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Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training

Towards Generalisable Video Moment Retrieval: Visual-Dynamic...

引用

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

作者： Luo, Dezhao Huang, Jiabo Gong, Shaogang Jin, Hailin Liu, Yang Queen Mary Univ London London England Adobe Res San Francisco CA USA Peking Univ WICT Beijing Peoples R China

ISBN: (纸本)9798350301298

The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding. Without sufficient temporal boundary annotations, it is non-trivial to learn universal video-text alignments. In this work, we explore multi-modal correlations derived from large-scale image-text data to facilitate generalisable VMR. To address the limitations of image-text pre-training models on capturing the video changes, we propose a generic method, referred to as Visual-Dynamic Injection (VDI), to empower the model's understanding of video moments. Whilst existing VMR methods are focusing on building temporal-aware video features, being aware of the text descriptions about the temporal changes is also critical but originally overlooked in pre-training by matching static images with sentences. Therefore, we extract visual context and spatial dynamic information from video frames and explicitly enforce their alignments with the phrases describing video changes (e.g. verb). By doing so, the potentially relevant visual and motion patterns in videos are encoded in the corresponding text embeddings (injected) so to enable more accurate video-text alignments. We conduct extensive experiments on two VMR benchmark datasets (Charades-STA and ActivityNet-Captions) and achieve state-of-the-art performances. Especially, VDI yields notable advantages when being tested on the out-of-distribution splits where the testing samples involve novel scenes and vocabulary.

关键词： and reasoning language vision

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IFSeg: Image-free Semantic Segmentation via vision-Language Model

IFSeg: Image-free Semantic Segmentation via Vision-Language ...

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

作者： Yun, Sukmin Park, Seong Hyeon Seo, Paul Hongsuck Shin, Jinwoo Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Google Res Seoul South Korea Mohamed Bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been underexplored, and the existing approaches still have the burden of acquiring additional training images or even segmentation annotations to adapt a VL model to downstream segmentation tasks. In this paper, we introduce a novel image-free segmentation task where the goal is to perform semantic segmentation given only a set of the target semantic categories, but without any task-specific images and annotations. To tackle this challenging task, our proposed method, coined IFSeg, generates VL-driven artificial image-segmentation pairs and updates a pre-trained VL model to a segmentation task. We construct this artificial training data by creating a 2D map of random semantic categories and another map of their corresponding word tokens. Given that a pre-trained VL model projects visual and text tokens into a common space where tokens that share the semantics are located closely, this artificially generated word map can replace the real image inputs for such a VL model. Through an extensive set of experiments, our model not only establishes an effective baseline for this novel task but also demonstrates strong performances compared to existing methods that rely on stronger supervision, such as task-specific images and segmentation masks. Code is available at https://***/alinlab/ifseg.

关键词： grouping and shape analysis Segmentation

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What Can Human Sketches Do for Object Detection?

What Can Human Sketches Do for Object Detection?

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

作者： Chowdhury, Pinaki Nath Bhunia, Ayan Kumar Sain, Aneeshan Koley, Subhadeep Xiang, Tao Song, Yi-Zhe Univ Surrey SketchX CVSSP Guildford GU2 5XH Surrey England

ISBN: (纸本)9798350301298

Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundamental vision task of object detection. The end result is a sketch-enabled object detection framework that detects based on what you sketch - that "zebra" (e.g., one that is eating the grass) in a herd of zebras (instance-aware detection), and only the part (e.g., "head" of a "zebra") that you desire (part-aware detection). We further dictate that our model works without (i) knowing which category to expect at testing (zero-shot) and (ii) not requiring additional bounding boxes (as per fully supervised) and class labels (as per weakly supervised). Instead of devising a model from the ground up, we show an intuitive synergy between foundation models (e.g., CLIP) and existing sketch models build for sketch-based image retrieval (SBIR), which can already elegantly solve the task - CLIP to provide model generalisation, and SBIR to bridge the (***) gap. In particular, we first perform independent prompting on both sketch and photo branches of an SBIR model to build highly generalisable sketch and photo encoders on the back of the generalisation ability of CLIP. We then devise a training paradigm to adapt the learned encoders for object detection, such that the region embeddings of detected boxes are aligned with the sketch and photo embeddings from SBIR. Evaluating our framework on standard object detection datasets like PASCAL-VOC and MS-COCO outperforms both supervised (SOD) and weakly-supervised object detectors (WSOD) on zero-shot setups. Project Page: https:// ***/sketch-detect

