Search Results for author:
Found 15 papers, 11 papers with code
Weakly Supervised Semantic Segmentation with Boundary Exploration
Weakly supervised semantic segmentation with image-level labels has attracted a lot of attention recently because these labels are already available in most datasets.
SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality
To this end, this work presents a simple and effective framework, SimCMF, to study an important problem: cross-modal fine-tuning from vision foundation models trained on natural RGB images to other imaging modalities of different physical properties (e. g., polarization).
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
With the development of large language models (LLMs), the ability to handle longer contexts has become a key capability for Web applications such as cross-document understanding and LLM-powered search systems.
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs
To address these limitations, we propose a novel self-correcting adaptive planning paradigm for KG-augmented LLM named Plan-on-Graph (PoG), which first decomposes the question into several sub-objectives and then repeats the process of adaptively exploring reasoning paths, updating memory, and reflecting on the need to self-correct erroneous reasoning paths until arriving at the answer.
Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding
Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem.
SimMAT: Exploring Transferability from Vision Foundation Models to Any Image Modality
To this end, this work presents a simple and effective framework SimMAT to study an open problem: the transferability from vision foundation models trained on natural RGB images to other image modalities of different physical properties (e. g., polarization).
General Geometry-aware Weakly Supervised 3D Object Detection
However, the annotation of large-scale 3D datasets requires significant human effort.
SyncNoise: Geometrically Consistent Noise Prediction for Text-based 3D Scene Editing
Meanwhile, the 2D diffusion models also exhibit substantial potentials for 3D editing tasks.
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms.
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite
To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift.
SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention.
FPR: False Positive Rectification for Weakly Supervised Semantic Segmentation
Without introducing any external supervision and human priors, the proposed FPR effectively suppresses wrong activations from the background objects.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
McQueen: a Benchmark for Multimodal Conversational Query Rewrite
In this paper, we propose the task of multimodal conversational query rewrite (McQR), which performs query rewrite under the multimodal visual conversation setting.
Multi-modal Siamese Network for Entity Alignment
To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect.
Ranked #7 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view.
