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Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA: A Cause-Effect Look at Language Bias

引用

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

作者： Niu, Yulei Tang, Kaihua Zhang, Hanwang Lu, Zhiwu Hua, Xian-Sheng Wen, Ji-Rong Nanyang Technol Univ Singapore Singapore Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Alibaba Grp Damo Acad Hangzhou Peoples R China

ISBN: (纸本)9781665445092

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fail to disentangle the "good" language context and "bad" language bias from the whole. In this paper, we investigate how to mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect. Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data.

关键词： computer vision Codes Linguistics Robustness Cognition pattern recognition

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Distilling vision-Language Pre-Training to Collaborate with Weakly-Supervised Temporal Action Localization

Distilling Vision-Language Pre-Training to Collaborate with ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Chen Ju Kunhao Zheng Jinxiang Liu Peisen Zhao Ya Zhang Jianlong Chang Qi Tian Yanfeng Wang CMIC Shanghai Jiao Tong University Huawei Cloud Shanghai AI Laboratory

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classification-Based Pre-training (CBP) to generate video features for action localization. However, the different optimization objectives between classification and localization, make temporally localized results suffer from the serious incomplete issue. To tackle this issue without additional annotations, this paper considers to distill free action knowledge from vision-Language Pre-training (VLP), as we surprisingly observe that the localization results of vanilla VLP have an over-complete issue, which is just complementary to the CBP results. To fuse such complementarity, we propose a novel distillation-collaboration framework with two branches acting as CBP and VLP respectively. The framework is optimized through a dual-branch alternate training strategy. Specifically, during the B step, we distill the confident background pseudo-labels from the CBP branch; while during the F step, the confident foreground pseudo-labels are distilled from the VLP branch. As a result, the dualbranch complementarity is effectively fused to promote one strong alliance. Extensive experiments and ablation studies on THUMOS14 and ActivityNet1.2 reveal that our method significantly outperforms state-of-the-art methods.

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TopNet: Transformer-Based Object Placement Network for Image Compositing

TopNet: Transformer-Based Object Placement Network for Image...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Sijie Zhu Zhe Lin Scott Cohen Jason Kuen Zhifei Zhang Chen Chen Center for Research in Computer Vision University of Central Florida Adobe Research

We investigate the problem of automatically placing an object into a background image for image compositing. Given a background image and a segmented object, the goal is to train a model to predict plausible placements (location and scale) of the object for compositing. The quality of the composite image highly depends on the predicted location/scale. Existing works either generate candidate bounding boxes or apply sliding-window search using global representations from background and object images, which fail to model local information in background images. However, local clues in background images are important to determine the compatibility of placing the objects with certain locations/scales. In this paper, we propose to learn the correlation between object features and all local background features with a transformer module so that detailed information can be provided on all possible location/scale configurations. A sparse contrastive loss is further proposed to train our model with sparse supervision. Our new formulation generates a 3D heatmap indicating the plausibility of all location/scale combinations in one network forward pass, which is > 10 x faster than the previous sliding-window method. It also supports interactive search when users provide a pre-defined location or scale. The proposed method can be trained with explicit annotation or in a self-supervised manner using an off-the-shelf inpainting model, and it outperforms state-of-the-art methods significantly. User study shows that the trained model generalizes well to real-world images with diverse challenging scenes and object categories.

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VirFace: Enhancing Face recognition via Unlabeled Shallow Data

VirFace: Enhancing Face Recognition via Unlabeled Shallow Da...

