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Universal Instance Perception as Object Discovery and Retrieval

Universal Instance Perception as Object Discovery and Retrie...

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

作者： Yan, Bin Jiang, Yi Wu, Jiannan Wang, Dong Luo, Ping Yuan, Zehuan Lu, Huchuan Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China ByteDance Beijing Peoples R China Univ Hong Kong Hong Kong Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks. In this work, we present a universal instance perception model of the next generation, termed UNINEXT. UNINEXT reformulates diverse instance perception tasks into a unified object discovery and retrieval paradigm and can flexibly perceive different types of objects by simply changing the input prompts. This unified formulation brings the following benefits: (1) enormous data from different tasks and label vocabularies can be exploited for jointly training general instance-level representations, which is especially beneficial for tasks lacking in training data. (2) the unified model is parameter-efficient and can save redundant computation when handling multiple tasks simultaneously. UNINEXT shows superior performance on 20 challenging benchmarks from 10 instance-level tasks including classical image-level tasks (object detection and instance segmentation), vision-and-language tasks (referring expression comprehension and segmentation), and six video-level object tracking tasks. Code is available at https://***/MasterBin-IIAU/UNINEXT.

关键词： detection recognition: Categorization retrieval

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Deep Deterministic Uncertainty: A New Simple Baseline

Deep Deterministic Uncertainty: A New Simple Baseline

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

作者： Mukhoti, Jishnu Kirsch, Andreas van Amersfoort, Joost Torr, Philip H. S. Gal, Yarin Univ Oxford Dept Comp Sci OATML Oxford England Univ Oxford Dept Engn Sci Torr Vis Grp Oxford England

ISBN: (纸本)9798350301298

Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass uncertainty approaches, DUQ and SNGP, and examine whether they mainly rely on a well-regularized feature space. Crucially, without using their more complex methods for estimating uncertainty, we find that a single softmax neural net with such a regularized feature-space, achieved via residual connections and spectral normalization, outperforms DUQ and SNGP's epistemic uncertainty predictions using simple Gaussian Discriminant Analysis post-training as a separate feature-space density estimator-without fine-tuning on OoD data, feature ensembling, or input pre-procressing. Our conceptually simple Deep Deterministic Uncertainty (DDU) baseline can also be used to disentangle aleatoric and epistemic uncertainty and performs as well as Deep Ensembles, the stateof-the art for uncertainty prediction, on several OoD benchmarks (CIFAR-10/100 vs SVHN/Tiny-ImageNet, ImageNet vs ImageNet-O), active learning settings across different model architectures, as well as in large scale vision tasks like semantic segmentation, while being computationally cheaper.

关键词： accountability ethics in vision fairness privacy Transparency

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Teaching Structured vision & Language Concepts to vision & Language Models

Teaching Structured Vision & Language Concepts to Vision & L...

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

作者： Doveh, Sivan Arbelle, Assaf Harary, Sivan Schwartz, Eli Herzig, Roei Giryes, Raja Feris, Rogerio Panda, Rameswar Ullman, Shimon Karlinsky, Leonid IBM Res Yorktown Hts NY 10598 USA Weizmann Inst Sci Rehovot Israel Tel Aviv Univ Tel Aviv Israel MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a challenge. We introduce the collective notion of Structured vision & Language Concepts (SVLC) which includes object attributes, relations, and states which are present in the text and visible in the image. Recent studies have shown that even the best VL models struggle with SVLC. A possible way of fixing this issue is by collecting dedicated datasets for teaching each SVLC type, yet this might be expensive and time-consuming. Instead, we propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs that makes more effective use of existing VL pre-training datasets and does not require any additional data. While automatic understanding of image structure still remains largely unsolved, language structure is much better modeled and understood, allowing for its effective utilization in teaching VL models. In this paper, we propose various techniques based on language structure understanding that can be used to manipulate the textual part of off-the-shelf paired VL datasets. VL models trained with the updated data exhibit a significant improvement of up to 15% in their SVLC understanding with only a mild degradation in their zero-shot capabilities both when training from scratch or fine-tuning a pre-trained model. Our code and pretrained models are available at: https://***/SivanDoveh/TSVLC

