论文检索

Zhu Liu 0004, Jinyuan Liu 0001, Benzhuang Zhang, Long Ma 0002, Xin Fan 0001, Risheng Liu

Infrared and visible image fusion is a powerful technique that combines complementary information from different modalities for downstream semantic perception tasks. Existing learning-based methods show remarkable performance, but are suffering from the inherent vulnerability of adversarial attacks, causing a significant decrease in accuracy. In this work, a perception-aware fusion framework is proposed to promote segmentation robustness in adversarial scenes. We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations. Based on these analyses, we propose a harmonized architecture search with a decomposition-based structure to balance standard accuracy and robustness. We also propose an adaptive learning strategy to improve the parameter robustness of image fusion, which can learn effective feature extraction under diverse adversarial perturbations. Thus, the goals of image fusion (i.e., extracting complementary features from source modalities and defending attack) can be realized from the perspectives of architectural and learning strategies. Extensive experimental results demonstrate that our scheme substantially enhances the robustness, with gains of 15.3% mIOU of segmentation in the adversarial scene, compared with advanced competitors. The source codes are available at https://github.com/LiuZhu-CV/PAIF.

Jingyu Wu, Shi Chen 0005, Shuyu Gan, Weijun Li, Changyuan Yang, Lingyun Sun

Co-speech gesture generation is essential for multimodal chatbots and agents. Previous research extensively studies the relationship between text, audio, and gesture. Meanwhile, to enhance cross-culture communication, culture-specific gestures are crucial for chatbots to learn cultural differences and incorporate cultural cues. However, culture-specific gesture generation faces two challenges: lack of large-scale, high-quality gesture datasets that include diverse cultural groups, and lack of generalization across different cultures. Therefore, in this paper, we first introduce a Multiple Culture Gesture Dataset (MCGD), the largest freely available gesture dataset to date. It consists of ten different cultures, over 200 speakers, and 10,000 segmented sequences. We further propose a Cultural Self-adaptive Gesture Generation Network (CSGN) that takes multimodal relationships into consideration while generating gestures using a cascade architecture and learnable dynamic weight. The CSGN adaptively generates gestures with different cultural characteristics without the need to retrain a new network. It extracts cultural features from the multimodal inputs or a cultural style embedding space with a designated culture. We broadly evaluate our method across four large-scale benchmark datasets. Empirical results show that our method achieves multiple cultural gesture generation and improves comprehensiveness of multimodal inputs. Our method improves the state-of-the-art average FGD from 53.7 to 48.0 and culture deception rate (CDR) from 33.63% to 39.87%.

Zongwei Wu, Jingjing Wang, Zhuyun Zhou, Zhaochong An, Qiuping Jiang, Cédric Demonceaux, Guolei Sun, Radu Timofte

Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy. We explore semantics among the multimodal inputs in two aspects: the modality-shared consistency and the modality-specific variation. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision. On the one hand, the AF block explicitly dissociates the shared and specific representation and learns to weight the modal contribution by adjusting the proportion, region, and pattern, depending upon the quality. On the other hand, our CFD initially decodes the shared feature and then refines the output through specificity-aware querying. Further, we enforce semantic consistency across the decoding layers to enable interaction across network hierarchies, improving feature discriminability. Exhaustive comparison on eleven datasets with depth or thermal clues, and on two challenging tasks, namely salient and camouflage object segmentation, validate our effectiveness in terms of both performance and robustness. The source code is publicly available at https://github.com/Zongwei97/XMSNet.

Shiping Ge, Zhiwei Jiang 0001, Yafeng Yin 0002, Cong Wang 0034, Zifeng Cheng, Qing Gu 0001

Audio-Visual Event Localization (AVEL) aims to locate events that are both visible and audible in a video. Existing AVEL methods primarily focus on learning generic localization patterns that are applicable to all events. However, events often exhibit modality biases, such as visual-dominated, audio-dominated, or modality-balanced, which can lead to different localization preferences. These preferences may be overlooked by existing methods, resulting in unsatisfactory localization performance. To address this issue, this paper proposes a novel event-aware localization paradigm, which first identifies the event category and then leverages localization preferences specific to that event for improved event localization. To achieve this, we introduce a memory-assisted metric learning framework, which utilizes historic segments as anchors to adjust the unified representation space for both event classification and event localization. To provide sufficient information for this metric learning, we design a spatial-temporal audio-visual fusion encoder to capture the spatial and temporal interaction between audio and visual modalities. Extensive experiments on the public AVE dataset in both fully-supervised and weakly-supervised settings demonstrate the effectiveness of our approach. Code will be released at https://github.com/ShipingGe/AVEL.

