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CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not

CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, ...

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

作者： Sain, Aneeshan Bhunia, Ayan Kumar Chowdhury, Pinaki Nath Koley, Subhadeep Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford England

ISBN: (纸本)9798350301298

In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best to achieve this synergy, for both the category setting and the fine-grained setting ("all"). At the very core of our solution is a prompt learning setup. First we show just via factoring in sketch-specific prompts, we already have a category-level ZS-SBIR system that overshoots all prior arts, by a large margin (24.8%) - a great testimony on studying the CLIP and ZS-SBIR synergy. Moving onto the fine-grained setup is however trickier, and requires a deeper dive into this synergy. For that, we come up with two specific designs to tackle the fine-grained matching nature of the problem: (i) an additional regularisation loss to ensure the relative separation between sketches and photos is uniform across categories, which is not the case for the gold standard standalone triplet loss, and (ii) a clever patch shuffling technique to help establishing instance-level structural correspondences between sketch-photo pairs. With these designs, we again observe significant performance gains in the region of 26.9% over previous state-of-the-art. The take-home message, if any, is the proposed CLIP and prompt learning paradigm carries great promise in tackling other sketch-related tasks (not limited to ZS-SBIR) where data scarcity remains a great challenge. Project page: https:// ***/ Sketch LVM/

关键词： vision applications and systems

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Hierarchical Video-Moment Retrieval and Step-Captioning

Hierarchical Video-Moment Retrieval and Step-Captioning

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

作者： Zola, Abhay Cho, Jaemin Kottur, Satwik Chen, Xilun Oguz, Barlas Mehdad, Yashar Bansal, Mohit UNC Chapel Hill Chapel Hill NC 27599 USA Meta Al Redmond WA USA

ISBN: (纸本)9798350301298

There is growing interest in searching for information from large video corpora. Prior works have studied relevant tasks, such as text-based video retrieval, moment retrieval, video summarization, and video captioning in isolation, without an end-to-end setup that can jointly search from video corpora and generate summaries. Such an end-to-end setup would allow for many interesting applications, e.g., a text-based search that finds a relevant video from a video corpus, extracts the most relevant moment from that video, and segments the moment into important steps with captions. To address this, we present the HIREST (HIerarchical REtrieval and STep-captioning) dataset and propose a new benchmark that covers hierarchical information retrieval and visual/textual stepwise summarization from an instructional video corpus. HIREST consists of 3.4K text-video pairs from an instructional video dataset, where 1.1K videos have annotations of moment spans relevant to text query and breakdown of each moment into key instruction steps with caption and timestamps (totaling 8.6K step captions). Our hierarchical benchmark consists of video retrieval, moment retrieval, and two novel moment segmentation and step captioning tasks. In moment segmentation, models break down a video moment into instruction steps and identify start-end boundaries. In step captioning, models generate a textual summary for each step. We also present starting point task-specific and end-to-end joint baseline models for our new benchmark. While the baseline models show some promising results, there still exists large room for future improvement by the community.(1)

关键词： and reasoning language vision

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Exploring Discontinuity for Video Frame Interpolation

Exploring Discontinuity for Video Frame Interpolation

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

作者： Lee, Sangjin Lee, Hyeongmin Shin, Chajin Son, Hanbin Lee, Sangyoun Yonsei Univ Seoul South Korea Korea Inst Sci & Technol KIST Seoul South Korea

ISBN: (纸本)9798350301298

Video frame interpolation (VFI) is the task that synthesizes the intermediate frame given two consecutive frames. Most of the previous studies have focused on appropriate frame warping operations and refinement modules for the warped frames. These studies have been conducted on natural videos containing only continuous motions. However, many practical videos contain various unnatural objects with discontinuous motions such as logos, user interfaces and subtitles. We propose three techniques that can make the existing deep learning-based VFI architectures robust to these elements. First is a novel data augmentation strategy called figure-text mixing (FTM) which can make the models learn discontinuous motions during training stage without any extra dataset. Second, we propose a simple but effective module that predicts a map called discontinuity map (D-map), which densely distinguishes between areas of continuous and discontinuous motions. Lastly, we propose loss functions to give supervisions of the discontinuous motion areas which can be applied along with FTM and D-map. We additionally collect a special test benchmark called Graphical Discontinuous Motion (GDM) dataset consisting of some mobile games and chatting videos. Applied to the various state-of-the-art VFI networks, our method significantly improves the interpolation qualities on the videos from not only GDM dataset, but also the existing benchmarks containing only continuous motions such as Vimeo90K, UCF101, and DAVIS.

