WooKyoungHan/ABCD

关键词： Low-level vision

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

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

作者： Gupta, Anshul Vuillecard, Pierre Farkhondeh, Arya Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350365474

Contextual cues related to a person's pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Gaze Following vision-Language Zero-Shot Evaluation

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Advancing Visual Grounding with Scene Knowledge: Benchmark and Method

Advancing Visual Grounding with Scene Knowledge: Benchmark a...

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

作者： Chen, Zhihong Zhang, Ruifei Song, Yibing Wan, Xiang Li, Guanbin Chinese Univ Hong Kong Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Tencent AI Lab Shenzhen Peoples R China Fudan Univ AI3 Inst Shanghai Peoples R China

ISBN: (纸本)9798350301298

Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understanding of the images and texts and their reasoning abilities over their joint space. However, most existing VG datasets are constructed using simple description texts, which do not require sufficient reasoning over the images and texts. This has been demonstrated in a recent study [27], where a simple LSTM-based text encoder without pretraining can achieve state-of-the-art performance on mainstream VG datasets. Therefore, in this paper, we propose a novel benchmark of Scene Knowledge-guided Visual Grounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, we propose two approaches to accept the triple-type input, where the former embeds knowledge into the image features before the image-query interaction;the latter leverages linguistic structure to assist in computing the image-text matching. We conduct extensive experiments to analyze the above methods and show that the proposed approaches achieve promising results but still leave room for improvement, including performance and interpretability. The dataset and code are available at https://***/zhjohnchan/SK-VG.

关键词： language reasoning vision

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Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate

Ranking Regularization for Critical Rare Classes: Minimizing...

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

作者： Mohammadi, Kiarash Zhao, He Zhai, Mengyao Tung, Frederick Borealis AI Toronto ON Canada Univ Montreal Mila Montreal PQ Canada

ISBN: (纸本)9798350301298

In many real-world settings, the critical class is rare and a missed detection carries a disproportionately high cost. For example, tumors are rare and a false negative diagnosis could have severe consequences on treatment outcomes;fraudulent banking transactions are rare and an undetected occurrence could result in significant losses or legal penalties. In such contexts, systems are often operated at a high true positive rate, which may require tolerating high false positives. In this paper, we present a novel approach to address the challenge of minimizing false positives for systems that need to operate at a high true positive rate. We propose a ranking-based regularization (RankReg) approach that is easy to implement, and show empirically that it not only effectively reduces false positives, but also complements conventional imbalanced learning losses. With this novel technique in hand, we conduct a series of experiments on three broadly explored datasets (CIFAR-10&100 and Melanoma) and show that our approach lifts the previous state-of-the-art performance by notable margins.

关键词： detection recognition: Categorization retrieval

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Adaptive Sparse Pairwise Loss for Object Re-Identification

Adaptive Sparse Pairwise Loss for Object Re-Identification

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

作者： Zhou, Xiao Zhong, Yujie Cheng, Zhen Liang, Fan Ma, Lin Tsinghua Univ BNRist Dept Automat Beijing 100084 Peoples R China Meituan Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong ReID network. Existing pairwise losses densely exploit each instance as an anchor and sample its triplets in a mini-batch. This dense sampling mechanism inevitably introduces positive pairs that share few visual similarities, which can be harmful to the training. To address this problem, we propose a novel loss paradigm termed Sparse Pairwise (SP) loss that only leverages few appropriate pairs for each class in a mini-batch, and empirically demonstrate that it is sufficient for the ReID tasks. Based on the proposed loss framework, we propose an adaptive positive mining strategy that can dynamically adapt to diverse intra-class variations. Extensive experiments show that SP loss and its adaptive variant AdaSP loss outperform other pairwise losses, and achieve state-of-the-art performance across several ReID benchmarks. Code is available at https://***/Astaxanthin/AdaSP.

关键词： detection recognition: Categorization retrieval

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GaitGCI: Generative Counterfactual Intervention for Gait recognition

GaitGCI: Generative Counterfactual Intervention for Gait Rec...

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

作者： Dou, Huanzhang Zhang, Pengyi Su, Wei Yu, Yunlong Lin, Yining Li, Xi Zhejiang Univ Coll Comp Sci Technol Hangzhou Peoples R China Zhejiang Univ Coll Informat Sci Elect Engn Hangzhou Peoples R China SupreMind Shanghai Peoples R China Zhejiang Univ Shanghai Inst Adv Study Hangzhou Peoples R China Shanghai AI Lab Shanghai Peoples R China Zhejiang Singapore Innovat & Joint Res Lab Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern;2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost;3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild).

关键词： Biometrics

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Potential Risk Localization via Weak Labeling out of Blind Spot

Potential Risk Localization via Weak Labeling out of Blind S...

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

作者： Shimomura, Kota Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

Achieving fully autonomous driving requires not only understanding the current surrounding conditions but also predicting how objects that could lead to potential risks may change in the future. Predicting potential risk regions, especially where pedestrians or vehicles might suddenly appear, is crucial for safe autonomous driving and accident avoidance. Constructing datasets annotated with potential risk regions is costly. Therefore, conventional methods have proposed blind spot estimation using depth maps or segmentation masks through automatic labeling. However, these methods are limited in applicability due to their reliance on camera parameters or point clouds. In this study, we propose a method to automatically generate labels from depth maps and segmentation masks and estimate potential risk regions in 2D. Our automatic labeling algorithm relies solely on images, making it applicable to all onboard camera datasets. To demonstrate the effectiveness of our approach, we define regions where pedestrians or vehicles might emerge from blind spots as potential risk regions and annotate them to create a new dataset extended with potential risk region annotations. Experiments using the Cityscapes Dataset show that weakly training with labels generated by our proposed method achieves equal or superior accuracy compared with supervised training with manually annotated ground truth (GT). Furthermore, experiments using the Mapillary Vistas Dataset and BDD100K Dataset demonstrate the versatility of our approach.

关键词： Advanced Driver-Assistance Systems Autonomous Driving computer vision Deep Learning

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