Real-time video frame interpolation (VFI) is very useful in video processing, media players, and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI method, RIFE uses a neural network named IFNet that can estimate the intermediate flows end-to-end with much faster speed. A privileged distillation scheme is designed for stable IFNet training and improve the overall performance. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation with the temporal encoding input. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. Compared with the popular SuperSlomo and DAIN methods, RIFE is 4--27 times faster and produces better results. Furthermore, RIFE can be extended to wider applications thanks to temporal encoding.
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Human relighting is a highly desirable yet challenging task. Existing works either require expensive one-light-at-a-time (OLAT) captured data using light stage or cannot freely change the viewpoints of the rendered body. In this work, we propose a principled framework, Relighting4D, that enables free-viewpoints relighting from only human videos under unknown illuminations. Our key insight is that the space-time varying geometry and reflectance of the human body can be decomposed as a set of neural fields of normal, occlusion, diffuse, and specular maps. These neural fields are further integrated into reflectance-aware physically based rendering, where each vertex in the neural field absorbs and reflects the light from the environment. The whole framework can be learned from videos in a self-supervised manner, with physically informed priors designed for regularization. Extensive experiments on both real and synthetic datasets demonstrate that our framework is capable of relighting dynamic human actors with free-viewpoints. Codes are available at https://github.com/FrozenBurning/Relighting4D.
Current person image retrieval methods have achieved great improvements in accuracy metrics. However, they rarely describe the reliability of the prediction. In this paper, we propose an Uncertainty-Aware Learning (UAL) method to remedy this issue. UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the “noise” inherent in the sample, while model uncertainty depicts the model’s confidence in the sample’s prediction. Specifically, in UAL, (1) we propose a sampling-free data uncertainty learning method to adaptively assign weights to different samples during training, down-weighting the low-quality ambiguous samples. (2) we leverage the Bayesian framework to model the model uncertainty by assuming the parameters of the network follow a Bernoulli distribution. (3) the data uncertainty and the model uncertainty are jointly learned in a unified network, and they serve as two fundamental criteria for the reliability assessment: if a probe is high-quality (low data uncertainty) and the model is confident in the prediction of the probe (low model uncertainty), the final ranking will be assessed as reliable. Experiments under the risk-controlled settings and the multi-query settings show the proposed reliability assessment is effective. Our method also shows superior performance on three challenging benchmarks under the vanilla single query settings. The code is available at: https://github.com/dcp15/UAL
Recent works on text-based localization of moments have shown high accuracy on several benchmark datasets. However, these approaches are trained and evaluated relying on the assumption that the localization system, during testing, will only encounter events that are available in the training set (i.e., seen events). As a result, these models are optimized for a fixed set of seen events and they are unlikely to generalize to the practical requirement of localizing a wider range of events, some of which may be unseen. Moreover, acquiring videos and text comprising all possible scenarios for training is not practical. In this regard, this paper introduces and tackles the problem of text-based temporal localization of novel/unseen events. Our goal is to temporally localize video moments based on text queries, where both the video moments and text queries are not observed/available during training. Towards solving this problem, we formulate the inference task of text-based localization of moments as a relational prediction problem, hypothesizing a conceptual relation between semantically relevant moments, e.g., a temporally relevant moment corresponding to an unseen text query and a moment corresponding to a seen text query may contain shared concepts. The likelihood of a candidate moment to be the correct one based on an unseen text query will depend on its relevance to the moment corresponding to the semantically most relevant seen query. Empirical results on two text-based moment localization datasets show that our proposed approach can reach up to 15% absolute improvement in performance compared to existing localization approaches.
Video object re-identification (reID) aims at re-identifying the same object under non-overlapping cameras by matching the video tracklets with cropped video frames. The key point is how to make full use of spatio-temporal interactions to extract more accurate representation. However, there are dilemmas within existing approaches: (1) 3D solutions model the spatio-temporal interaction but are often troubled with the misalignment of adjacent frames, and (2) 2D solutions adopt a divide-and-conquer strategy against the misalignment but cannot take advantage of the spatio-temporal interactions. To address the above problems, we propose a Contextual Alignment Vision Transformer (\textbf{CAViT}) to the spatio-temporal interaction with a 2D solution. It contains a Multi-shape Patch Embedding (\textbf{MPE}) module and a Temporal Shift Attention (\textbf{TSA}) module. MPE is designed to retain spatial semantic information against the misalignment caused by pose, occlusion, or misdetection. TSA is designed to achieve contextual spatial semantic feature alignment and jointly model spatio-temporal clues. We further propose a Residual Position Embedding (\textbf{RPE}) to guide TSA in focusing on the temporal saliency clues. Experimental results on five video person reID datasets demonstrate the superiority of the proposed CAViT. Additionally, the experiment conducted on VVeRI-901-trial also shows the effectiveness of CAViT for the video vehicle reID. Our code is available on \href{https://github.com/KimWu1994/CAViT}{https://github.com/KimWu1994/CAViT}.
