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Found 400 papers, 175 papers with code
FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image Restoration
Despite the significant progress made by all-in-one models in universal image restoration, existing methods suffer from a generalization bottleneck in real-world scenarios, as they are mostly trained on small-scale synthetic datasets with limited degradations.
VLSBench: Unveiling Visual Leakage in Multimodal Safety
To explain such a counter-intuitive phenomenon, we discover a visual safety information leakage (VSIL) problem in existing multimodal safety benchmarks, i. e., the potentially risky and sensitive content in the image has been revealed in the textual query.
Visual SLAMMOT Considering Multiple Motion Models
Specifically, we propose a solution of visual SLAMMOT considering multiple motion models and validate the inherent advantages of IMM-SLAMMOT in the visual domain.
DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes
This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes.
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Large language models have demonstrated substantial advancements in reasoning capabilities, particularly through inference-time scaling, as illustrated by models such as OpenAI's o1.
Motion Control for Enhanced Complex Action Video Generation
Furthermore, MVideo supports motion condition editing and composition, facilitating the generation of videos with more complex actions.
Exploring Hierarchical Molecular Graph Representation in Multimodal LLMs
We then explore the effect of various feature levels on performance, finding that both the quality of LLM-generated molecules and performance on different tasks benefit from different feature levels.
InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Models
NotInject contains 339 benign samples enriched with trigger words common in prompt injection attacks, enabling fine-grained evaluation.
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues.
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification
This black-box, self-supervised approach ensures the generalizability of our attack against various VI-ReID models.
STTATTS: Unified Speech-To-Text And Text-To-Speech Model
Speech recognition and speech synthesis models are typically trained separately, each with its own set of learning objectives, training data, and model parameters, resulting in two distinct large networks.
Deep Learning-based Detection of Bacterial Swarm Motion Using a Single Image
It blindly achieved a sensitivity of 97. 92% and a specificity of 96. 77% for DB10, and a sensitivity of 100% and a specificity of 97. 22% for H6.
PUMA: Empowering Unified MLLM with Multi-granular Visual Generation
This work represents a significant step towards a truly unified MLLM capable of adapting to the granularity demands of various visual tasks.
Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking
Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing contacts, surfaces, and water flows.
Derail Yourself: Multi-turn LLM Jailbreak Attack through Self-discovered Clues
This study exposes the safety vulnerabilities of Large Language Models (LLMs) in multi-turn interactions, where malicious users can obscure harmful intents across several queries.
Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models
Our study not only enriches the body of knowledge on practical applications of FM4SE and SE4FM but also demonstrates the utility of FMs as a powerful and efficient approach in conducting literature surveys within technical and grey literature domains.
CAS-GAN for Contrast-free Angiography Synthesis
Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients.
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
The rapid advancement of artificial intelligence (AI) in weather research has been driven by the ability to learn from large, high-dimensional datasets.
Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework
Specifically, we utilize a gate model to identify videos that may have playback issues in real-time, and then we employ a ranking model to select the optimal result from a locally-cached pool to replace the stuttering videos.
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law
To address this issue, we propose an efficient law article recommendation approach utilizing a Knowledge Graph (KG) and a Large Language Model (LLM).
AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation
Subsequently, we sample on the target domain with binary vessel masks from the source domain to get paired data, i. e., target domain synthetic images conditioned on the binary vessel map.
One-step Noisy Label Mitigation
Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale pre-training tasks is often impractical.
Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval
Then, we take these semantic slots as internal features and leverage them to interact with the visual features.
HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations
In digital healthcare, large language models (LLMs) have primarily been utilized to enhance question-answering capabilities and improve patient interactions.
Triple Point Masking
Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation.
FaceVid-1K: A Large-Scale High-Quality Multiracial Human Face Video Dataset
We obtain the corresponding performance benchmarks and compared them with those trained on public datasets to demonstrate the superiority of our dataset.
Extract-and-Abstract: Unifying Extractive and Abstractive Summarization within Single Encoder-Decoder Framework
In this paper, we first introduce a parameter-free highlight method into the encoder-decoder framework: replacing the encoder attention mask with a saliency mask in the cross-attention module to force the decoder to focus only on salient parts of the input.
FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
The results demonstrate that FuXi-2. 0 consistently outperforms ECMWF HRES in forecasting key meteorological variables relevant to these sectors.
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis
Meta-learning is characterized by its ability to learn how to learn, enabling the adaptation of learning strategies across different tasks.