关键词： Low-level vision

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ReCo: Region-Controlled Text-to-Image Generation

ReCo: Region-Controlled Text-to-Image Generation

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

作者： Yang, Zhengyuan Wang, Jianfeng Gan, Zhe Li, Linjie Lin, Kevin Wu, Chenfei Duan, Nan Liu, Zicheng Liu, Ce Zeng, Michael Wang, Lijuan Microsoft Albuquerque NM 87108 USA

ISBN: (纸本)9798350301298

Recently, large-scale text-to-image (T2I) models have shown impressive performance in generating high-fidelity images, but with limited controllability, e.g., precisely specifying the content in a specific region with a free-form text description. In this paper, we propose an effective technique for such regional control in T2I generation. We augment T2I models' inputs with an extra set of position tokens, which represent the quantized spatial coordinates. Each region is specified by four position tokens to represent the top-left and bottom-right corners, followed by an open-ended natural language regional description. Then, we fine-tune a pre-trained T2I model with such new input interface. Our model, dubbed as ReCo (Region-Controlled T2I), enables the region control for arbitrary objects described by open-ended regional texts rather than by object labels from a constrained category set. Empirically, ReCo achieves better image quality than the T2I model strengthened by positional words (FID: 8.82 -> 7.36, SceneFID: 15.54 -> 6.51 on COCO), together with objects being more accurately placed, amounting to a 20.40% region classification accuracy improvement on COCO. Furthermore, we demonstrate that ReCo can better control the object count, spatial relationship, and region attributes such as color/size, with the free-form regional description. Human evaluation on PaintSkill shows that ReCo is +19.28% and +17.21% more accurate in generating images with correct object count and spatial relationship than the T2I model. Code is available at https://***/microsoft/ReCo.

关键词： language reasoning vision

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Adapting Shortcut with Normalizing Flow: An Efficient Tuning Framework for Visual recognition

Adapting Shortcut with Normalizing Flow: An Efficient Tuning...

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

作者： Wang, Yaoming Shi, Bowen Zhang, Xiaopeng Li, Jin Liu, Yuchen Dai, Wenrui Li, Chenglin Xiong, Hongkai Tian, Qi Shanghai Jiao Tong Univ Shanghai Peoples R China Huawei Cloud Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Pretraining followed by fine-tuning has proven to be effective in visual recognition tasks. However, fine-tuning all parameters can be computationally expensive, particularly for large-scale models. To mitigate the computational and storage demands, recent research has explored Parameter-Efficient Fine-Tuning (PEFT), which focuses on tuning a minimal number of parameters for efficient adaptation. Existing methods, however, fail to analyze the impact of the additional parameters on the model, resulting in an unclear and suboptimal tuning process. In this paper, we introduce a novel and effective PEFT paradigm, named SNF (Shortcut adaptation via Normalization Flow), which utilizes normalizing flows to adjust the shortcut layers. We highlight that layers without Lipschitz constraints can lead to error propagation when adapting to down-stream datasets. Since modifying the over-parameterized residual connections in these layers is expensive, we focus on adjusting the cheap yet crucial shortcuts. Moreover, learning new information with few parameters in PEFT can be challenging, and information loss can result in label information degradation. To address this issue, we propose an information-preserving normalizing flow. Experimental results demonstrate the effectiveness of SNF. Specifically, with only 0.036M parameters, SNF surpasses previous approaches on both the FGVC and VTAB-1k benchmarks using ViT/B-16 as the backbone. The code is available at https://***/Wang-Yaoming/SNF

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

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RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction

RepMode: Learning to Re-parameterize Diverse Experts for Sub...

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

作者： Zhou, Donghao Gu, Chunbin Xu, Junde Liu, Furui Wang, Qiong Chen, Guangyong Heng, Pheng-Ann Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Comp Vis & Virtual Real Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9798350301298

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to cells. In this paper, we model it as a deep learning task termed subcellular structure prediction (SSP), aiming to predict the 3D fluorescent images of multiple subcellular structures from a 3D transmitted-light image. Unfortunately, due to the limitations of current biotechnology, each image is partially labeled in SSP. Besides, naturally, subcellular structures vary considerably in size, which causes the multi-scale issue of SSP. To overcome these challenges, we propose Re-parameterizing Mixture-of-Diverse-Experts (RepMode), a network that dynamically organizes its parameters with task-aware priors to handle specified single-label prediction tasks. In RepMode, the Mixture-of-Diverse-Experts (MoDE) block is designed to learn the generalized parameters for all tasks, and gating re-parameterization (GatRep) is performed to generate the specialized parameters for each task, by which RepMode can maintain a compact practical topology exactly like a plain network, and meanwhile achieves a powerful theoretical topology. Comprehensive experiments show that RepMode can achieve state-of-the-art overall performance in SSP.

关键词： cell microscopy Medical and biological vision

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High-level context representation for emotion recognition in images

High-level context representation for emotion recognition in...

