Search Results for author:
Found 18 papers, 12 papers with code
The Meta-Representation Hypothesis
In this paper, we build a bridge between meta-representation and generalization, showing that generalization performance benefits from meta-representation learning.
OpenViewer: Openness-Aware Multi-View Learning
However, deploying these models in real-world scenarios presents two primary openness challenges.
NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction
We contend that the key to addressing these challenges lies in accurately decoding both high-level semantics and low-level perception flows, as perceived by the brain in response to video stimuli.
PSTNet: Enhanced Polyp Segmentation with Multi-scale Alignment and Frequency Domain Integration
Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC).
SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation
In this paper, we propose a trans\textbf{Former} network (\textbf{SkinFormer}) that efficiently extracts and fuses statistical texture representation for \textbf{Skin} lesion segmentation.
Generalization Boosted Adapter for Open-Vocabulary Segmentation
To address these limitations, we propose Generalization Boosted Adapter (GBA), a novel adapter strategy that enhances the generalization and robustness of VLMs for open-vocabulary segmentation.
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning
In partially observable multi-agent systems, agents typically only have access to local observations.
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
Additionally, we introduce a token merging method guided by comprehensive semantics from the frequency and spatial domains.
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.
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.
Advances in Embodied Navigation Using Large Language Models: A Survey
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 Attention-weighted Semantic Relation Distillation (ASRD), 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.
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.