NYK-MS: A Well-annotated Multi-modal Metaphor and Sarcasm Understanding Benchmark on Cartoon-Caption Dataset
Metaphor and sarcasm are common figurative expressions in people's communication, especially on the Internet or the memes popular among teenagers.
GRPose: Learning Graph Relations for Human Image Generation with Pose Priors
Recent methods using diffusion models have made significant progress in human image generation with various additional controls such as pose priors.
Cascaded Temporal Updating Network for Efficient Video Super-Resolution
However, the key components in recurrent-based VSR networks significantly impact model efficiency, e. g., the alignment module occupies a substantial portion of model parameters, while the bidirectional propagation mechanism significantly amplifies the inference time.
LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document summarisation.
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs.
Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN
The second information is geolocation awareness, which means how people whereabouts are made available.
ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack
However, existing works on diffusion-based unrestricted attacks are mostly focused on images yet are seldom explored in videos.
FuXi Weather: A data-to-forecast machine learning system for global weather
Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrates global observational systems, data assimilation (DA), and forecasting models.
PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound
The proposed model adopts the segmentation from our fully automated model for initialization and is designed in a human-in-the-loop manner to achieve iterative improvements.
Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets
This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual DW images acquired using various b-values, to align with the style of images acquired using b-values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines.
VidGen-1M: A Large-Scale Dataset for Text-to-video Generation
Produced through a coarse-to-fine curation strategy, this dataset guarantees high-quality videos and detailed captions with excellent temporal consistency.
Model Hijacking Attack in Federated Learning
Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications ranging from autonomous driving to face recognition.
An Error Discovery and Correction for the Family of V-Shaped BPSO Algorithms
So, traditionally, it has to rely on a low w value in the later stage to force these algorithms to converge, but also makes them quickly lose their search ability and prone to getting trapped in local optima.
Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging
We propose a novel framework for retinal feature point alignment, designed for learning cross-modality features to enhance matching and registration across multi-modality retinal images.
SeqMIA: Sequential-Metric Based Membership Inference Attack
Building upon this signal, we introduce a novel attack method called Sequential-metric based Membership Inference Attack (SeqMIA).
Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation
Specifically, we provide an automated method for reference local action sampling and leverage graph attention networks to assess the guiding weight of each local action in the overall motion synthesis.
Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models
We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner.
Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images
These models produce a segmentation from visual prompts provided to indicate the target region, which may offer a feasible solution for practical use.
Adaptively Robust and Sparse K-means Clustering
While K-means is known to be a standard clustering algorithm, its performance may be compromised due to the presence of outliers and high-dimensional noisy variables.
Exploring the Causality of End-to-End Autonomous Driving
Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions.
Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality
Our study is the first to show that using a non-default binding can help improve machine learning software quality from the time cost perspective compared to the default Python binding while still achieving the same level of correctness.
HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis
Panoramic image segmentation in computational pathology presents a remarkable challenge due to the morphologically complex and variably scaled anatomy.
SPIRONet: Spatial-Frequency Learning and Topological Channel Interaction Network for Vessel Segmentation
Automatic vessel segmentation is paramount for developing next-generation interventional navigation systems.
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most diverse publicly available cardiac k-space dataset.
XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis
To address this, this paper presents a novel driving view synthesis dataset and benchmark specifically designed for autonomous driving simulations.
VDG: Vision-Only Dynamic Gaussian for Driving Simulation
Dynamic Gaussian splatting has led to impressive scene reconstruction and image synthesis advances in novel views.
EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models
Our evaluation across 24 text-to-image generation models demonstrate that EvalAlign not only provides superior metric stability but also aligns more closely with human preferences than existing metrics, confirming its effectiveness and utility in model assessment.
Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge
Recently, in silico labeling has emerged as a promising alternative, aiming to use machine learning models to directly predict the fluorescently labeled images from label-free microscopy.
Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments
Experiments conducted on the dataset collected from the benchmark show that MicroCERCL can accurately localize the root cause of microservice systems in such environments, significantly outperforming state-of-the-art approaches with an increase of at least 24. 1% in top-1 accuracy.
Text-aware Speech Separation for Multi-talker Keyword Spotting
TPDT-SS shows remarkable success in addressing permutation problems in mixed keyword speech, thereby greatly boosting the performance of the backend.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
Crucial to addressing this real-world need are event summary and persona management, which enable reasoning for appropriate long-term dialogue responses.