关键词： Multi-modal learning

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Event-Based Frame Interpolation with Ad-hoc Deblurring

Event-Based Frame Interpolation with Ad-hoc Deblurring

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

作者： Sun, Lei Sakaridis, Christos Liang, Jingyun Sun, Peng Cao, Jiezhang Zhang, Kai Jiang, Qi Wang, Kaiwei Van Gool, Luc Zhejiang Univ Hangzhou Peoples R China Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolated, depending on the length of the exposure time of the frames and the speed of the motion, and assume either that the input video is sharp, restricting themselves to frame interpolation, or that it is blurry, including an explicit, separate deblurring stage before interpolation in their pipeline. We instead propose a general method for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that naturally incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. In addition, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments on the standard GoPro benchmark and on our dataset show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring and the joint task of interpolation and deblurring. Our code and dataset are available at https://***/AHupuJR/REFID.

关键词： Low-level vision

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Distilling Self-Supervised vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation

Distilling Self-Supervised Vision Transformers for Weakly-Su...

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

作者： Kang, Dahyun Koniusz, Piotr Cho, Minsu Murray, Naila Meta AI Menlo Pk CA 94025 USA POSTECH Pohang South Korea CSIRO Data61 Canberra Australia Australian Natl Univ Canberra Australia

ISBN: (纸本)9798350301298

We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions through separate task heads. Our model is able to effectively learn to perform classification and segmentation in the absence of pixel-level labels during training, using only image-level labels. To do this it uses attention maps, created from tokens generated by the self-supervised ViT backbone, as pixel-level pseudo-labels. We also explore a practical setup with "mixed" supervision, where a small number of training images contains ground-truth pixel-level labels and the remaining images have only image-level labels. For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels. Experiments on Pascal-5(i) and COCO-20(i) demonstrate significant performance gains in a variety of supervision settings, and in particular when little-to-no pixel-level labels are available.

关键词： continual low-shot meta or long-tail learning Transfer

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N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image ...

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

作者： Choi, Haram Lee, Jeongmin Yang, Jihoon Sogang Univ Dept Comp Sci & Engn Seoul South Korea LG Innotek Seoul South Korea

ISBN: (纸本)9798350301298

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://***/rami0205/NGramSwin.

关键词： Low-level vision

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Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies

Collaborative Noisy Label Cleaner: Learning Scene-aware Trai...

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

作者： Gan, Bei Shu, Xiujun Qiao, Ruizhi Wu, Haoqian Chen, Keyu Li, Hanjun Ren, Bo Tencent YouTu Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations. (2) Besides previous supervised or unsupervised settings, some existing video corpora can be useful, e.g., trailers, but they are often noisy and incomplete to cover the full highlights. In this work, we study a more practical and promising setting, i.e., reformulating highlight detection as "learning with noisy labels". This setting does not require time-consuming manual annotations and can fully utilize existing abundant video corpora. First, based on movie trailers, we leverage scene segmentation to obtain complete shots, which are regarded as noisy labels. Then, we propose a Collaborative noisy Label Cleaner (CLC) framework to learn from noisy highlight moments. CLC consists of two modules: augmented cross-propagation (ACP) and multi-modality cleaning (MMC). The former aims to exploit the closely related audio-visual signals and fuse them to learn unified multi-modal representations. The latter aims to achieve cleaner highlight labels by observing the changes in losses among different modalities. To verify the effectiveness of CLC, we further collect a large-scale highlight dataset named MovieLights. Comprehensive experiments on MovieLights and YouTube Highlights datasets demonstrate the effectiveness of our approach. Code has been made available at: https://***/TencentYoutuResearch/HighlightDetection-CLC.

