Current RGB-D salient object detection (RGB-D SOD) methods mainly develop a generalizable model trained by binary cross-entropy (BCE) loss based on convolutional or Transformer backbones. However, they usually exploit convolutional modules to fuse multi-modality features, with little attention paid to capturing the long-range multi-modality interactions for feature fusion. Furthermore, BCE loss does not explicitly explore intra- and inter-pixel relationships in a joint embedding space. To address these issues, we propose a cross-modality interaction parallel-transformer (CIPT) module, which better captures the long-range multi-modality interactions, generating more comprehensive fusion features. Besides, we propose a pixel-level contrastive learning (PCL) method that improves inter-pixel discrimination and intra-pixel compactness, resulting in a well-structured embedding space and a better saliency detector. Specifically, we propose an asymmetric network (TPCL) for RGB-D SOD, which consists of a Swin V2 Transformer-based backbone and a designed lightweight backbone (LDNet). Moreover, an edge-guided module and a feature enhancement (FE) module are proposed to refine the learned fusion features. Extensive experiments demonstrate that our method achieves excellent performance against 15 state-of-the-art methods on seven public datasets. We expect our work to facilitate the exploration of applying Transformer and contrastive learning for RGB-D SOD tasks. Our codes and predicted saliency maps will be released on GitHub https://github.com/TomorrowJW/TPCL_RGBDSOD .

Sentiment analysis of Chinese micro-blog based on sentiment dictionary has become a challenging research subject in the field of artificial intelligence. However, due to insufficient sentiment words, Chinese micro-blog sentiment analysis is difficult to process high accuracy. Aimed at this issue, we propose a method for constructing multiple sentiment dictionaries, which mainly constructs original sentiment dictionary, emoji dictionary, and other related dictionaries. Among them, we have innovatively constructed a Chinese micro-blog new word sentiment dictionary. Multiple sentiment dictionaries increase the coverage of sentiment words. At the same time, we further analyze semantic rule sets between Chinese micro-blog texts and take the inter-sentence analysis rules and sentence pattern analysis rules into the sentiment analysis of Chinese micro-blog, which further improves the accuracy of Chinese mi-cro-blog sentiment analysis. Finally, based on the method of multiple sentiment dictionaries and semantic rule sets, we propose an algorithm for Chinese micro-blog sentiment calculation from complex sentences to clauses and then from clauses to words, and finally combined with the emoji. This algorithm can accurately classify Chinese micro-blog into positive Chinese micro-blog, negative Chinese micro-blog, and neutral Chinese micro-blog. The experimental results show that this method has greatly improved the sentiment analysis of Chinese micro-blog.

Existing camouflaged object detection (COD) methods typically have large model parameters and computations, hindering their deployment in real-world applications. Although using lightweight backbones can help alleviate this problem, their weaker feature representation often leads to performance degradation. To address this issue, we observe that frequency information has shown effective for cumbersome networks, but its effectiveness for lightweight ones has not been thoroughly investigated. Biological studies indicate that the human visual system utilizes distinct neural pathways to respond to different frequency stimuli, contributing to specialization and efficiency. Motivated by this, we propose an efficient frequency injection module (FIM) to aid lightweight backbone features by separately injecting detailed high frequency and object-level low frequency cues at each stage. FIM can be used as a plug-and-play component in existing COD networks to enhance backbone features at a low cost. With FIM, our proposed frequency injection network (FINet) achieves competitive performance against most state-ofthe- art methods with much faster speed (692FPS for the input size of 384 x 384) and fewer parameters (3.74M). Source codes will be released at https://github.com/crrcoo/FINet.

Camouflaged object detection (COD) involves segmenting objects that share similar patterns, such as color and texture, with their surroundings. Current methods typically employ multiple well-designed modules or rely on edge cues to learn object feature representations for COD. However, these methods still struggle to capture the discriminative semantics between camouflaged objects (foreground) and background, possibly generating blurry prediction maps. To address these limitations, we propose a novel mask-and-edge co-guided separable network (MECS-Net) for COD that leverages both edge and mask cues to learn more discriminative representations and improve detection performance. Specifically, we design a mask-and-edge co-guided separable attention (MECSA) module, which consists of three flows for separately capturing edge, foreground, and background semantics. In addition, we propose a multi-scale enhancement fusion (MEF) module to aggregate multi-scale features of objects. The predictions are decoded in a top-down manner. Extensive experiments and visualizations demonstrate that our CNN-based and Transformer-based MECS-Net outperform 13 state-of-the-art methods on four popular COD datasets. Codes and results are available ^{https://github.com/TomorrowJW/MECS-Net-COD$\ast$} .

Camouflaged object detection in monocular images has garnered broad attention recently, aiming to segment objects that have high intrinsic similarity with their surroundings. Despite remarkable performance achieved by existing methods, two limitations persist: insufficient utilization of multi-level semantics at each decoding scale and a lack of “explicit” knowledge guidance in boundary learning, leading to performance drops in challenging scenarios. To address these issues, we propose a weighted dense semantic aggregation and explicit boundary modeling network. Specifically, a weighted dense semantic aggregation module is proposed to sufficiently aggregate multi-level semantics at each decoding scale, and enable the exploration of the relationship between multi-level features and camouflaged objects. An explicit boundary modeling module is developed to capture edge semantics with explicit boundary knowledge guidance and enhance the feature representation with edge cues. A detail enhanced multi-scale module is further designed to refine multi-scale features. Extensive experiments demonstrate that our proposed method achieves competitive performance against state-of-the-art methods on four benchmark datasets without excessive model complexity. Codes and results will be released at https://github.com/crrcoo/SAE-Net.