Search Results for author:
Found 17 papers, 13 papers with code
Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment
With the increasing abundance of meeting transcripts, meeting summary has attracted more and more attention from researchers.
iDAT: inverse Distillation Adapter-Tuning
Adapter-Tuning (AT) method involves freezing a pre-trained model and introducing trainable adapter modules to acquire downstream knowledge, thereby calibrating the model for better adaptation to downstream tasks.
CLAPP: Contrastive Language-Audio Pre-training in Passive Underwater Vessel Classification
Existing research on audio classification faces challenges in recognizing attributes of passive underwater vessel scenarios and lacks well-annotated datasets due to data privacy concerns.
LAMM: Label Alignment for Multi-Modal Prompt Learning
We conduct experiments on 11 downstream vision datasets and demonstrate that our method significantly improves the performance of existing multi-modal prompt learning models in few-shot scenarios, exhibiting an average accuracy improvement of 2. 31(\%) compared to the state-of-the-art methods on 16 shots.
GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction
2) They neglect the interaction between the intrinsic task-agnostic knowledge of pre-trained models and the task-specific knowledge in downstream tasks.
EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation
Moreover, to our best knowledge, this is the first model with a parameter count limited to just 50KB.
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming
Open-domain dialogue systems have made promising progress in recent years.
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
In this paper, we propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning on visual, textual and visual-textual tasks respectively.
Generalizable Person Re-identification
Representation Learning
CluCDD:Contrastive Dialogue Disentanglement via Clustering
A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines.
MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation
To address this challenge, we propose a light-weight model to achieve competitive performances for skin lesion segmentation at the lowest cost of parameters and computational complexity so far.
Deep Multimodal Fusion for Generalizable Person Re-identification
Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance.
Domain Generalization
Generalizable Person Re-identification
+2
MEW-UNet: Multi-axis representation learning in frequency domain for medical image segmentation
Specifically, our block performs a Fourier transform on the three axes of the input feature and assigns the external weight in the frequency domain, which is generated by our Weights Generator.
Rethinking Person Re-Identification via Semantic-Based Pretraining
Pretraining is a dominant paradigm in computer vision.
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification
Moreover, aiming to fully exploit the potential of FineGPR and promote the efficient training from millions of synthetic data, we propose an attribute analysis pipeline called AOST, which dynamically learns attribute distribution in real domain, then eliminates the gap between synthetic and real-world data and thus is freely deployed to new scenarios.
Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification
Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation.
Attribute analysis with synthetic dataset for person re-identification
To address this problem, firstly, we develop a large-scale synthetic data engine, the salient characteristic of this engine is controllable.