Jiaqing Fan, Tiankang Su, Kaihua Zhang 0001, Bo Liu 0005, Qingshan Liu 0001

Spatial-temporal structural details of targets in video (e.g. varying edges, textures over time) are essential to accurate Unsupervised Video Object Segmentation (UVOS). The vanilla multi-head self-attention in the Transformer-based UVOS methods usually concentrates on learning the general low-frequency information (e.g. illumination, color), while neglecting the high-frequency texture details, leading to unsatisfying segmentation results. To address this issue, this paper presents a Temporally efficient Gabor Transformer (TGFormer) for UVOS. The TGFormer jointly models the spatial dependencies and temporal coherence intra- and inter-frames, which can fully capture the rich structural details for accurate UVOS. Concretely, we first propose an effective learnable Gabor filtering Transformer to mine the structural texture details of the object for accurate UVOS. Then, to adaptively store the redundant neighboring historical information, we present an efficient dynamic neighboring frame selection module to automatically choose the useful temporal information, which simultaneously relieves the blurry frame and reduces the computation burden. Finally, we make the UVOS model be a fully Transformer architecture, meanwhile aggregating the information from space, Gabor and time domains, yielding a strong representation with rich structure details. Extensive experiments on five mainstream UVOS benchmarks (DAVIS2016, FBMS, DAVSOD, ViSal, and MCL) demonstrate the superiority of the presented solution to sate-of-the-art methods.

Zihan Li, Yuan Zheng, Xiangde Luo, Dandan Shan, Qingqi Hong

Medical image segmentation plays a critical role in clinical decision-making, treatment planning, and disease monitoring. However, accurate segmentation of medical images is challenging due to several factors, such as the lack of high-quality annotation, imaging noise, and anatomical differences across patients. In addition, there is still a considerable gap in performance between the existing label-efficient methods and fully-supervised methods. To address the above challenges, we propose ScribbleVC, a novel framework for scribble-supervised medical image segmentation that leverages vision and class embeddings via the multimodal information enhancement mechanism. In addition, ScribbleVC uniformly utilizes the CNN features and Transformer features to achieve better visual feature extraction. The proposed method combines a scribble-based approach with a segmentation network and a class-embedding module to produce accurate segmentation masks. We evaluate ScribbleVC on three benchmark datasets and compare it with state-of-the-art methods. The experimental results demonstrate that our method outperforms existing approaches in terms of accuracy, robustness, and efficiency. The datasets and code are released on GitHub.

Meng Liu 0014, Ke Liang 0006, Dayu Hu, Hao Yu 0017, Yue Liu 0008, Lingyuan Meng, Wenxuan Tu, Sihang Zhou 0001, Xinwang Liu 0002

Audiovisual data is everywhere in this digital age, which raises higher requirements for the deep learning models developed on them. To well handle the information of the multi-modal data is the key to a better audiovisual modal. We observe that these audiovisual data naturally have temporal attributes, such as the time information for each frame in the video. More concretely, such data is inherently multi-modal according to both audio and visual cues, which proceed in a strict chronological order. It indicates that temporal information is important in multi-modal acoustic event modeling for both intra- and inter-modal. However, existing methods deal with each modal feature independently and simply fuse them together, which neglects the mining of temporal relation and thus leads to sub-optimal performance. With this motivation, we propose a Temporal Multi-modal graph learning method for Acoustic event Classification, called TMac, by modeling such temporal information via graph learning techniques. In particular, we construct a temporal graph for each acoustic event, dividing its audio data and video data into multiple segments. Each segment can be considered as a node, and the temporal relationships between nodes can be considered as timestamps on their edges. In this case, we can smoothly capture the dynamic information in intra-modal and inter-modal. Several experiments are conducted to demonstrate TMac outperforms other SOTA models in performance. Our code is available at https://github.com/MGitHubL/TMac.