Parameter-Inverted Image Pyramid Networks
Our core idea is to use models with different parameter sizes to process different resolution levels of the image pyramid, thereby balancing computational efficiency and performance.
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation
In our work, we introduce an argument mining dataset that captures the end-to-end process of preparing an argumentative essay for a debate, which covers the tasks of claim and evidence identification (Task 1 ED), evidence convincingness ranking (Task 2 ECR), argumentative essay summarisation and human preference ranking (Task 3 ASR) and metric learning for automated evaluation of resulting essays, based on human feedback along argument quality dimensions (Task 4 SQE).
Deep Learning based Performance Testing for Analog Integrated Circuits
In this paper, we propose a deep learning based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus.
VOODOO XP: Expressive One-Shot Head Reenactment for VR Telepresence
We further integrate our novel head reenactment solution into an accessible high-fidelity VR telepresence system, where any person can instantly build a personalized neural head avatar from any photo and bring it to life using the headset.
Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation
Experimental evaluations conducted on MS COCO, Cityscapes, and CTW1500 datasets indicate that the QEIS models' performance can be significantly improved when pre-trained with our method.
An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual Representation
Moreover, the model capacity of the text encoder from CLIP is relatively limited compared to Large Language Models (LLMs), which offer multilingual input, accommodate longer context, and achieve superior text representation.
Local-peak scale-invariant feature transform for fast and random image stitching
Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure.
Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval
Large language models (LLMs) have demonstrated remarkable capabilities across various domains, although their susceptibility to hallucination poses significant challenges for their deployment in critical areas such as healthcare.
FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting
This innovative approach makes FuXi-ENS an advancement over the traditional ones that use L1 loss combined with the KL loss in standard VAE models for ensemble weather forecasting.
A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models
Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data.
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories.
Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics
Through extensive experiments, our findings reveal that Arena can boost inference speeds by up to 1. 58\(\times\) and 1. 82\(\times\) on average while consuming only 47\% and 31\% of the bandwidth, respectively, all with high inference accuracy.
Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations
Data assimilation (DA), as an indispensable component within contemporary Numerical Weather Prediction (NWP) systems, plays a crucial role in generating the analysis that significantly impacts forecast performance.
Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
Multi-level graph subspace contrastive learning: multi-level contrastive learning was conducted to obtain local-global joint graph representations, to improve the consistency of the positive samples between views, and to obtain more robust graph embeddings.
360$^\circ$REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
The framework employs a novel 360$^\circ$ performance assessment method for multi-perspective performance evaluation with fine-grained assessment.
Collaborative Feedback Discriminative Propagation for Video Super-Resolution
However, inaccurate alignment usually leads to aligned features with significant artifacts, which will be accumulated during propagation and thus affect video restoration.
Ranked #5 on
Video Super-Resolution
on Vid4 - 4x upscaling
NeRFCodec: Neural Feature Compression Meets Neural Radiance Fields for Memory-Efficient Scene Representation
The emergence of Neural Radiance Fields (NeRF) has greatly impacted 3D scene modeling and novel-view synthesis.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation
Due to the complementary nature of visible light and thermal infrared modalities, object tracking based on the fusion of visible light images and thermal images (referred to as RGB-T tracking) has received increasing attention from researchers in recent years.
Ranked #5 on
Rgb-T Tracking
on GTOT
TDT-KWS: Fast And Accurate Keyword Spotting Using Token-and-duration Transducer
Designing an efficient keyword spotting (KWS) system that delivers exceptional performance on resource-constrained edge devices has long been a subject of significant attention.
GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time
Specifically, we design a novel joint learning framework that consists of an Iterative Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model.
LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection
Moreover, LAN can be also applied to post-hoc ITD, surpassing 8 competitive baselines by at least 7. 70% and 4. 03% in AUC on two datasets.
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content
Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories.
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation
Vanilla text-to-image diffusion models struggle with generating accurate human images, commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs. Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls -- human-centric priors such as pose or depth maps -- during the image generation phase.
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
It is designed to enhance the model's generalizability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics.
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT).
ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings
In addition, our approach can be generalized into a broader method for generating transferable adversarial suffixes that can successfully attack multiple LLMs, even black-box LLMs, such as ChatGPT and Gemini.
Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing
GAN-based image attribute editing firstly leverages GAN Inversion to project real images into the latent space of GAN and then manipulates corresponding latent codes.
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.
Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training.
MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation
2. 5D-based segmentation models bridge computational efficiency of 2D-based models and spatial perception capabilities of 3D-based models.
Instance Brownian Bridge as Texts for Open-vocabulary Video Instance Segmentation
To mold instance queries to follow Brownian bridge and accomplish alignment with class texts, we design Bridge-Text Alignment (BTA) to learn discriminative bridge-level representations of instances via contrastive objectives.
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
This classifier is then used to detect if a trained model is overfit.
Hierarchical Fashion Design with Multi-stage Diffusion Models
Cross-modal fashion synthesis and editing offer intelligent support to fashion designers by enabling the automatic generation and local modification of design drafts. While current diffusion models demonstrate commendable stability and controllability in image synthesis, they still face significant challenges in generating fashion design from abstract design elements and fine-grained editing. Abstract sensory expressions, \eg office, business, and party, form the high-level design concepts, while measurable aspects like sleeve length, collar type, and pant length are considered the low-level attributes of clothing. Controlling and editing fashion images using lengthy text descriptions poses a difficulty. In this paper, we propose HieraFashDiff, a novel fashion design method using the shared multi-stage diffusion model encompassing high-level design concepts and low-level clothing attributes in a hierarchical structure. Specifically, we categorized the input text into different levels and fed them in different time step to the diffusion model according to the criteria of professional clothing designers. HieraFashDiff allows designers to add low-level attributes after high-level prompts for interactive editing incrementally. In addition, we design a differentiable loss function in the sampling process with a mask to keep non-edit areas. Comprehensive experiments performed on our newly conducted Hierarchical fashion dataset, demonstrate that our proposed method outperforms other state-of-the-art competitors.
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech
Furthermore, experiments on the continuous speech dataset LibriSpeech demonstrate that, by incorporating audio discrimination, CLAD achieves significant performance gain over CL without audio discrimination.
Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning
Autonomous vehicles (AVs) have the potential to prevent accidents caused by drivers errors and reduce road traffic risks.
Coordinated Planning of Offshore Charging Stations and Electrified Ships: A Case Study on Shanghai-Busan Maritime Route
Despite the success of electric vehicles on land, electrification of maritime ships is challenged by the dilemma of range anxiety and cargo-carrying capacity.
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
To establish an optimal multimodal semantic environment for text modality, we develop a modality-aware prompting module (MAP), which effectively aligns and fuses features from text, video and audio modalities with similarity-based modality alignment and cross-modality attention mechanism.
Ranked #2 on
Multimodal Intent Recognition
on MIntRec
FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge.
Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
Many reinforcement learning environments (e. g., Minecraft) provide only sparse rewards that indicate task completion or failure with binary values.
Diffusion-based Blind Text Image Super-Resolution
Since text prior is important to guarantee the correctness of the restored text structure according to existing arts, we also propose a Text Diffusion Model (TDM) for text recognition which can guide IDM to generate text images with correct structures.
Negative Pre-aware for Noisy Cross-modal Matching
Since clean samples are easier distinguished by GMM with increasing noise, the memory bank can still maintain high quality at a high noise ratio.
Cross-modal retrieval with noisy correspondence
Image-text matching
+3
The performance of multiple language models in identifying offensive language on social media
Text classification is an important topic in the field of natural language processing.
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
This method trades off performance and robustness via introducing the robust Bellman operator into the algorithm.
VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment
We present a 3D-aware one-shot head reenactment method based on a fully volumetric neural disentanglement framework for source appearance and driver expressions.
FreestyleRet: Retrieving Images from Style-Diversified Queries
In this paper, we propose the Style-Diversified Query-Based Image Retrieval task, which enables retrieval based on various query styles.
Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition
With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction.
Novel OCT mosaicking pipeline with Feature- and Pixel-based registration
High-resolution Optical Coherence Tomography (OCT) images are crucial for ophthalmology studies but are limited by their relatively narrow field of view (FoV).
Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks
The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multi-tasking.
GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding
Applying NeRF to downstream perception tasks for scene understanding and representation is becoming increasingly popular.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
In this paper, we assess the test-time variability for interactive medical image segmentation with diverse point prompts.
InfMLLM: A Unified Framework for Visual-Language Tasks
Our experiments demonstrate that preserving the positional information of visual embeddings through the pool-adapter is particularly beneficial for tasks like visual grounding.
Ranked #140 on
Visual Question Answering
on MM-Vet
