Search Results for author:
Found 22 papers, 11 papers with code
Points as Queries: Weakly Semi-supervised Object Detection by Points
We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points.
HINet: Half Instance Normalization Network for Image Restoration
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
Ranked #3 on
Single Image Deraining
on Test2800
Multi-task Graph Convolutional Neural Network for Calcification Morphology and Distribution Analysis in Mammograms
In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms.
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
A detailed description of all models developed in the challenge is provided in this paper.
Improving Image Restoration by Revisiting Global Information Aggregation
Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
Ranked #1 on
Color Image Denoising
on Urban100 sigma30
Simple Baselines for Image Restoration
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.
Ranked #1 on
Deblurring
on MSU BASED
NAFSSR: Stereo Image Super-Resolution Using NAFNet
This paper inherits a strong and simple image restoration model, NAFNet, for single-view feature extraction and extends it by adding cross attention modules to fuse features between views to adapt to binocular scenarios.
BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection
PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media. Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed.
Ranked #1 on
Multi-label Condescension Detection
on DPM
Binary Condescension Detection
Multi-Label Classification
+1
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning
Machine Reading Comprehension with Unanswerable Questions is a difficult NLP task, challenged by the questions which can not be answered from passages.
Automatic tagging of knowledge points for K12 math problems
In this paper, K12 math problems taken as the research object, the LABS model based on label-semantic attention and multi-label smoothing combining textual features is proposed to improve the automatic tagging of knowledge points for math problems.
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices.
Otter: A Multi-Modal Model with In-Context Instruction Tuning
Large language models (LLMs) have demonstrated significant universal capabilities as few/zero-shot learners in various tasks due to their pre-training on vast amounts of text data, as exemplified by GPT-3, which boosted to InstrctGPT and ChatGPT, effectively following natural language instructions to accomplish real-world tasks.
Ranked #8 on
Visual Question Answering
on BenchLMM
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
We release the MIMIC-IT dataset, instruction-response collection pipeline, benchmarks, and the Otter model.
Ranked #83 on
Visual Question Answering
on MM-Vet
Benchmarking and Analyzing Generative Data for Visual Recognition
Advancements in large pre-trained generative models have expanded their potential as effective data generators in visual recognition.
ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation
This paper presents the development and evaluation of ChatHome, a domain-specific language model (DSLM) designed for the intricate field of home renovation.
LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
Specifically, we design a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning.
DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking
The fast thinking model serves as the primary interface for external interactions and initial response generation, evaluating the necessity for engaging the slow thinking model based on the complexity of the complete response.
Panoptic Video Scene Graph Generation
PVSG relates to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects grounded with bounding boxes in videos.
FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability
Our method's efficacy has been validated on multiple representative DreamBooth and LoRA models, delivering substantial improvements over the original outcomes in terms of facial fidelity, text-to-image editability, and video motion.
From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture enhancing the integration of Large Language Models (LLMs) like GPT-4 into conversational agents.
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
MMInA: Benchmarking Multihop Multimodal Internet Agents
To answer this question, we present MMInA, a multihop and multimodal benchmark to evaluate the embodied agents for compositional Internet tasks, with several appealing properties: 1) Evolving real-world multimodal websites.