Wei Ji 0011, Jingjing Li, Cheng Bian, Zhicheng Zhang 0005, Li Cheng 0001

Growing interests in multispectral semantic segmentation (MSS) have been witnessed in recent years, thanks to the unique advantages of combining RGB and thermal infrared images to tackle challenging scenarios with adverse conditions. However, unlike traditional RGB-only semantic segmentation, the lack of a large-scale MSS dataset has become a hindrance to the progress of this field. To address this issue, we introduce a SemanticRT dataset - the largest MSS dataset to date, comprising 11,371 high-quality, pixel-level annotated RGB-thermal image pairs. It is 7 times larger than the existing MFNet dataset, and covers a wide variety of challenging scenarios in adverse lighting conditions such as low-light and pitch black. Further, a novel Explicit Complement Modeling (ECM) framework is developed to extract modality-specific information, which is propagated through a robust cross-modal feature encoding and fusion process. Extensive experiments demonstrate the advantages of our approach and dataset over the existing counterparts. Our new dataset may also facilitate further development and evaluation of existing and new MSS algorithms.

Qingwei Wang, Jinyu Yang, Xiaosheng Yu 0003, Fangyi Wang, Peng Chen, Feng Zheng 0001

Camouflaged Object Detection (COD) aims to identify and segment objects that blend into their surroundings. Since the color and texture of the camouflaged objects are extremely similar to the surrounding environment, it is super challenging for vision models to precisely detect them. Inspired by research on biology and evolution, we introduce depth information as an additional cue to help break camouflage, which can provide spatial information and texture-free separation for foreground and background. To dig clues of camouflaged objects in both RGB and depth modalities, we innovatively propose Depth-aided Camouflaged Object Detection (DaCOD), which involves two key components. We firstly propose the Multi-modal Collaborative Learning (MCL) module, which aims to collaboratively learning deep features from both RGB and depth channels via a hybrid backbone. Then, we propose a novel Cross-modal Asymmetric Fusion (CAF) strategy, which asymmetrically fuse RGB and depth information for complementary depth feature enhancement to produce accurate predictions. We conducted numerous experiments of the proposed DaCOD on three widely-used challenging COD benchmark datasets, in which DaCOD outperforms the current state-of-the-arts by a large margin. All resources are available at https://github.com/qingwei-wang/DaCOD.

Yansheng Qiu, Ziyuan Zhao, Hongdou Yao, Delin Chen, Zheng Wang 0007

In the realm of medical imaging, distinct magnetic resonance imaging (MRI) modalities can provide complementary medical insights. However, it is not uncommon for one or more modalities to be absent due to image corruption, artifacts, acquisition protocols, allergies to contrast agents, or cost constraints, posing a significant challenge for perceiving the modality-absent state in incomplete modality segmentation.In this work, we introduce a novel incomplete multi-modal segmentation framework called Modal-aware Visual Prompting (MAVP), which draws inspiration from the widely used pre-training and prompt adjustment protocol employed in natural language processing (NLP). In contrast to previous prompts that typically use textual network embeddings, we utilize embeddings as the prompts generated by a modality state classifier that focuses on the missing modality states. Additionally, we integrate modality state prompts into both the extraction stage of each modality and the modality fusion stage to facilitate intra/inter-modal adaptation. Our approach achieves state-of-the-art performance in various modality-incomplete scenarios compared to incomplete modality-specific solutions.

Cai Xu, Zehui Li, Ziyu Guan, Wei Zhao 0019, Xiangyu Song, Yue Wu 0004, Jianxin Li 0001

