Multimodal Emotion Recognition in Conversation (MERC) aims to predict speakers’ emotions by integrating textual, acoustic, and visual cues. Existing approaches either struggle to capture complex cross‑modal interactions or experience gradient conflicts and unstable training when using deeper architectures. To address these issues, we propose Cross-Space Synergy (CSS), which couples a representation component with an optimization component. Synergistic Polynomial Fusion (SPF) serves the representation role, leveraging low-rank tensor factorization to efficiently capture high-order cross-modal interactions. Pareto Gradient Modulator (PGM) serves the optimization role, steering updates along Pareto-optimal directions across competing objectives to alleviate gradient conflicts and improve stability. Experiments show that CSS outperforms existing representative methods on IEMOCAP and MELD in both accuracy and training stability, demonstrating its effectiveness in complex multimodal scenarios.

Efficient fusion of infrared and visible images is of critical importance for real-time applications such as autonomous driving. While deep learning-based fusion methods have demonstrated significant improvements in fusion quality in recent years, current network architectures still exhibit unsatisfactory computational complexity and processing speed. To reduce computational complexity and improve fusion efficiency, mask-based methods or approaches driven by downstream tasks often prioritize key regions. However, such methods tend to overemphasize target objects, potentially overlooking contextually significant elements. To address this limitation and achieve more effective fusion, we propose BEFuse, a decoupled two-stage training strategy with an end-to-end inference framework. BEFuse extracts shallow features and gradient information from images in the first stage via a cross-modal image segmentation subnetwork. In the fusion stage, we use the Hadamard product to map features to an implicit quadratic feature space, combining feature similarity and gradient mask information, allowing automatic adjustment of loss weights and improving fusion accuracy. Experiments on four datasets (MSRS, TNO, RoadScene, and M3FD) show that BEFuse outperforms existing methods in both fusion quality and computational speed.

Prior work on few-shot action recognition predominantly adopts two strategies: spatio-temporal separated frame matching and multi-stream multi-modal networks. However, each suffering from either incomplete spatio-temporal modeling or an over-reliance on additional annotation data. To address these limitations, we propose a Contrastive Single-Stream Spatio-Temporal joint modeling Few-Shot Action Recognition (CS3T-FSAR) model. In terms of spatio-temporal modeling, our approach directly constructs high-quality three-dimensional spatio-temporal representations to fully capture the global associations among video frames. Regarding the loss function design, we integrate a triplet loss to achieve precise matching while reducing both inference cost and computational complexity. Ultimately, our method achieves significant performance improvements across four benchmark datasets, demonstrating its competitiveness in few-shot action recognition.

This paper presents a Blur Resist Generative Adversarial Network (GAN) (BrGAN) with multiple joint dilated residual convolutions for chlorophyll image restoration of the Geostationary Ocean Color Imager (GOCI). First, a publicly available dataset was built to support this study. Second, a multiple attention perception mechanism and a multiple joint dilated residual convolution module was proposed to cope with the challenge of large missing areas in GOCI chlorophyll images. Third, a patch GAN based discrimination module was proposed to avoid the restored areas with generating mosaic and shadows. Our experimental results demonstrate that the BrGAN can reach 37.06 in the peak signal-to-noise ratio (PSNR) and 0.0485 in the Learned Perceptual Image Patch Similarity (LPIPS), respectively. The comparative study shows that the BrGAN achieves the highest effectiveness and advancement among other seven state-of-the-art methods.

Occlusion is known as one of the most challenging factors in long-term tracking because of its unpredictable shape. Existing works devoted into the design of loss functions, training strategies or model architectures, which are considered to have not directly touched the key point. Alternatively, we came up with a direct and natural idea that is discarding things that covers the target. We propose a novel occluder-aware representation learning framework to develop this idea. First, we design a local occluders detection module (LODM) to localize the occluders, which works on the principle that discriminates the non-noumenal part from a target based on the general knowledge of this category. An extra dataset and a clustering strategy is proposed to support this general knowledge. Second, we devise a feature reconstruction module to guide the occluder-aware representation learning. With the help of above methods, our localizing occluders tracker, called LOTracker, can learn an occluder-free representation and promote the performance that tracks with occlusion scenarios. Extensive experimental results show that our LOTracker achieves a state-of-the-art performance in multiple benchmarks such as LaSOT, VOTLT2018, VOTLT2019, and OxUvALT.

Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result. Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve state-of-the-art results. The code will be available at https://github.com/hqucv/siamrn

Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the local neighborhood sizes when imposing a connectivity structure on the given set of high-dimensional data points and 2) the adaptive bias reduction in the local low-dimensional embedding by accounting for the variations in the curvature of the manifold as well as its interplay with the sampling density of the data set. We demonstrate the effectiveness of our methods for improving the performance of manifold learning algorithms using both synthetic and real-world data sets.

Recently, there have been several advances in the machine learning and pattern recognition communities for developing manifold learning algo- rithms to construct nonlinear low-dimensional manifolds from sample data points embedded in high-dimensional spaces. In this paper, we de- velop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the neighborhood sizes; and 2) better fitting the local geometric structure to account for the variations in the curvature of the manifold and its interplay with the sampling density of the data set. We also illustrate the effectiveness of our methods on some synthetic data sets.