Multi-label image recognition with convolutional neural networks has achieved remarkable progress in the past few years. However, most existing multi-label image recognition methods suffer from the long-tailed data distribution problem,i.e., head categories occupy most training samples, while tailed classes have few samples. This work firstly studies the influence of long-tailed data distribution on existing multi-label image recognition methods. Based on this, two crucial issues of the existing methods are identified: 1) severe gradient imbalance between head and tailed categories, even though re-balancing strategies are adopted; 2) the lack of diversity of tail category training samples. To tackle the first issue, this paper proposes a group sampling strategy to create group-wise balanced data distribution. Meanwhile, a dynamic gradient balancing loss is proposed to equalize the gradient for all categories. To tackle the second issue, this paper proposes a diversity enhancement module to fuse the information across all categories, preventing the network from overfitting tail classes. Furthermore, it also balances the gradient, promoting the discriminability of learned classifiers. Our method significantly outperforms the baseline method and achieves competitive performance with state-of-the-art methods on VOC-LT and COCO-LT datasets. Extensive ablation studies are conducted to verify the effectiveness of the essential proposals.

Edge segmentation is well-known to be subjective due to personalized annotation styles and preferred granular-ity. However, most existing deterministic edge detection methods produce only a single edge map for one input image. We argue that generating multiple edge maps is more reasonable than generating a single one considering the subjectivity and ambiguity of the edges. Thus motivated, in this paper we propose multiple granularity edge detection, called MuGE, which can produce a wide range of edge maps, from approximate object contours to fine texture edges. Specifically, we first propose to design an edge granularity network to estimate the edge granularity from an individual edge annotation. Subsequently, to guide the generation of diversified edge maps, we integrate such edge granularity into the multi-scale feature maps in the spatial domain. Meanwhile, we decompose the feature maps into low-frequency and high-frequency parts, where the encoded edge granularity is further fused into the high-frequency part to achieve more precise control over the details of the produced edge maps. Compared to previous methods, MuGE is able to not only generate multiple edge maps at different controllable granularities but also achieve a com-petitive performance on the BSDS500 and Multicue benchmark datasets.

Semantic edge detection (SED) is pivotal for the precise demarcation of object boundaries, yet it faces ongoing challenges due to the prevalence of low-quality labels in current methods. In this paper, we present a novel solution to bolster SED through the encoding of both language and image data. Distinct from antecedent language-driven techniques, which predominantly utilize static elements such as dataset labels, our method taps into the dynamic language content that details the objects in each image and their interrelations. By encoding this varied input, we generate integrated features that utilize semantic insights to refine the high-level image features and the ultimate mask representations. This advancement improves the quality of these features and elevates SED performance. Experimental evaluation on benchmark datasets, including SBD and Cityscape, showcases the efficacy of our method, achieving leading ODS F-scores of 79.0 and 76.0, respectively. Our approach signifies a notable advancement in SED technology by seamlessly integrating multimodal textual information, embracing both static and dynamic aspects.

Multi-view clustering is a long-standing important task, however, it remains challenging to exploit valuable information from the complex multi-view data located in diverse high-dimensional spaces. The core issue is the effective collaboration of multiple views to holistically uncover the essential correlations between multi-view data through graph learning. Furthermore, it is indispensable for most existing methods to introduce an additional clustering step to produce the final clusters, which evidently reduces the uniform relationship between graph learning and clustering. Based on the above considerations, in this paper, we present a novel method named multi-view clustering via graph collaboration (MCGC). Based on the low-dimensional representation space developed by MCGC, it first perceives the correlations between samples in each individual view under the supervision of the Hilbert-Schmidt independence criterion (HSIC). Then, MCGC proposes learning a consensus graph by adaptively collaborating between all the views, which is able to uncover the essential structure of the multi-view data. Meanwhile, by imposing the rank constraint on the Laplacian matrix of the consensus graph to partition the multi-view data naturally into the required number of clusters, the optimal clustering results can be obtained directly without any postprocessing steps. Finally, the resulting optimization problem is solved by an alternating optimization scheme with guaranteed fast convergence. Extensive experiments on 5 benchmark multi-view datasets demonstrate that MCGC markedly outperforms the state-of-the-art baselines.

Contour detection plays an important role in both academic research and real-world applications. As the basic building block of many applications, its accuracy and efficiency highly influence the subsequent stages. In this work, we propose a novel lightweight system for contour detection that achieves state-of-the-art performance while keeps ultra-slim model size. The proposed method is built on an efficient encoder in a bottom-up/top-down fashion. Specially, we propose a novel decoder that compresses side features from an encoder and effectively decodes compact contextual information for high-accurate boundary localization. Besides, we propose a novel loss function that is able to assist a model to produce crisp object boundaries.

Object contour detection is the fundamental and preprocessing step for multimedia applications such as icon generation, object segmentation, and tracking. The quality of contour prediction is of great importance in these applications since it affects the subsequent process. In this work, we aim to develop a high-performance contour detection system. We first propose a novel yet very effective loss function for contour detection. The proposed loss function is capable of penalizing the distance of contour-structure similarity between each pair of prediction and ground-truth. Moreover, to better distinguishing object contours and background textures, we introduce a novel convolutional encoder-decoder network. Within the network, we present a hyper module that captures dense connections among high-level features and produces effective semantic information. Then the information is progressively propagated and fused with low-level features. We conduct extensive experiments on the BSDS500 and Multi-Cue datasets, the results show significant improvement against the state-of-the-art competitors. We further demonstrate the benefit of our DSCD method for crowd counting.