19 accepted papers
As an important and challenging problem in computer vision, Panoramic Semantic Segmentation (PASS) aims to provide complete scene perception based on an ultra-wide angle of view. Most PASS methods often focus on spherical geometry with RGB input or use the depth information in original or HHA format
Accurate wind speed forecasting is essential for mitigating the challenges of wind power grid integration. However, existing wind speed prediction models overlook the distributional shift problem within wind speed series, and this time-varying distribution can significantly impact wind prediction ac…
Self-training is a strong baseline for semi-supervised domain adaptive semantic segmentation. However, it inevitably introduces biased links between features and concepts in the prediction of certain "hard pixels", which may mislead the generalization of models. We consider these hard pixels to come…
Large language models (LLMs) excel in natural language tasks, with Chain-of-Thought (CoT) prompting enhancing reasoning through step-by-step decomposition. However, CoT struggles in knowledge-intensive tasks with multiple entities and implicit multi-hop relations, failing to connect entities systema
Deep learning-based semantic segmentation technology has become a critical tool in assisting doctors with automatic lesion segmentation in medical images. However, the high cost of acquiring large-scale, pixel-level annotations poses a significant challenge, limiting the scalability and application…
Sequential recommender systems aim to learn user preferences through historical interaction sequences. User interactions are driven both by popular trends and personal interests, introducing two types of noise: popular choices triggered by conformist behavior and irrelevant terms that do not reflect…
The class imbalance problem, which is prevalent in medical image datasets, seriously affects the diagnostic effectiveness of deep learning-based network models. Recently, the method based on two-stage learning has produced promising results in solving class imbalance. In two-stage learning, the lear…
Recommender systems based on Matrix Factorization are widely used. However, they can easily suffer from the problem of overrecommendation of popular items, i.e., popularity bias. To mitigate popularity bias, current methods often uniformly model interactions' popularity bias degree considering user…
The resolution of a whole slide image (WSI) is too large to process directly, but WSI can be segmented into patches and be classified through multiple instance learning (MIL). Some patches have either close distances or similar pathological morphology, indicating that there are at least two types of…
Semi-supervised domain generalization (SSDG) aims to build a domain-generalized model using partially labeled data from source domains. Mainstream SSDG methods follow the augmentation consistency in FixMatch. However, the extraction of domain-invariant features may be challenging due to the absence…
Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation. In this paper, we introduc…
Knowledge distillation (KD) has shown potential for learning compact models in dense object detection. However, the commonly used softmax-based distillation ignores the absolute classification scores for individual categories. Thus, the optimum of the distillation loss does not necessarily lead to t…
Recently, diffusion models have achieved great success in image synthesis. However, when it comes to the layout-to-image generation where an image often has a complex scene of multiple objects, how to make strong control over both the global layout map and each detailed object remains a challenging…
As an important and challenging problem in computer vision, PAnoramic Semantic Segmentation (PASS) gives complete scene perception based on an ultra-wide angle of view. Usually, prevalent PASS methods with 2D panoramic image input focus on solving image distortions but lack consideration of the 3D p…
Acoustic-to-articulatory inversion (AAI) aims to estimate the parameters of articulators from speech audio. There are two common challenges in AAI, which are the limited data and the unsatisfactory performance in speaker independent scenario. Most current works focus on extracting features directly…
Previous works derive personalized speech features by training the model on a large dataset composed of his/her audio sounds. It was reported that face information has a strong link with the speech sound. Thus in this work, we innovatively extract personalized speech features from human faces to syn…
Multi-label video classification (MLVC) is a long-standing and challenging research problem in video signal analysis. Generally, there exist many complex action labels in real-world videos and these actions are with inherent dependencies at both spatial and temporal domains. Motivated by this observ…
Weakly-supervised temporal action localization (WTAL) is a long-standing and challenging research problem in video signal analysis. It is to localize the action segments in the video given only video-level labels. The key to this task is understanding how the diverse actions interact. In this paper,…
In self-supervised monocular depth estimation, the depth discontinuity and motion objects' artifacts are still challenging problems. Existing self-supervised methods usually utilize two views to train the depth estimation network and use one single view to make predictions. Compared with static view…