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM) based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON) module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods. Codes are available at https://github.com/NJUST-VIPGroup/SEMICON.
We propose a generic feature compression method for Approximate Nearest Neighbor Search (ANNS) problems, which speeds up existing ANNS methods in a plug-and-play manner. Specifically, based on transformer, we propose a new network structure to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy. Specifically, we use multiple compression projections to cast the feature into many low dimensional spaces, and then use transformer to globally optimize these projections such that the features are well compressed following the guidance from our loss function. The loss function is designed to assign high weights on point pairs that are close in original feature space, and keep their distances in projected space. Keeping these distances helps maintain the eventual top-k retrieval accuracy, and down weighting others creates room for feature compression. In experiments, we run our compression method on public datasets, and use the compressed features in graph based, product quantization and scalar quantization based ANNS solutions. Experimental results show that our compression method can significantly improve the efficiency of these methods while preserves or even improves search accuracy, suggesting its broad potential impact on real world applications. Source code is available at https://github.com/hkzhang91/CCST
Retrieving desired videos using natural language queries has attracted increasing attention in research and industry fields as a huge number of videos appear on the internet. Some existing methods attempted to address this video retrieval problem by exploiting multi-modal information, especially audio-visual data of videos. However, many videos often have mismatched visual and audio cues for several reasons including background music, noise, and even missing sound. Therefore, the naive fusion of such mismatched visual and audio cues can negatively affect the semantic embedding of video scenes. Mismatch condition can be categorized into two cases: (i) Audio itself does not exist, (ii) Audio exists but does not match with visual. To deal with (i), we introduce audio-visual associative memory (AVA-Memory) to associate audio cues even from videos without audio data. The associated audio cues can guide the video embedding feature to be aware of audio information even in the missing audio condition. To address (ii), we propose audio embedding adjustment by considering the degree of matching between visual and audio data. In this procedure, constructed AVA-Memory enables to figure out how well the visual and audio in the video are matched and to adjust the weighting between actual audio and associated audio. Experimental results show that the proposed method outperforms other state-of-the-art video retrieval methods. Further, we validate the effectiveness of the proposed network designs with ablation studies and analyses.
Visible-infrared person re-identification (VI-ReID) is a challenging task due to the large cross-modality discrepancies and intra-class variations. Existing works mainly focus on learning modality-shared representations by embedding different modalities into the same feature space. However, these methods usually damage the modality-specific information and identification information contained in the features. To alleviate the above issues, we propose a novel Cross-Modality Transformer (CMT) to jointly explore a modality-level alignment module and an instance-level module for VI-ReID. The proposed CMT enjoys several merits. First, the modality-level alignment module is designed to compensate for the missing modality-specific information via a Transformer encoder-decoder architecture. Second, we propose an instance-level alignment module to adaptively adjust the sample features, which is achieved by a query-adaptive feature modulation. To the best of our knowledge, this is the first work to exploit a cross-modality transformer to achieve the modality compensation for VI-ReID. Extensive experimental results on two standard benchmarks demonstrate that our CMT performs favorably against the state-of-the-art methods.
Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-identification
PDF ↗Visible-Infrared Re-Identification (VI-ReID) is challenging in image retrievals. The modality discrepancy will easily make huge intra-class variations. Most existing methods either bridge different modalities through modality-invariance or generate the intermediate modality for better performance. Differently, this paper proposes a novel framework, named Modality Synergy Complement Learning Network (MSCLNet) with Cascaded Aggregation. Its basic idea is to synergize two modalities to construct diverse representations of identity-discriminative semantics and less noise. Then, we complement synergistic representations under the advantages of the two modalities. Furthermore, we propose the Cascaded Aggregation strategy for fine-grained optimization of the feature distribution, which progressively aggregates feature embeddings from the subclass, intra-class, and inter-class. Extensive experiments on SYSU-MM01 and RegDB datasets show that MSCLNet outperforms the state-of-the-art by a large margin. On the large-scale SYSU-MM01 dataset, our model can achieve 76.99% and 71.64% in terms of Rank-1 accuracy and mAP value. Our code will be available at https://github.com/bitreidgroup/VI-ReID-MSCLNet
In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval. Different from previous research that considers feature fusion only at one end, let it be video or text, we aim for feature fusion for both ends within a unified framework. We hypothesize that optimizing the convex combination of the features is preferred to modeling their correlations by computationally heavy multi-head self attention. We propose Lightweight Attentional Feature Fusion (LAFF). LAFF performs feature fusion at both early and late stages and at both video and text ends, making it a powerful method for exploiting diverse (off-the-shelf) features. The interpretability of LAFF can be used for feature selection. Extensive experiments on five public benchmark sets (MSR-VTT, MSVD, TGIF, VATEX and TRECVID AVS 2016-2020) justify LAFF as a new baseline for text-to-video retrieval.
RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-Supervised Learning
PDF ↗Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision. Several algorithms have been developed and obtained considerable success. However, most existing methods have unpleasant performance in the hazy scenario due to poor visibility. Though some strategies are possible to resolve this problem, they still have room to be improved due to the limited performance in real-world scenarios and the lack of real-world clear ground truth. Thus, to resolve this problem, inspired by CycleGAN, we construct a training paradigm called \textbf{RVSL} which integrates ReID and domain transformation techniques. The network is trained on semi-supervised fashion and does not require to employ the ID labels and the corresponding clear ground truths to learn hazy vehicle ReID mission in the real-world haze scenes. To further constrain the unsupervised learning process effectively, several losses are developed. Experimental results on synthetic and real-world datasets indicate that the proposed method can achieve state-of-the-art performance on hazy vehicle ReID problems. It is worth mentioning that although the proposed method is trained without real-world label information, it can achieve competitive performance compared to existing supervised methods trained on complete label information.
There has been a recent explosion of large-scale image-text datasets, as images with alt-text captions can be easily obtained online. Obtaining large-scale, high quality data for video in the form of text-video and text-audio pairs however, is more challenging. To close this gap we propose a new video mining pipeline which involves transferring captions from image captioning datasets to video clips with no additional manual effort. Using this pipeline, we create a new large-scale, weakly labelled audio-video captioning dataset consisting of millions of paired clips and captions. We show that training a multimodal transformed based model on this data achieves competitive performance on video retrieval and video captioning, matching or even outperforming HowTo100M pretraining with 20x fewer clips. We also show that our mined clips are suitable for text-audio pretraining, and achieve state of the art results for the task of audio retrieval.
Strong image search models can be learned for a specific domain, ie. set of labels, provided that some labeled images of that domain are available. A practical visual search model, however, should be versatile enough to solve multiple retrieval tasks simultaneously, even if those cover very different specialized domains. Additionally, it should be able to benefit from even unlabeled images from these various retrieval tasks. This is the more practical scenario that we consider in this paper. We address it with the proposed Grappa, an approach that starts from a strong pretrained model, and adapts it to tackle multiple retrieval tasks concurrently, using only unlabeled images from the different task domains. We extend the pretrained model with multiple independently trained sets of adaptors that use pseudo-label sets of different sizes, effectively mimicking different pseudo-granularities. We reconcile all adaptor sets into a single unified model suited for all retrieval tasks by learning fusion layers that we guide by propagating pseudo-granularity attentions across neighbors in the feature space. Results on a benchmark composed of six heterogeneous retrieval tasks show that the unsupervised Grappa model improves the zero-shot performance of a state-of-the-art self-supervised learning model, and in some places reaches or improves over a task label-aware oracle that selects the most fitting pseudo-granularity per task.
Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time, which is a realistic but challenging problem. In contrast to methods assuming an identical model for different domains, Mixture of Experts (MoE) exploits multiple domain-specific networks for leveraging complementary information between domains, obtaining impressive results. However, prior MoE-based DG ReID methods suffer from a large model size with the increase of the number of source domains, and most of them overlook the exploitation of domain-invariant characteristics. To handle the two issues above, this paper presents a new approach called Mimic Embedding via adapTive Aggregation (META) for DG person ReID. To avoid the large model size, experts in META do not adopt a branch network for each source domain but share all the parameters except for the batch normalization layers. Besides multiple experts, META leverages Instance Normalization (IN) and introduces it into a global branch to pursue invariant features across domains. Meanwhile, META considers the relevance of an unseen target sample and source domains via normalization statistics and develops an aggregation module to adaptively integrate multiple experts for mimicking unseen target domain. Benefiting from a proposed consistency loss and an episodic training algorithm, META is expected to mimic embedding for a truly unseen target domain. Extensive experiments verify that META surpasses state-of-the-art DG person ReID methods by a large margin.
In hash-based image retrieval systems, degraded or transformed inputs usually generate different codes from the original, deteriorating the retrieval accuracy. To mitigate this issue, data augmentation can be applied during training. However, even if augmented samples of an image are similar in real feature space, the quantization can scatter them far away in Hamming space. This results in representation discrepancies that can impede training and degrade performance. In this work, we propose a novel self-distilled hashing scheme to minimize the discrepancy while exploiting the potential of augmented data. By transferring the hash knowledge of the weakly-transformed samples to the strong ones, we make the hash code insensitive to various transformations. We also introduce hash proxy-based similarity learning and binary cross entropy-based quantization loss to provide fine quality hash codes. Ultimately, we construct a deep hashing framework that not only improves the existing deep hashing approaches, but also achieves the state-of-the-art retrieval results. Extensive experiments are conducted and confirm the effectiveness of our work.