关键词： vision applications and systems

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Adversarial Normalization: I Can visualize Everything (ICE)

Adversarial Normalization: I Can visualize Everything (ICE)

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

作者： Choi, Hoyoung Jin, Seungwan Han, Kyungsik Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image;thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://***/Hanyang-HCC-La

关键词： Explainable computer vision

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3Mformer: Multi-order Multi-mode Transformer for Skeletal Action recognition

3Mformer: Multi-order Multi-mode Transformer for Skeletal Ac...

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

作者： Wang, Lei Koniusz, Piotr Australian Natl Univ Canberra ACT Australia Data61 CSIRO Eveleigh Australia

ISBN: (纸本)9798350301298

Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore the dependency between not linked body joints. We propose to form hypergraph to model hyperedges between graph nodes (e.g., third- and fourth-order hyper-edges capture three and four nodes) which help capture higher-order motion patterns of groups of body joints. We split action sequences into temporal blocks, Higherorder Transformer (HoT) produces embeddings of each temporal block based on (i) the body joints, (ii) pairwise links of body joints and (iii) higher-order hyper-edges of skeleton body joints. We combine such HoT embeddings of hyper-edges of orders 1,..., r by a novel Multi-order Multi-mode Transformer (3Mformer) with two modules whose order can be exchanged to achieve coupled-mode attention on coupled-mode tokens based on 'channel-temporal block', 'order-channel-body joint', 'channel-hyper-edge (any order)' and 'channel-only' pairs. The first module, called Multi-order Pooling (MP), additionally learns weighted aggregation along the hyper-edge mode, whereas the second module, Temporal block Pooling (TP), aggregates along the temporal block1 mode. Our end-to-end trainable network yields state-of-the-art results compared to GCN-, transformer- and hypergraph-based counterparts.

关键词： Video: Action and event understanding

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Learning to Segment Every Referring Object Point by Point

Learning to Segment Every Referring Object Point by Point

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

作者： Qu, Mengxue Wu, Yu Wei, Yunchao Liu, Wu Liang, Xiaodan Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Wuhan Univ Wuhan Peoples R China JD Explore Acad Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China MBZUAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Referring Expression Segmentation (RES) can facilitate pixel-level semantic alignment between vision and language. Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., training using abundant referring bounding boxes and only a few (e.g., 1%) pixel-level referring masks. To maximize the transferability from the REC model, we construct our model based on the point-based sequence prediction model. We propose the co-content teacher-forcing to make the model explicitly associate the point coordinates (scale values) with the referred spatial features, which alleviates the exposure bias caused by the limited segmentation masks. To make the most of referring bounding box annotations, we further propose the resampling pseudo points strategy to select more accurate pseudo-points as supervision. Extensive experiments show that our model achieves 52.06% in terms of accuracy (versus 58.93% in fully supervised setting) on RefCOCO+@testA, when only using 1% of the mask annotations. Code is available at https://***/ qumengxue/Partial- ***.

关键词： language reasoning vision

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Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving

Visual Exemplar Driven Task-Prompting for Unified Perception...

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

作者： Liang, Xiwen Niu, Minzhe Han, Jianhua Xu, Hang Xu, Chunjing Liang, Xiaodan Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Multi-task learning has emerged as a powerful paradigm to solve a range of tasks simultaneously with good efficiency in both computation resources and inference time. However, these algorithms are designed for different tasks mostly not within the scope of autonomous driving, thus making it hard to compare multi-task methods in autonomous driving. Aiming to enable the comprehensive evaluation of present multi-task learning methods in autonomous driving, we extensively investigate the performance of popular multi-task methods on the large-scale driving dataset, which covers four common perception tasks, i.e., object detection, semantic segmentation, drivable area segmentation, and lane detection. We provide an in-depth analysis of current multi-task learning methods under different common settings and find out that the existing methods make progress but there is still a large performance gap compared with single-task baselines. To alleviate this dilemma in autonomous driving, we present an effective multi-task framework, VE-Prompt, which introduces visual exemplars via task-specific prompting to guide the model toward learning high-quality task-specific representations. Specifically, we generate visual exemplars based on bounding boxes and color-based markers, which provide accurate visual appearances of target categories and further mitigate the performance gap. Furthermore, we bridge transformer-based encoders and convolutional layers for efficient and accurate unified perception in autonomous driving. Comprehensive experimental results on the diverse self-driving dataset BDD100K show that the VE-Prompt improves the multi-task baseline and further surpasses single-task models.