Most existing multi-view learning methods assume that the dimensions of different views are similar. In real-world applications, it is often the case that the dimension of a view may be extremely small compared with these of other views, resulting in an unbalanced multi-view learning problem. Previous methods for this problem have at least one of the following drawbacks: (1) despising the information of low dimensional views; (2) constructing balanced view-specific inter-instance similarity graphs or employing decision-level fusion, which cannot well learn multi-level inter-view correlations and is limited to category-related tasks such as clustering. To eliminate all these drawbacks, we present an Unbalanced Multi-view Deep Learning (UMDL) method. Considering a low dimensional view usually contains multiple patterns, we construct an overcomplete dictionary with its atoms exceeding the dimension of the original data. We transfer the original data into a combination of atoms and obtain a higher dimensional representation. We propose a sparse multi-view fusion paradigm to explicitly capture the complementarity of multi-view data in a flexible manner. Moreover, we construct positive and negative examples via balanced similarity graphs and employ contrastive learning to train UMDL in a self-supervised manner. Experiments conducted on a toy example and 7 balanced/unbalanced datasets show that UMDL outperforms baseline methods and can be well applied to downstream classification and segmentation tasks. The code is released at https://github.com/xdmvteam/UMDL.

Haiyang Yu 0004, Xiaocong Wang, Ke Niu 0004, Bin Li 0015, Xiangyang Xue 0001

Text segmentation is a crucial aspect of various text-related tasks, including text erasing, text editing, and font style transfer. In recent years, multiple text segmentation datasets, such as TextSeg focusing on Latin text segmentation and BTS on bilingual text segmentation, have been proposed. However, existing methods either disregard the annotations of text location or directly use pre-trained text detectors. In general, these methods cannot fully utilize the annotations of text location in the datasets. To explicitly incorporate text location information to guide text segmentation, we propose an end-to-end text-focused segmentation framework, where text detection and segmentation are jointly optimized. In the proposed framework, we first extract multi-level global visual features through residual convolution blocks and then predict the mask of text areas using a text detection head. Subsequently, we develop a text-focused module that compels the model to pay more attention to text areas. Specifically, we introduce two types of attention masks to extract corresponding features: text-aware and instance-aware features. Finally, we employ hierarchical Transformer encoders to fuse multi-level features and predict the text mask with a text segmentation head. To evaluate the effectiveness of our method, we conduct experiments on six text segmentation benchmarks. The experimental results demonstrate that the proposed method outperforms the previous state-of-the-art (SOTA) methods by a clear margin in most cases. The code and supplementary materials are available at https://github.com/FudanVI/FudanOCR/tree/main/text-focused-Transformers https://github.com/FudanVI/FudanOCR/tree/main/text-focused-Transformers.

Kosuke Mizufune, Shunsuke Tanaka, Toshihide Yukitake, Tatsushi Matsubayashi

Video boundary detection is a task to divide a video into several segments based on event changes such as scenes or actions. The most common evaluation is to judge whether the distance between predicted boundaries and the ground truth boundaries is lower than allowed margin and then compute F1 score. However, we found that the evaluation only by F1 measure can lead to wrong conclusions since even completely random model can achieve inflated F1 when the number of predictions is large. To design a robust metric against chance, we propose Margin Matthews Correlation Coefficient (MMCC) as an extension of Matthews Correlation Coefficient (MCC) to video boundary detection with allowed margin. Although MCC is a robust metric against chance, it is not obvious that the same is true in video boundary detection due to allowed margin. Specifically, some definitions of MCC do not keep a constant as for the number of predicted boundaries. Therefore, we design MMCC so that the expected MMCC for random guessing will be zero, based on mathematical analysis. We empirically examine if MMCC is robust against completely random guessing and oversegmentation/undersegmentation, while F1 is not.

Lei Zhao 0026, Le Han, Min Yao, Nenggan Zheng

In the field of pose estimation, keypoint representations can take the form of Gaussian heatmaps, classification vectors, or direct coordinates. However, the current networks suffer from a lack of consistency with these keypoint representations. They only accommodate these representations in the final layer, resulting in suboptimal efficiency and requiring a high number of parameters or computational resources. In this paper, we propose a simple yet efficient plug-and-play module, named the Implicit Decouple Module (IDM), which decouples features into two parts along the x-y axes and aggregates features in a direction-aware manner. This approach implicitly fuses direction-specific coordinate information, improving the consistency with the keypoint representations, especially in vector form. Furthermore, we introduce a fully convolutional backbone network, named the Implicit Decouple Network (IDN), which incorporates IDM without the need to maintain high-resolution features, dense multi-level feature fusion, or lots of repeated stages, while still achieving high performance. In experiments on the COCO dataset, our basic IDN without pre-training can outperform HRNet (28.5M) by 2.4 AP with 18.2M parameters, and even surpass some transformer-based methods. In the lightweight model scenario, our model outstrips Lite-HRNet by 3.9 AP with only 2.5M parameters. We also evaluate our model on the person instance segmentation task and other datasets, demonstrating its generality and effectiveness. http(s)://znk.ink/su/mm23idn.