关键词： vision applications and systems

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VLPD: Context-Aware Pedestrian Detection via vision-Language Semantic Self-Supervision

VLPD: Context-Aware Pedestrian Detection via Vision-Language...

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

作者： Liu, Mengyin Jiang, Jie Zhu, Chao Yin, Xu-Cheng Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Tencent Data Platform Dept Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are inadequate, thus how to fully utilize more explicit and semantic contexts becomes a key problem. Meanwhile, previous context-aware pedestrian detectors either only learn latent contexts with visual clues, or need laborious annotations to obtain explicit and semantic contexts. Therefore, we propose in this paper a novel approach via vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations. Firstly, we propose a self-supervised vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models. Furthermore, a self-supervised Prototypical Semantic Contrastive (PSC) learning method is proposed to better discriminate pedestrians and other classes, based on more explicit and semantic contexts obtained from VLS. Extensive experiments on popular benchmarks show that our proposed VLPD achieves superior performances over the previous state-of-the-arts, particularly under challenging circumstances like small scale and heavy occlusion. Code is available at https://***/lmy98129/VLPD.

关键词： detection recognition: Categorization retrieval

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Search-Map-Search: A Frame Selection Paradigm for Action recognition

Search-Map-Search: A Frame Selection Paradigm for Action Rec...

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

作者： Zhao, Mingjun Yu, Yakun Wang, Xiaoli Yang, Lei Niu, Di Univ Alberta Edmonton AB Canada Tencent Shenzhen Peoples R China Tencent Edmonton AB Canada

ISBN: (纸本)9798350301298

Despite the success of deep learning in video understanding tasks, processing every frame in a video is computationally expensive and often unnecessary in real-time applications. Frame selection aims to extract the most informative and representative frames to help a model better understand video content. Existing frame selection methods either individually sample frames based on per-frame importance prediction, without considering interaction among frames, or adopt reinforcement learning agents to find representative frames in succession, which are costly to train and may lead to potential stability issues. To overcome the limitations of existing methods, we propose a Search-Map-Search learning paradigm which combines the advantages of heuristic search and supervised learning to select the best combination of frames from a video as one entity. By combining search with learning, the proposed method can better capture frame interactions while incurring a low inference overhead. Specifically, we first propose a hierarchical search method conducted on each training video to search for the optimal combination of frames with the lowest error on the downstream task. A feature mapping function is then learned to map the frames of a video to the representation of its target optimal frame combination. During inference, another search is performed on an unseen video to select a combination of frames whose feature representation is close to the projected feature representation. Extensive experiments based on several action recognition benchmarks demonstrate that our frame selection method effectively improves performance of action recognition models, and significantly outperforms a number of competitive baselines.

关键词： Video: Action and event understanding

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AltFreezing for More General Video Face Forgery Detection

AltFreezing for More General Video Face Forgery Detection

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

作者： Wang, Zhendong Bao, Jianmin Zhou, Wengang Wangi, Weilun Li, Hougiang Univ Sci & Technol China EELS Dept CAS Key Lab GIPAS Langfang Hebei Peoples R China Microsoft Res Asia Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity). They may experience significant performance degradation when facing out-domain artifacts. In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection. A simple idea is to leverage a spatiotemporal model (3D ConvNet). However, we find that it may easily rely on one type of artifact and ignore the other. To address this issue, we present a novel training strategy called AltFreezing for more general face forgery detection. The AltFreezing aims to encourage the model to detect both spatial and temporal artifacts. It divides the weights of a spatiotemporal network into two groups: spatial-related and temporal-related. Then the two groups of weights are alternately frozen during the training process so that the model can learn spatial and temporal features to distinguish real or fake videos. Furthermore, we introduce various video-level data augmentation methods to improve the generalization capability of the forgery detection model. Extensive experiments show that our framework outperforms existing methods in terms of generalization to unseen manipulations and datasets.

关键词： accountability ethics in vision fairness privacy Transparency

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