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Found 19 papers, 13 papers with code
IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models.
ConSept: Continual Semantic Segmentation via Adapter-based Vision Transformer
Empirical investigations on the adaptation of existing frameworks to vanilla ViT reveal that incorporating visual adapters into ViTs or fine-tuning ViTs with distillation terms is advantageous for enhancing the segmentation capability of novel classes.
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
In the rapidly evolving landscape of Large Language Models (LLMs), ensuring robust safety measures is paramount.
TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields
To this end, an NTFGen module is proposed to model general text latent code in noisy fields.
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users.
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
In this paper, we explore the potential of LLMs such as GPT-4 to outperform traditional AI tools in dementia diagnosis.
Towards Universal Vision-language Omni-supervised Segmentation
To improve the open-world segmentation ability, we leverage omni-supervised data (i. e., panoptic segmentation data, object detection data, and image-text pairs data) into training, thus enriching the open-world segmentation ability and achieving better segmentation accuracy.
Toward a Unified Framework for Unsupervised Complex Tabular Reasoning
To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs.
Texts as Images in Prompt Tuning for Multi-Label Image Recognition
Nonetheless, visual data (e. g., images) is by default prerequisite for learning prompts in existing methods.
Contrastive Learning
Multi-label Image Recognition with Partial Labels
LPT: Long-tailed Prompt Tuning for Image Classification
For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into target domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with discrimination ability.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
(using extra training data)
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification.
Ranked #3 on
Training-free 3D Point Cloud Classification
on ScanObjectNN
(using extra training data)
W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
Generally, with given pseudo ground-truths generated from the well-trained WSOD network, we propose a two-module iterative training algorithm to refine pseudo labels and supervise better object detector progressively.
Symmetry-Aware Transformer-based Mirror Detection
Mirror detection aims to identify the mirror regions in the given input image.
Prompt Tuning for Discriminative Pre-trained Language Models
Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks.
Self-Promoted Supervision for Few-Shot Transformer
The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired.
Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
In this paper, we defend the problem setting for improving localization performance by leveraging the bounding box regression knowledge from a well-annotated auxiliary dataset.
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification
Few-shot classification requires classifiers to adapt to new classes with only a few training instances.
Missing Movie Synergistic Completion across Multiple Isomeric Online Movie Knowledge Libraries
Online knowledge libraries refer to the online data warehouses that systematically organize and categorize the knowledge-based information about different kinds of concepts and entities.
FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network
This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance.
