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Visible-infrared person re-identification (VI-ReID) is challenging due to the large modality discrepancy between visible and infrared images. Existing methods mainly focus on learning modality-shared representations by embedding images from different modalities into a common feature space, in which some discriminative modality information is discarded. Different from these methods, in this paper, we propose a novel Modality-Specific Memory Network (MSMNet) to complete the missing modality information and aggregate visible and infrared modality features into a unified feature space for the VI-ReID task. The proposed model enjoys several merits. First, it can exploit the missing modality information to alleviate the modality discrepancy when only the single-modality input is provided. To the best of our knowledge, this is the first work to exploit the missing modality information completion and alleviate the modality discrepancy with the memory network. Second, to guide the learning process of the memory network, we design three effective learning strategies, including feature consistency, memory representativeness and structural alignment. By incorporating these learning strategies in a unified model, the memory network can be well learned to propagate identity-related information between modalities and boost the VI-ReID performance. Extensive experimental results on two standard benchmarks (SYSU-MM01 and RegDB) demonstrate that the proposed MSMNet performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recent studies have pointed out that many well-developed Visual Question Answering (VQA) models are heavily affected by the language prior problem. It refers to making predictions based on the co-occurrence pattern between textual questions and answers instead of reasoning upon visual contents. To tackle this problem, most existing methods focus on strengthening the visual feature learning capability to reduce this text shortcut influence on model decisions. However, few efforts have been devoted to analyzing its inherent cause and providing an explicit interpretation. It thus lacks a good guidance for the research community to move forward in a purposeful way, resulting in model construction perplexity towards overcoming this non-trivial problem. In this paper, we propose to interpret the language prior problem in VQA from a class-imbalance view. Concretely, we design a novel interpretation scheme whereby the loss of mis-predicted frequent and sparse answers from the same question type is distinctly exhibited during the late training phase. It explicitly reveals why the VQA model tends to produce a frequent yet obviously wrong answer, to a given question whose right answer is sparse in the training set. Based upon this observation, we further propose a novel loss re-scaling approach to assign different weights to each answer according to the training data statistics for estimating the final loss. We apply our approach into six strong baselines and the experimental results on two VQA-CP benchmark datasets evidently demonstrate its effectiveness. In addition, we also justify the validity of the class imbalance interpretation scheme on other computer vision tasks, such as face recognition and image classification. [ABSTRACT FROM AUTHOR]

We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the relations in motions at various spatial and temporal scales. Different from many previous hierarchical structures, our multiscale spatio-temporal graph is built in a data-adaptive fashion, which captures nonphysical, yet motion-based relations. The key module of MST-GNN is a multiscale spatio-temporal graph computational unit (MST-GCU) based on the trainable graph structure. MST-GCU embeds underlying features at individual scales and then fuses features across scales to obtain a comprehensive representation. The overall architecture of MST-GNN follows an encoder-decoder framework, where the encoder consists of a sequence of MST-GCUs to learn the spatial and temporal features of motions, and the decoder uses a graph-based attention gate recurrent unit (GA-GRU) to generate future poses. Extensive experiments are conducted to show that the proposed MST-GNN outperforms state-of-the-art methods in both short and long-term motion prediction on the datasets of Human 3.6M, CMU Mocap and 3DPW, where MST-GNN outperforms previous works by 5.33% and 3.67% of mean angle errors in average for short-term and long-term prediction on Human 3.6M, and by 11.84% and 4.71% of mean angle errors for short-term and long-term prediction on CMU Mocap, and by 1.13% of mean angle errors on 3DPW in average, respectively. We further investigate the learned multiscale graphs for interpretability. [ABSTRACT FROM AUTHOR]

Conversational image search, a revolutionary search mode, is able to interactively induce the user response to clarify their intents step by step. Several efforts have been dedicated to the conversation part, namely automatically asking the right question at the right time for user preference elicitation, while few studies focus on the image search part given the well-prepared conversational query. In this paper, we work towards conversational image search, which is much difficult compared to the traditional image search task, due to the following challenges: 1) understanding complex user intents from a multimodal conversational query; 2) utilizing multiform knowledge associated images from a memory network; and 3) enhancing the image representation with distilled knowledge. To address these problems, in this paper, we present a novel contextuaL imAge seaRch sCHeme (LARCH for short), consisting of three components. In the first component, we design a multimodal hierarchical graph-based neural network, which learns the conversational query embedding for better user intent understanding. As to the second one, we devise a multi-form knowledge embedding memory network to unify heterogeneous knowledge structures into a homogeneous base that greatly facilitates relevant knowledge retrieval. In the third component, we learn the knowledge-enhanced image representation via a novel gated neural network, which selects the useful knowledge from retrieved relevant one. Extensive experiments have shown that our LARCH yields significant performance over an extended benchmark dataset. As a side contribution, we have released the data, codes, and parameter settings to facilitate other researchers in the conversational image search community. [ABSTRACT FROM AUTHOR]