The misalignment of features caused by pose and viewpoint variances is a crucial problem in Vehicle Re-Identification (ReID). Previous methods align the features by structuring the vehicles from pre-defined vehicle parts (such as logos, lights, windows, etc.) or vehicle attributes, which are inefficient because of additional manual annotation. To align the features without requirements of additional annotation, this paper proposes a Unstructured Feature Decoupling Network (UFDN), which consists of a transformer-based feature decomposing head (TDH) and a novel cluster-based decoupling constraint (CDC). Different from the structured knowledge used in previous decoupling methods, we aim to achieve more flexible unstructured decoupled features with diverse discriminative information as shown in Fig. 1. The self-attention mechanism in the decomposing head helps the model preliminarily learn the discriminative decomposed features in a global scope. To further learn diverse but aligned decoupled features, we introduce a cluster-based decoupling constraint consisting of a diversity constraint and an alignment constraint. Furthermore, we improve the alignment constraint into a modulated one to eliminate the negative impact of the outlier features that cannot align the clusters in semantic. Extensive experiments show the proposed UFDN achieves state-of-the-art performance on three popular Vehicle ReID benchmarks with both CNN and Transformer backbones.
Text-Video retrieval is a task of great practical value and has received increasing attention, among which learning spatial-temporal video representation is one of the research hotspots. The video encoders in the state-of-the-art video retrieval models usually directly adopt the pre-trained vision backbones with the network structure fixed, they therefore can not be further improved to produce the fine-grained spatial-temporal video representation. In this paper, we propose Token Shift and Selection Network (TS2-Net), a novel token shift and selection transformer architecture, which dynamically adjusts the token sequence and selects informative tokens in both temporal and spatial dimensions from input video samples. The token shift module temporally shifts the whole token features back-and-forth across adjacent frames, to preserve the complete token representation and capture subtle movements. Then the token selection module selects tokens that contribute most to local spatial semantics. Based on thorough experiments, the proposed TS2-Net achieves state-of-the-art performance on major text-video retrieval benchmarks, including new records on MSRVTT, VATEX, LSMDC, ActivityNet, and DiDeMo. Code is available at https://github.com/yuqi657/ts2_net.
Person search is a challenging task which aims to achieve joint pedestrian detection and person re-identification (ReID). Previous works have made significant advances under fully and weakly supervised settings. However, existing methods ignore the generalization ability of the person search models. In this paper, we take a further step and present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source domain to the unlabeled target domain. Two major challenges arises under this new setting: one is how to simultaneously solve the domain misalignment issue for both detection and Re-ID tasks, and the other is how to train the ReID subtask without reliable detection results on the target domain. To address these challenges, we propose a strong baseline framework with two dedicated designs. 1) We design a domain alignment module including image-level and task-sensitive instance-level alignments, to minimize the domain discrepancy. 2) We take full advantage of the unlabeled data with a dynamic clustering strategy, and employ pseudo bounding boxes to support ReID and detection training on the target domain. With the above designs, our framework achieves 34.7% in mAP and 80.6% in top-1 on PRW dataset, surpassing the direct transferring baseline by a large margin. Surprisingly, the performance of our unsupervised DAPS model even surpasses some of the fully and weakly supervised methods. The code is available at https://github.com/caposerenity/DAPS.
Dynamically Transformed Instance Normalization Network for Generalizable Person Re-identification
PDF ↗Existing person re-identification methods often suffer significant performance degradation on unseen domains, which fuels interest in domain generalizable person re-identification (DG-PReID). As an effective technology to alleviate domain variance, the Instance Normalization (IN) has been widely employed in many existing works. However, IN also suffers from the limitation of eliminating discriminative patterns that might be useful for a particular domain or instance. In this work, we propose a new normalization scheme called Dynamically Transformed Instance Normalization (DTIN) to alleviate the drawback of IN. Our idea is to employ dynamic convolution to allow the unnormalized feature to control the transformation of the normalized features into new representations. In this way, we can ensure the network has sufficient flexibility to strike the right balance between eliminating irrelevant domain-specific features and adapting to individual domains or instances. We further utilize a multi-task learning strategy to train the model, ensuring it can adaptively produce discriminative feature representations for an arbitrary domain. Our results show a great domain generation capability and achieve state-of-the-art performance on three mainstream DG-PReID settings.