Xiaodong Jin, Taiping Zhang

Temporal Action Localization (TAL) aims to predict the categories and temporal segments of all action instances in untrimmed videos, which is a critical and challenging task in the video understanding field. The performances of existing TAL methods remain unsatisfactory, due to the lack of highly effective temporal modeling and refined action proposal decoding. In this paper, we propose Multiscale Temporal Similarity Network (MTSN), a novel one-stage method for TAL, which mainly benefits from dynamic complementary modeling and temporal similarity decoding. Specifically, we first design Dynamic Complementary Context Aggregation (DCCA), a Transformer-based encoder. DCCA performs both long-range and short-range temporal modeling through different interaction range types of attention heads at each feature pyramid level, while higher-level semantic representations are effectively complemented with more short-range detail information in a dynamic fashion. Moreover, Temporal Similarity Mask (TSM) is designed to generate masks through an optimized globally-aware decoding process, including similarity cross-modeling, region-aware optimization and multiscale aggregated residual, which leads to high-quality action proposals. We conduct extensive experiments on two major TAL benchmarks: THUMOS14 and ActivityNet-1.3, where our method establishes a new state-of-the-art and significantly outperforms the previous best methods. Without bells and whistles, on THUMOS14, MTSN achieves an average mAP of 72.1% (+5.3%). On ActivityNet-1.3, MTSN reaches an average mAP of 40.7% (+3.1%), which crosses the 40% average mAP for the first time.

Xiao Liu 0022, Xiuya Shi, Lufei Chen, Linbo Qing, Chao Ren 0002

In this work, we propose PMSDSEN, a parallel multi-scale encoder-decoder network architecture for semantic segmentation, inspired by the human visual perception system's ability to aggregate contextual information in various contexts and scales. Our approach introduces the efficient Parallel Multi-Scale Detail and Semantic Encoding (PMSDSE) unit to extract detailed local information and coarse large-range relationships in parallel, enabling the recognition of object boundaries and object-level areas. By stacking multiple PMSDSEs, our network learns fine-grained details and textures along with abstract category and semantic information, effectively utilizing a larger range of surrounding context information for robust segmentation. To further enhance the network's receptive field without increasing computational complexity, the Multi-Scale Semantic Extractor (MSSE) at the end of the encoder is utilized for multi-scale semantic context extraction and detailed information encoding. Additionally, the Dynamic Weighted Feature Fusion (DWFF) strategy is employed to integrate shallow layer detail information and deep layer semantic information during the decoder stage. Our method can obtain multi-scale context from local to global, achieving efficiently low-level feature extraction to high-level semantic interpretation at different scales and in different contexts. Without bells and whistles, PMSDSEN obtains a better trade-off between accuracy and complexity on popular benchmarks, including Cityscapes and Camvid. Specifically, PMSDSEN attains 73.2% mIoU with only 0.9M parameters on the Cityscapes test set. Codes and supplementary materials link: https://github.com/liux520/PMSDSEN.

Xizhe Xue, Dongdong Yu, Lingqiao Liu, Yu Liu 0015, Satoshi Tsutsui, Ying Li 0017, Zehuan Yuan, Ping Song, Mike Zheng Shou

Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage segmentation framework, which first locates the candidate object bounding boxes and then performs instance segmentation. In this work, we instead promote a single-stage transformer-based framework for OWIS. We argue that the end-to-end training process in the single-stage framework can be more convenient for directly regularizing the localization of class-agnostic object pixels. Based on the transformer-based instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss. We show that such a consistency loss could alleviate the problem of incomplete instance annotation - a common problem in the existing OWIS datasets. We also show that the proposed loss lends itself to an effective solution to semi-supervised OWIS that could be considered an extreme case that all object annotations are absent for some images. Our extensive experiments demonstrate that the proposed method achieves impressive results in both fully-supervised and semi-supervised settings. Compared to SOTA methods, the proposed method significantly improves the AP_100 score by 4.75% in UVO dataset →UVO dataset setting and 4.05% in COCO dataset →UVO dataset setting.