Weakly supervised temporal action detection has better scalability and practicability than fully supervised action detection in reality deployment. However, it is difficult to learn a robust model without temporal action boundary annotations. In this paper, we propose an en-to-end Multi-Scale Structure-Aware Network (MSA-Net) for weakly supervised temporal action detection by exploring both the global structure information of a video and the local structure information of actions. The proposed SA-Net enjoys several merits. First, to localize actions with different durations, each video is encoded into feature representations with different temporal scales. Second, based on the multi-scale feature representation, the proposed model has designed two effective structure modeling mechanisms including global structure modeling and local structure modeling, which can effectively learn discriminative structure aware representations for robust and complete action detection. To the best of our knowledge, this is the first work to fully explore the global and local structure information in a unified deep model for weakly supervised action detection. And extensive experimental results on two benchmark datasets demonstrate that the proposed MSA-Net performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Gait recognition aims to recognize persons’ identities by walking styles. Gait recognition has unique advantages due to its characteristics of non-contact and long-distance compared with face and fingerprint recognition. Cross-view gait recognition is a challenge task because view variance may produce large impact on gait silhouettes. The development of deep learning has promoted cross-view gait recognition performances to a higher level. However, performances of existing deep learning-based cross-view gait recognition methods are limited by lack of gait samples under different views. In this paper, we take a Multi-view Gait Generative Adversarial Network (MvGGAN) to generate fake gait samples to extend existing gait datasets, which provides adequate gait samples for deep learning-based cross-view gait recognition methods. The proposed MvGGAN method trains a single generator for all view pairs involved in single or multiple datasets. Moreover, we perform domain alignment based on projected maximum mean discrepancy to reduce the influence of distribution divergence caused by sample generation. The experimental results on CASIA-B and OUMVLP dataset demonstrate that fake gait samples generated by the proposed MvGGAN method can improve performances of existing state-of-the-art cross-view gait recognition methods obviously on both single-dataset and cross-dataset evaluation settings. [ABSTRACT FROM AUTHOR]

Natural language moment localization aims at localizing video clips according to a natural language description. The key to this challenging task lies in modeling the relationship between verbal descriptions and visual contents. Existing approaches often sample a number of clips from the video, and individually determine how each of them is related to the query sentence. However, this strategy can fail dramatically, in particular when the query sentence refers to some visual elements that appear outside of, or even are distant from, the target clip. In this paper, we address this issue by designing an Interaction-Integrated Network (I2N), which contains a few Interaction-Integrated Cells (I2Cs). The idea lies in the observation that the query sentence not only provides a description to the video clip, but also contains semantic cues on the structure of the entire video. Based on this, I2Cs go one step beyond modeling short-term contexts in the time domain by encoding long-term video content into every frame feature. By stacking a few I2Cs, the obtained network, I2N, enjoys an improved ability of inference, brought by both (I) multi-level correspondence between vision and language and (II) more accurate cross-modal alignment. When evaluated on a challenging video moment localization dataset named DiDeMo, I2N outperforms the state-of-the-art approach by a clear margin of 1.98%. On other two challenging datasets, Charades-STA and TACoS, I2N also reports competitive performance. [ABSTRACT FROM AUTHOR]

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval aims to identify relevant data across different modalities. In this work, we are dedicated to cross-modal retrieval between images and text sentences, which is formulated into similarity measurement for each image-text pair. To this end, we propose a Cross-modal Relation Guided Network (CRGN) to embed image and text into a latent feature space. The CRGN model uses GRU to extract text feature and ResNet model to learn the globally guided image feature. Based on the global feature guiding and sentence generation learning, the relation between image regions can be modeled. The final image embedding is generated by a relation embedding module with an attention mechanism. With the image embeddings and text embeddings, we conduct cross-modal retrieval based on the cosine similarity. The learned embedding space well captures the inherent relevance between image and text. We evaluate our approach with extensive experiments on two public benchmark datasets, i.e., MS-COCO and Flickr30K. Experimental results demonstrate that our approach achieves better or comparable performance with the state-of-the-art methods with notable efficiency. [ABSTRACT FROM AUTHOR]