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

作者： Li, Wenyu Guo, Tianchu Li, Pengyu Chen, Binghui Wang, Biao Zuo, Wangmeng Zhang, Lei Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recently, how to exploit unlabeled data for training face recognition models has been attracting increasing attention. However, few works consider the unlabeled shallow data(1) in real-world scenarios. The existing semi-supervised face recognition methods that focus on generating pseudo labels or minimizing softmax classification probabilities of the unlabeled data do not work very well on the unlabeled shallow data. It is still a challenge on how to effectively utilize the unlabeled shallow face data to improve the performance of face recognition. In this paper, we propose a novel face recognition method, named VirFace, to effectively exploit the unlabeled shallow data for face recognition. VirFace consists of VirClass and VirInstance. Specifically, VirClass enlarges the inter-class distance by injecting the unlabeled data as new identities, while VirInstance produces virtual instances sampled from the learned distribution of each identity to further enlarge the inter-class distance. To the best of our knowledge, we are the first to tackle the problem of unlabeled shallow face data. Extensive experiments have been conducted on both the small- and large-scale datasets, e.g. LFW and IJB-C, etc, demonstrating the superiority of the proposed method.

关键词： Training computer vision Face recognition Benchmark testing Data models

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Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding

Hierarchical Semantic Correspondence Networks for Video Para...

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conference on computer vision and pattern recognition (cvpr)

作者： Chaolei Tan Zihang Lin Jian-Fang Hu Wei-Shi Zheng Jianhuang Lai School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Video Paragraph Grounding (VPG) is an essential yet challenging task in vision-language understanding, which aims to jointly localize multiple events from an untrimmed video with a paragraph query description. One of the critical challenges in addressing this problem is to comprehend the complex semantic relations between visual and textual modalities. Previous methods focus on modeling the contextual information between the video and text from a single-level perspective (i.e., the sentence level), ignoring rich visual-textual correspondence relations at different semantic levels, e.g., the video-word and video-paragraph correspondence. To this end, we propose a novel Hierarchical Semantic Correspondence Network (HSCNet), which explores multi-level visual-textual correspondence by learning hierarchical semantic alignment and utilizes dense supervision by grounding diverse levels of queries. Specifically, we develop a hierarchical encoder that encodes the multi-modal inputs into semantics-aligned representations at different levels. To exploit the hierarchical semantic correspondence learned in the encoder for multi-level supervision, we further design a hierarchical decoder that progressively performs finer grounding for lower-level queries conditioned on higher-level semantics. Extensive experiments demonstrate the effectiveness of HSCNet and our method significantly outstrips the state-of-the-arts on two challenging benchmarks, i.e., ActivityNet-Captions and TACoS.

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HOICLIP: Efficient Knowledge Transfer for HOI Detection with vision-Language Models

HOICLIP: Efficient Knowledge Transfer for HOI Detection with...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Shan Ning Longtian Qiu Yongfei Liu Xuming He ShanghaiTech University Shanghai China ByteDance Inc. Shanghai Engineering Research Center of Intelligent Vision and Imaging

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios. In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization. In detail, we first introduce a novel interaction decoder to extract informative regions in the visual feature map of CLIP via a cross-attention mechanism, which is then fused with the detection backbone by a knowledge integration block for more accurate human- object pair detection. In addition, prior knowledge in CLIP text encoder is leveraged to generate a classifier by embedding HOI descriptions. To distinguish fine-grained interactions, we build a verb classifier from training data via visual semantic arithmetic and a lightweight verb representation adapter. Furthermore, we propose a training-free enhancement to exploit global HOI predictions from CLIP. Extensive experiments demonstrate that our method outperforms the state of the art by a large margin on various settings, e.g. +4.04 mAP on HICO-Det. The source code is available in https://***/Artanic30/HOICLIP.

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You See What I Want You to See: Exploring Targeted Black-Box Transferability Attack for Hash-based Image Retrieval Systems

You See What I Want You to See: Exploring Targeted Black-Box...

引用

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

作者： Xiao, Yanru Wang, Cong Old Dominion Univ Norfolk VA 23529 USA

ISBN: (纸本)9781665445092

With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic back-end from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online images into the database also opens up new attack vectors. Attackers can embed adversarial images into the database and target specific categories to be retrieved by user queries. In this paper, we start from an adversarial standpoint to explore and enhance the capacity of targeted black-box transferability attack for deep hashing. We motivate this work by a series of empirical studies to see the unique challenges in image retrieval. We study the relations between adversarial subspace and black-box transferability via utilizing random noise as a proxy. Then we develop a new attack that is simultaneously adversarial and robust to noise to enhance transferability. Our experimental results demonstrate about 1.2-3x improvements of black-box transferability compared with the state-of-the-art mechanisms.

