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Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development, categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices., Comment: This paper provides an outlook on FL development as part of the ICASSP 2022 special session entitled "Frontiers of Federated Learning: Applications, Challenges, and Opportunities"

We propose a new structured pruning framework for compressing Deep Neural Networks (DNNs) with skip connections, based on measuring the statistical dependency of hidden layers and predicted outputs. The dependence measure defined by the energy statistics of hidden layers serves as a model-free measure of information between the feature maps and the output of the network. The estimated dependence measure is subsequently used to prune a collection of redundant and uninformative layers. Model-freeness of our measure guarantees that no parametric assumptions on the feature map distribution are required, making it computationally appealing for very high dimensional feature space in DNNs. Extensive numerical experiments on various architectures show the efficacy of the proposed pruning approach with competitive performance to state-of-the-art methods.

One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch. The main difficulty lies in the situation that the class label of the query patch and its respective examples are not available in the training data. Our main idea leverages the concept of language translation to boost metric-learning-based detection methods. Specifically, we emulate the language translation process to adaptively translate the feature of each object proposal to better correlate the given query feature for discriminating the class-similarity among the proposal-query pairs. To this end, we propose the Adaptive Image Transformer (AIT) module that deploys an attention-based encoder-decoder architecture to simultaneously explore intra-coder and inter-coder (i.e., each proposal-query pair) attention. The adaptive nature of our design turns out to be flexible and effective in addressing the one-shot learning scenario. With the informative attention cues, the proposed model excels in predicting the class-similarity between the target image proposals and the query image patch. Though conceptually simple, our model significantly outperforms a state-of-the-art technique, improving the unseen-class object classification from 63.8 mAP and 22.0 AP50 to 72.2 mAP and 24.3 AP50 on the PASCAL-VOC and MS-COCO benchmark datasets, respectively.

This paper aims to tackle the problem of referring image segmentation, which is targeted at reasoning the region of interest referred by a query natural language sentence. One key issue to address the referring image segmentation is how to establish the cross-modal representation for encoding the two modalities, namely, the query sentence and the input image. Most existing methods are designed to concatenate the features from each modality or to gradually encode the cross-modal representation concerning each word’s effect. In contrast, our approach leverages the correlation between the two modalities for constructing the cross-modal representation. To make the resulting cross-modal representation more discriminative for the segmentation task, we propose a novel mechanism of language-driven attention to encode the cross-modal representation for reflecting the attention between every single visual element and the entire query sentence. The proposed mechanism, named as Language-Driven Attention (LDA), first decouples the cross-modal correlation to channel-attention and spatial-attention and then integrates the two attentions for obtaining the cross-modal representation. The channel attention and the spatial attention respectively reveal how sensitive each channel or each pixel of a particular feature map is with respect to the query sentence. With a proper fusion of the two kinds of feature attention, the proposed LDA model can effectively guide the generation of the final cross-modal representation. The resulting representation is further strengthened for capturing the multi-receptive-field and multi-level-semantic for the intended segmentation. We assess our referring image segmentation model on four public benchmark datasets, and the experimental results show that our model achieves state-of-the-art performance

Temporal action proposal generation (TAPG) is a challenging problem for analyzing video content. It aims to localize the video segments which are likely to contain actions or events. Intuitively, making a satisfying prediction of these video segments is directly relies on their representation quality. A typical representation of a video segment is applying a two-stream feature, which comprises appearance and motion information. Rather than directly concatenating the two-stream features as the previous methods, we illustrate a feature-aggregation network (FA-Net) concerning the feature-relation among neighboring video segments for obtaining the high-quality representation that better characterizing the actions or events. Further, we design a feature-expansion network (FE-Net) to extract multi-granularity features for retrieving the proposals of high action-instance covering confidence. We evaluate our approach on two challenging datasets: ActivityNet-1.3 and THUMOS-14. The experiments showed that the proposed approach consistently outperforms the existing state-of-the-art TAPG methods.