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共 11891 条记录，以下是1011-1020 订阅

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Prefix Conditioning Unifies Language and Label Supervision

Prefix Conditioning Unifies Language and Label Supervision

引用

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

作者： Saito, Kuniaki Sohn, Kihyuk Zhang, Xiang Li, Chun-Liang Lee, Chen-Yu Saenko, Kate Pfister, Tomas Boston Univ Boston MA 02215 USA Google Cloud AI Res Mountain View CA USA Google Res Mountain View CA USA MIT IBM Watson Iab Cambridge MA USA

ISBN: (纸本)9798350301298

Pretraining visual models on web-scale image-caption datasets has recently emerged as a powerful alternative to traditional pretraining on image classification data. Image-caption datasets are more "open-domain", containing broader scene types and vocabulary words, and result in models that have strong performance in few- and zero-shot recognition tasks. However large-scale classification datasets can provide fine-grained categories with a balanced label distribution. In this work, we study a pretraining strategy that uses both classification and caption datasets to unite their complementary benefits. First, we show that naively unifying the datasets results in suboptimal performance in downstream zero-shot recognition tasks, as the model is affected by dataset bias: the coverage of image domains and vocabulary words is different in each dataset. We address this problem with novel Prefix Conditioning, a simple yet effective method that helps disentangle dataset biases from visual concepts. This is done by introducing prefix tokens that inform the language encoder of the input data type (e.g., classification vs caption) at training time. Our approach allows the language encoder to learn from both datasets while also tailoring feature extraction to each dataset. Prefix conditioning is generic and can be easily integrated into existing VL pretraining objectives, such as CLIP or UniCL. In experiments, we show that it improves zero-shot image recognition and robustness to image-level distribution shift.

关键词： language reasoning vision

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Weak-shot Object Detection through Mutual Knowledge Transfer

Weak-shot Object Detection through Mutual Knowledge Transfer

引用

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

作者： Du, Xuanyi Wan, Weitao Sun, Chong Li, Chen WeChat Hong Kong Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Weak-shot Object Detection methods exploit a fully-annotated source dataset to facilitate the detection performance on the target dataset which only contains image-level labels for novel categories. To bridge the gap between these two datasets, we aim to transfer the object knowledge between the source (S) and target (T) datasets in a bi-directional manner. We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset. By jointly optimizing the classification loss and the proposed KT loss, the multiple instance learning module effectively learns to classify object proposals into novel categories in the T dataset with the transferred knowledge from base categories in the S dataset. Noticing the predicted boxes on the T dataset can be regarded as an extension for the original annotations on the S dataset to refine the proposal generator in return, we further propose a novel Consistency Filtering (CF) method to reliably remove inaccurate pseudo labels by evaluating the stability of the multiple instance learning module upon noise injections. Via mutually transferring knowledge between the S and T datasets in an iterative manner, the detection performance on the target dataset is significantly improved. Extensive experiments on public benchmarks validate that the proposed method performs favourably against the state-of-the-art methods without increasing the model parameters or inference computational complexity.

关键词： detection recognition: Categorization retrieval

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Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training

Fair Scratch Tickets: Finding Fair Sparse Networks without W...

引用

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

作者： Tang, Pengwei Yao, Wei Li, Zhicong Liu, Yong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent studies suggest that computer vision models come at the risk of compromising fairness. There are extensive works to alleviate unfairness in computer vision using pre-processing, in-processing, and post-processing methods. In this paper, we lead a novel fairness-aware learning paradigm for in-processing methods through the lens of the lottery ticket hypothesis (LTH) in the context of computer vision fairness. We randomly initialize a dense neural network and find appropriate binary masks for the weights to obtain fair sparse subnetworks without any weight training. Interestingly, to the best of our knowledge, we are the first to discover that such sparse subnetworks with inborn fairness exist in randomly initialized networks, achieving an accuracy-fairness trade-off comparable to that of dense neural networks trained with existing fairness-aware in-processing approaches. We term these fair subnetworks as Fair Scratch Tickets (FSTs). We also theoretically provide fairness and accuracy guarantees for them. In our experiments, we investigate the existence of FSTs on various datasets, target attributes, random initialization methods, sparsity patterns, and fairness surrogates. We also find that FSTs can transfer across datasets and investigate other properties of FSTs.

关键词： accountability ethics in vision fairness privacy Transparency

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Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection

Pixels, Regions, and Objects: Multiple Enhancement for Salie...

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

作者： Wang, Yi Wang, Ruili Fan, Xin Wang, Tianzhu He, Xiangjian Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China Massey Univ Sch Math & Computat Sci Palmerston North New Zealand Univ Nottingham Ningbo China Sch Comp Sci Ningbo Peoples R China

ISBN: (纸本)9798350301298

Salient object detection (SOD) aims to mimic the human visual system (HVS) and cognition mechanisms to identify and segment salient objects. However, due to the complexity of these mechanisms, current methods are not perfect. Accuracy and robustness need to be further improved, particularly in complex scenes with multiple objects and background clutter. To address this issue, we propose a novel approach called Multiple Enhancement Network (MENet) that adopts the boundary sensibility, content integrity, iterative refinement, and frequency decomposition mechanisms of HVS. A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features. A flexible multiscale feature enhancement module (ME-Module) is then designed to gradually aggregate and refine global or detailed features by changing the size order of the input feature sequence. An iterative training strategy is used to enhance boundary features and adaptive features in the dual-branch decoder of MENet. Comprehensive evaluations on six challenging benchmark datasets show that MENet achieves state-of-the-art results. Both the codes and results are publicly available at https://***/yiwangtz/MENet.