关键词： computer vision Codes Image retrieval Multimedia databases pattern recognition Classification algorithms Image storage

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Visual Semantic Role Labeling for Video Understanding

Visual Semantic Role Labeling for Video Understanding

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

作者： Sadhu, Arka Gupta, Tanmay Yatskar, Mark Nevatia, Ram Kembhavi, Aniruddha Univ Southern Calif Los Angeles CA 90007 USA Univ Penn Philadelphia PA 19104 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9781665445092

We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events, wherein each event consists of a verb and multiple entities that fulfill various roles relevant to that event. To study the challenging task of semantic role labeling in videos or VidSRL, we introduce the VidSitu benchmark, a large scale video understanding data source with 29K 10-second movie clips richly annotated with a verb and semantic-roles every 2 seconds. Entities are co-referenced across events within a movie clip and events are connected to each other via event-event relations. Clips in VidSitu are drawn from a large collection of movies (similar to 3K) and have been chosen to be both complex (similar to 4.2 unique verbs within a video) as well as diverse (similar to 200 verbs have more than 100 annotations each). We provide a comprehensive analysis of the dataset in comparison to other publicly available video understanding benchmarks, several illustrative baselines and evaluate a range of standard video recognition models. Our code and dataset is available at ***.

关键词： Visualization computer vision Annotations Semantics Benchmark testing Motion pictures pattern recognition

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Scene Text Retrieval via Joint Text Detection and Similarity Learning

Scene Text Retrieval via Joint Text Detection and Similarity...

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

作者： Wang, Hao Bai, Xiang Yang, Mingkun Zhu, Shenggao Wang, Jing Liu, Wenyu Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Huawei Cloud & AI Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Scene text retrieval aims to localize and search all text instances from an image gallery, which are the same or similar with a given query text. Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter. In this paper, we address this problem by directly learning a cross-modal similarity between a query text and each text instance from natural images. Specifically, we establish an end-to-end trainable network, jointly optimizing the procedures of scene text detection and cross-modal similarity learning. In this way, scene text retrieval can be simply performed by ranking the detected text instances with the learned similarity. Experiments on three benchmark datasets demonstrate our method consistently outperforms the state-of-the-art scene text spotting/retrieval approaches. In particular, the proposed framework of joint detection and similarity learning achieves significantly better performance than separated methods. Code is available at: https://***/lanfeng4659/STR-TDSL.

关键词： computer vision Codes Text recognition Benchmark testing Task analysis

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Self-supervised Motion Learning from Static Images

Self-supervised Motion Learning from Static Images

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

作者： Huang, Ziyuan Zhang, Shiwei Jiang, Jianwen Tang, Mingqian Jin, Rong Ang, Marcelo H., Jr. Natl Univ Singapore Singapore Singapore Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Motions are reflected in videos as the movement of pixels, and actions are essentially patterns of inconsistent motions between the foreground and the background. To well distinguish the actions, especially those with complicated spatio-temporal interactions, correctly locating the prominent motion areas is of crucial importance. However, most motion information in existing videos are difficult to label and training a model with good motion representations with supervision will thus require a large amount of human labour for annotation. In this paper, we address this problem by self-supervised learning. Specifically, we propose to learn Motion from Static Images (MoSI). The model learns to encode motion information by classifying pseudo motions generated by MoSI. We furthermore introduce a static mask in pseudo motions to create local motion patterns, which forces the model to additionally locate notable motion areas for the correct classification. We demonstrate that MoSI can discover regions with large motion even without fine-tuning on the downstream datasets. As a result, the learned motion representations boost the performance of tasks requiring understanding of complex scenes and motions, i.e., action recognition. Extensive experiments show the consistent and transferable improvements achieved by MoSI. Codes will be soon released.

关键词： Training computer vision Codes Annotations pattern recognition Task analysis Videos

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