关键词： vision applications and systems

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Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at vision Transformer Inference

Castling-ViT: Compressing Self-Attention via Switching Towar...

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

作者： You, Haoran Xiong, Yunyang Dai, Xiaoliang Wu, Bichen Zhang, Peizhao Fan, Haoqi Vajda, Peter Lin, Yingyan (Celine) Georgia Inst Technol Atlanta GA 30332 USA Meta Res Menlo Pk CA USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs' attention measures global similarities and thus has a quadratic complexity with the number of input tokens. Existing efficient ViTs adopt local attention or linear attention, which sacrifice ViTs' capabilities of capturing either global or local context. In this work, we ask an important research question: Can ViTs learn both global and local context while being more efficient during inference? To this end, we propose a framework called Castling-ViT, which trains ViTs using both linear-angular attention and masked softmax-based quadratic attention, but then switches to having only linear-angular attention during inference. Our Castling-ViT leverages angular kernels to measure the similarities between queries and keys via spectral angles. And we further simplify it with two techniques: (1) a novel linear-angular attention mechanism: we decompose the angular kernels into linear terms and high-order residuals, and only keep the linear terms;and (2) we adopt two parameterized modules to approximate high-order residuals: a depthwise convolution and an auxiliary masked softmax attention to help learn global and local information, where the masks for softmax attention are regularized to gradually become zeros and thus incur no overhead during inference. Extensive experiments validate the effectiveness of our Castling-ViT, e.g., achieving up to a 1.8% higher accuracy or 40% MACs reduction on classification and 1.2 higher mAP on detection under comparable FLOPs, as compared to ViTs with vanilla softmax-based attentions. Project page is available at here.

关键词： Deep learning architectures and techniques

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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 ...

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

作者： Ju, Chen Zheng, Kunhao Liu, Jinxiang Zhao, Peisen Zhang, Ya Chang, Jianlong Tian, Qi Wang, Yanfeng Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Huawei Cloud Shenzhen Peoples R China

ISBN: (纸本)9798350301298

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 dual-branch 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.

关键词： Video: Action and event understanding

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An Actor-centric Causality Graph for Asynchronous Temporal Inference in Group Activity

An Actor-centric Causality Graph for Asynchronous Temporal I...

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

作者： Xie, Zhao Gao, Tian Wu, Kewei Chang, Jiao Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350301298

The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence on the centric actor (effect actor) from its correlative actors (cause actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficient to deal with the causality relation with asynchronous temporal features. In this paper, we propose an Actor-Centric Causality Graph Model, which learns the asynchronous temporal causality relation with three modules, i.e., an asynchronous temporal causality relation detection module, a causality feature fusion module, and a causality relation graph inference module. First, given a centric actor and its correlative actor, we analyze their influences to detect causality relation. We estimate the self influence of the centric actor with self regression. We estimate the correlative influence from the correlative actor to the centric actor with correlative regression, which uses asynchronous features at different timestamps. Second, we synchronize the two action features by estimating the temporal delay between the cause action and the effect action. The synchronized features are used to enhance the feature of the effect action with a channel-wise fusion. Third, we describe the nodes (actors) with causality features and learn the edges by fusing the causality relation with the appearance relation and distance relation. The causality relation graph inference provides crucial features of effect action, which are complementary to the base model using synchronous relation inference. Experiments show that our method achieves state-of-the-art performance on the Volleyball dataset and Collective Activity dataset.

关键词： detection recognition: Categorization retrieval

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