关键词： detection recognition: Categorization retrieval

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Optimization-Inspired Cross-Attention Transformer for Compressive Sensing

Optimization-Inspired Cross-Attention Transformer for Compre...

引用

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

作者： Song, Jiechong Mou, Chong Wang, Shiqi Ma, Siwei Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS). However, existing DUNs often improve the visual quality at the price of a large number of parameters and have the problem of feature information loss during iteration. In this paper, we propose an Optimization-inspired Cross-attention Transformer (OCT) module as an iterative process, leading to a lightweight OCT-based Unfolding Framework (OCTUF) for image CS. Specifically, we design a novel Dual Cross Attention (Dual-CA) sub-module, which consists of an Inertia-Supplied Cross Attention (ISCA) block and a Projection-Guided Cross Attention (PGCA) block. ISCA block introduces multi-channel inertia forces and increases the memory effect by a cross attention mechanism between adjacent iterations. And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block. Extensive CS experiments manifest that our OCTUF achieves superior performance compared to state-of-the-art methods while training lower complexity. Codes are available at https://***/songjiechong/OCTUF.

关键词： Low-level vision

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Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring

Deep Discriminative Spatial and Temporal Network for Efficie...

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

作者： Pan, Jinshan Xu, Boming Dong, Jiangxin Ge, Jianjun Tang, Jinhui Nanjing Univ Sci & Technol Nanjing Peoples R China China Elect Technol Grp Corp Beijing Peoples R China

ISBN: (纸本)9798350301298

How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. We show that the proposed method does not require additional alignment methods and performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity.

关键词： Low-level vision

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Semantic-Conditional Diffusion Networks for Image Captioning

Semantic-Conditional Diffusion Networks for Image Captioning

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

作者： Luo, Jianjie Li, Yehao Pan, Yingwei Yao, Ting Feng, Jianlin Chao, Hongyang Mei, Tao Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China HiDream Ai Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent advances on text-to-image generation have witnessed the rise of diffusion models which act as powerful generative models. Nevertheless, it is not trivial to exploit such latent variable models to capture the dependency among discrete words and meanwhile pursue complex visual-language alignment in image captioning. In this paper, we break the deeply rooted conventions in learning Transformer-based encoder-decoder, and propose a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net). Technically, for each input image, we first search the semantically relevant sentences via cross-modal retrieval model to convey the comprehensive semantic information. The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process. In SCD-Net, multiple Diffusion Transformer structures are stacked to progressively strengthen the output sentence with better visional-language alignment and linguistical coherence in a cascaded manner. Furthermore, to stabilize the diffusion process, a new self-critical sequence training strategy is designed to guide the learning of SCD-Net with the knowledge of a standard autoregressive Transformer model. Extensive experiments on COCO dataset demonstrate the promising potential of using diffusion models in the challenging image captioning task. Source code is available at https://***/YehLi/xmodaler/tree/master/configs/image_caption/scdnet.

关键词： language reasoning vision

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DegAE: A New Pretraining Paradigm for Low-level vision

DegAE: A New Pretraining Paradigm for Low-level Vision

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

作者： Liu, Yihao He, Jingwen Gu, Jinjin Kong, Xiangtao Qiao, Yu Dong, Chao Shanghai Artificial Intelligence Lab Shanghai Peoples R China Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acquisition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for highcost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (DegAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tailored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

关键词： Low-level vision

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Tell Me What Happened: Unifying Text-guided Video Completion via Multimodal Masked Video Generation

Tell Me What Happened: Unifying Text-guided Video Completion...

引用

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

作者： Fu, Tsu-Jui Yu, Licheng Zhang, Ning Fu, Cheng-Yang Su, Jong-Chyi Wang, William Yang Bell, Sean UC Santa Barbara Santa Barbara CA 93106 USA Meta Menlo Pk CA USA NEC Labs Amer Princeton NJ USA

ISBN: (纸本)9798350301298

Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video completion. Since there could be different outcomes from the hints of just a few frames, a system that can follow natural language to perform video completion may significantly improve controllability. Inspired by this, we introduce a novel task, text-guided video completion (TVC), which requests the model to generate a video from partial frames guided by an instruction. We then propose Multimodal Masked Video Generation (MMVG) to address this TVC task. During training, MMVG discretizes the video frames into visual tokens and masks most of them to perform video completion from any time point. At inference time, a single MMVG model can address all 3 cases of TVC, including video prediction, rewind, and infilling, by applying corresponding masking conditions. We evaluate MMVG in various video scenarios, including egocentric, animation, and gaming. Extensive experimental results indicate that MMVG is effective in generating high-quality visual appearances with text guidance for TVC.

关键词： and reasoning language vision

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Progressive Transformation Learning for Leveraging Virtual Images in Training

Progressive Transformation Learning for Leveraging Virtual I...

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

作者： Shen, Yi-Ting Lee, Hyungtae Kwon, Heesung Bhattacharyya, Shuvra S. Univ Maryland College Pk MD USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.

关键词： detection recognition: Categorization retrieval

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