Jiaming Liu, Yue Wu 0004, Maoguo Gong, Qiguang Miao, Wenping Ma 0001, Cai Xu

Existing work shows that 3D point clouds produce only about a 4% drop in semantic segmentation even at 1% random point annotation, which inspires us to further explore how to achieve better results at lower cost. As scene point clouds provide position and color information and often used in tandem as the only input, with little work going into segmentation by fusing information from dual spaces. To optimize point cloud representations, we propose a novel framework for the dual representation query network (DRQNet). The proposed framework partitions the input point cloud into position and color spaces, using the separately extracted geometric structure and semantic context to create an internal supervisory mechanism that bridges the dual spaces and fuses the information. Adopting sparsely annotated points as the query set, DRQNet provide guidance and perceptual information for multi-stage point clouds through random sampling. More, to differentiate and enhance the features generated by local neighbourhoods within multiple perceptual fields, we design a representation selection module to identify the contributions made by the position and color of each query point, and weight them adaptively according to reliability. The proposed DRQNet is robust to point cloud analysis and eliminates the effects of irregularities and disorder. Our method achieves significant performance gains on three mainstream benchmarks.

Ziyang Gong, Fuhao Li, Yupeng Deng 0002, Wenjun Shen, Xianzheng Ma, Zhenming Ji, Nan Xia

Unsupervised Domain Adaptation (UDA) for semantic segmentation has received widespread attention for its ability to transfer knowledge from the source to target domains without a high demand for annotations. However, semantic segmentation under adverse conditions still poses significant challenges for autonomous driving, as bad weather observation data may introduce unforeseeable problems. Although previous UDA works are devoted to adverse scene tasks, their adaptation process is redundant. For instance, unlabeled snow scene training data is a must for the model to achieve fair segmentation performance in snowy scenarios. We propose calling this type of adaptation process the Single to Single (STS) strategy. Clearly, STS is time-consuming and may show weaknesses in some comprehensive scenes, such as a night scene of sleet. Motivated by the concept of Domain Generalization (DG), we propose the Single to All (STA) model. Unlike DG, which trains models on one or multiple source domains without target domains, the STA model is based on UDA and employs one source domain, one target domain, and one introduced domain to achieve generalization to all adverse conditions by training on a single-scene dataset. Specifically, the STA model is advantageous as it learns from the source domain, reserves the style factors via a Reservation domain, and adapts the unified factors by the Randomization module. An Output Space Refusion module is also further incorporated to strengthen STA. Our STA achieves state-of-the-art performance in the Foggy Driving benchmark and demonstrates great domain generalizability in all conditions of the ACDC and Foggy Zurich benchmarks.

Xiaojie Li, Jianlong Wu, Shaowei He, Shuo Kang, Yue Yu 0001, Liqiang Nie, Min Zhang 0005

Self-supervised learning methods have shown significant promise in acquiring robust spatiotemporal representations from unlabeled videos. In this work, we address three critical limitations in existing self-supervised video representation learning: 1) insufficient utilization of contextual information and lifelong memory, 2) lack of fine-grained visual concept alignment, and 3) neglect of the feature distribution gap between encoders. To overcome these limitations, we propose a novel memory-enhanced predictor that leverages key-value memory networks with separate memories for the online and target encoders. This design enables the effective storage and retrieval of contextual knowledge, facilitating informed predictions and enhancing overall performance. Additionally, we introduce a visual concept alignment module that ensures fine-grained alignment of shared semantic information across segments of the same video. By employing coupled dictionary learning, we effectively decouple visual concepts, enriching the semantic representation stored in the memory networks. Our proposed approach is extensively evaluated on widely recognized benchmarks for action recognition and retrieval tasks, demonstrating its superiority in learning generalized video representations with significantly improved performance compared to existing state-of-the-art self-supervised learning methods. Code is released at https://github.com/xiaojieli0903/FGKVMemPred_video.