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

作者： Costa, Willams de Lima Martinez, Estefania Talavera Figueiredo, Lucas Silva Teichrieb, Veronica Univ Fed Pernambuco Voxar Labs Ctr Informat Ave Jorn Anibal Fernandes Recife PE Brazil Univ Twente Drienerlolaan 5 NL-7522 NB Enschede Netherlands Univ Fed Rural Pernambuco Unidade Acad Belo Jardim PE-166100 Belo Jardim Brazil

ISBN: (纸本)9798350302493

Emotion recognition is the task of classifying perceived emotions in people. Previous works have utilized various nonverbal cues to extract features from images and correlate them to emotions. Of these cues, situational context is particularly crucial in emotion perception since it can directly influence the emotion of a person. In this paper, we propose an approach for high-level context representation extraction from images. The model relies on a single cue and a single encoding stream to correlate this representation with emotions. Our model competes with the state-of-the-art, achieving an mAP of 0.3002 on the EMOTIC dataset while also being capable of execution on consumer-grade hardware at approximate to 90 frames per second. Overall, our approach is more efficient than previous models and can be easily deployed to address real-world problems related to emotion recognition.

关键词： Emotion recognition

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Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection

Learning on Gradients: Generalized Artifacts Representation ...

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

作者： Tan, Chuangchuang Zhao, Yao Wei, Shikui Gu, Guanghua Wei, Yunchao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao Peoples R China

ISBN: (纸本)9798350301298

Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of 11.4%. The code is released at https://***/chuangchuangtan/LGrad.

关键词： detection recognition: Categorization retrieval

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ProD: Prompting-to-disentangle Domain Knowledge for Cross-domain Few-shot Image Classification

ProD: Prompting-to-disentangle Domain Knowledge for Cross-do...

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

作者： Ma, Tianyi Sun, Yifan Yang, Zongxin Yang, Yi Univ Technol Sydney Ultimo Australia Baidu Inc Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798350301298

This paper considers few-shot image classification under the cross-domain scenario, where the train-to-test domain gap compromises classification accuracy. To mitigate the domain gap, we propose a prompting-to-disentangle (ProD) method through a novel exploration with the prompting mechanism. ProD adopts the popular multi-domain training scheme and extracts the backbone feature with a standard Convolutional Neural Network. Based on these two common practices, the key point of ProD is using the prompting mechanism in the transformer to disentangle the domain-general (DG) and domain-specific (DS) knowledge from the backbone feature. Specifically, ProD concatenates a DG and a DS prompt to the backbone feature and feeds them into a lightweight transformer. The DG prompt is learnable and shared by all the training domains, while the DS prompt is generated from the domain-of-interest on the fly. As a result, the transformer outputs DG and DS features in parallel with the two prompts, yielding the disentangling effect. We show that: 1) Simply sharing a single DG prompt for all the training domains already improves generalization towards the novel test domain. 2) The cross-domain generalization can be further reinforced by making the DG prompt neutral towards the training domains. 3) When inference, the DS prompt is generated from the support samples and can capture test domain knowledge through the prompting mechanism. Combining all three benefits, ProD significantly improves cross-domain few-shot classification. For instance, on CUB, ProD improves the 5-way 5-shot accuracy from 73.56% (baseline) to 79.19%, setting a new state of the art.

关键词： detection recognition: Categorization retrieval

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Iterative Proposal Refinement forWeakly-Supervised Video Grounding

Iterative Proposal Refinement forWeakly-Supervised Video Gro...

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

作者： Cao, Meng Wei, Fangyun Xu, Can Geng, Xiubo Chen, Long Zhang, Can Zou, Yuexian Shen, Tao Jiang, Daxin Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Microsoft Redmond WA 98052 USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations. To date, most of the state-of-the-art WSVG methods follow a two-stage pipeline, i.e., firstly generating potential temporal proposals and then grounding with these proposal candidates. Despite the recent progress, existing proposal generation methods suffer from two drawbacks: 1) lack of explicit correspondence modeling;and 2) partial coverage of complex events. To this end, we propose a novel IteRative prOposal refiNement network (dubbed as IRON) to gradually distill the prior knowledge into each proposal and encourage proposals with more complete coverage. Specifically, we set up two lightweight distillation branches to uncover the cross-modal correspondence on both the semantic and conceptual levels. Then, an iterative Label Propagation (LP) strategy is devised to prevent the network from focusing excessively on the most discriminative events instead of the whole sentence content. Precisely, during each iteration, the proposal with the minimal distillation loss and its adjacent ones are regarded as the positive samples, which refines proposal confidence scores in a cascaded manner. Extensive experiments and ablation studies on two challenging WSVG datasets have attested to the effectiveness of our IRON. The code will be available at https://***/mengcaopku/IRON.

关键词： and reasoning language vision

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