Search Results for author:
Found 12 papers, 9 papers with code
HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection
Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels.
NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) stands as a key research problem of Embodied AI, aiming at enabling agents to navigate in unseen environments following linguistic instructions.
Local Feature Matching Using Deep Learning: A Survey
The objective of this endeavor is to furnish a comprehensive overview of local feature matching methods.
Spectral Prompt Tuning:Unveiling Unseen Classes for Zero-Shot Semantic Segmentation
Recently, CLIP has found practical utility in the domain of pixel-level zero-shot segmentation tasks.
The Development of LLMs for Embodied Navigation
This article offers an exhaustive summary of the symbiosis between LLMs and embodied intelligence with a focus on navigation.
Segment Anything Model is a Good Teacher for Local Feature Learning
To do so, first, we construct an auxiliary task of Pixel Semantic Relational Distillation (PSRD), which distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination.
Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition
However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i. e., old and future entity types are labeled as the non-entity type in the current task).
Attention Weighted Local Descriptors
Local features detection and description are widely used in many vision applications with high industrial and commercial demands.
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation
In addition, to further improve the segmentation accuracy, we design a Variation-aware Refine Module to enhance the local consistency of pseudo-labels by computing pixel-level variation.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Treating Pseudo-labels Generation as Image Matting for Weakly Supervised Semantic Segmentation
To solve this problem, we develop a Double Decoupled Class Activation Map (D2CAM) for Mat-Label to generate a high-quality trimap.
MTLDesc: Looking Wider to Describe Better
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context.
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results.
