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Found 430 papers, 161 papers with code
zydhjh4593@SMM4H’22: A Generic Pre-trained BERT-based Framework for Social Media Health Text Classification
This paper describes our proposed framework for the 10 text classification tasks of Task 1a, 2a, 2b, 3a, 4, 5, 6, 7, 8, and 9, in the Social Media Mining for Health (SMM4H) 2022.
Enabling Deep Residual Networks for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale image-level annotation for detector training.
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement.
Safe Data-Driven Predictive Control
In the realm of control systems, model predictive control (MPC) has exhibited remarkable potential; however, its reliance on accurate models and substantial computational resources has hindered its broader application, especially within real-time nonlinear systems.
AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting
We thus introduce a novel framework, AVP-AP, the first to use Atlas Prompting for self-supervised Automatic View Positioning in the 3D CT volume.
Can Test-Time Scaling Improve World Foundation Model?
Our findings reveal that test-time scaling laws hold for WFMs and that SWIFT provides a scalable and effective pathway for improving WFM inference without retraining or increasing model size.
Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis
Our evaluation of state-of-the-art 3D-VL models on Beacon3D reveals that (i) object-centric evaluation elicits true model performance and particularly weak generalization in QA; (ii) grounding-QA coherence remains fragile in current 3D-VL models, and (iii) incorporating large language models (LLMs) to 3D-VL models, though as a prevalent practice, hinders grounding capabilities and has yet to elevate QA capabilities.
Fundamental Limit of Angular Resolution in Partly Calibrated Arrays with Position Errors
We then theoretically analyze the declining and plateau phases of CRB, and explain that the turning point of CRB in partly calibrated arrays is close to the angular resolution limit of distributed arrays without errors, demonstrating high resolution ability.
SIT-FER: Integration of Semantic-, Instance-, Text-level Information for Semi-supervised Facial Expression Recognition
Specifically, for the unlabeled data, considering the comprehensive knowledge within the textual descriptions and instance representations, we respectively calculate the similarities between the facial vision features and the corresponding textual and instance features to obtain the probabilities at the text- and instance-level.
MambaIC: State Space Models for High-Performance Learned Image Compression
Inspired by the effectiveness of state space models (SSMs) in capturing long-range dependencies, we leverage SSMs to address computational inefficiency in existing methods and improve image compression from multiple perspectives.
GenDR: Lightning Generative Detail Restorator
In detail, we train a new SD2. 1-VAE16 (0. 9B) via representation alignment to expand latent space without enlarging the model size.
TR-DQ: Time-Rotation Diffusion Quantization
TR-DQ achieves state-of-the-art (SOTA) performance on image generation and video generation tasks and a 1. 38-1. 89x speedup and 1. 97-2. 58x memory reduction in inference compared to existing quantization methods.
OrdRankBen: A Novel Ranking Benchmark for Ordinal Relevance in NLP
The evaluation of ranking tasks remains a significant challenge in natural language processing (NLP), particularly due to the lack of direct labels for results in real-world scenarios.
MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation
Medical Hyperspectral Imaging (MHSI) offers potential for computational pathology and precision medicine.
VidLBEval: Benchmarking and Mitigating Language Bias in Video-Involved LVLMs
Recently, Large Vision-Language Models (LVLMs) have made significant strides across diverse multimodal tasks and benchmarks.
Constructing a Norm for Children's Scientific Drawing: Distribution Features Based on Semantic Similarity of Large Language Models
The use of children's drawings to examining their conceptual understanding has been proven to be an effective method, but there are two major problems with previous research: 1.
A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection
Secondly, we propose a macro-hierarchical transfer learning module to generate engagement features based on common users' shared behaviors in different domains for improving effectiveness of knowledge transfer.
MSVCOD:A Large-Scale Multi-Scene Dataset for Video Camouflage Object Detection
This expanded scope increases the practical applicability of the VCOD task in camouflaged object detection.
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
Experiments on the MVTecLOCO dataset confirm the efficacy of ComGEN, achieving the best AUROC score of 91. 2%.
Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance
While large language models (LLMs) have shown strong general reasoning capabilities, their effectiveness in financial reasoning, which is crucial for real-world financial applications remains underexplored.
Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving
Considering the high annotation cost for 3D outdoor scenes, we propose a semi-supervised vision-centric 3D occupancy world model, PreWorld, to leverage the potential of 2D labels through a novel two-stage training paradigm: the self-supervised pre-training stage and the fully-supervised fine-tuning stage.
A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems
The explosive growth of video data has driven the development of distributed video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling efficient video processing, real-time inference, and privacy-preserving analysis.
A Comprehensive Review of Protein Language Models
At the intersection of the rapidly growing biological data landscape and advancements in Natural Language Processing (NLP), protein language models (PLMs) have emerged as a transformative force in modern research.
Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior
Recent advancements in diffusion models have been leveraged to address inverse problems without additional training, and Diffusion Posterior Sampling (DPS) (Chung et al., 2022a) is among the most popular approaches.
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain
In-Domain Query Interaction module utilizes a transformer to learn content and depth information for each domain and outputs object queries.
In-Context Meta LoRA Generation
As a result, our method achieves more accurate LoRA parameter generation for diverse tasks using CVAE.
A Holistically Point-guided Text Framework for Weakly-Supervised Camouflaged Object Detection
Weakly-Supervised Camouflaged Object Detection (WSCOD) has gained popularity for its promise to train models with weak labels to segment objects that visually blend into their surroundings.
BASIC: Semi-supervised Multi-organ Segmentation with Balanced Subclass Regularization and Semantic-conflict Penalty
Additionally, based on a mean teacher framework, we elaborately design a balanced subclass regularization to utilize the teacher predictions of SCS task to supervise the student predictions of MoS task, thus effectively transferring unbiased knowledge to the MoS subnetwork and alleviating the influence of the class-imbalance problem.
Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection
Through extensive experiments on state-of-the-art LLMs across multiple social dimensions, we demonstrate that LLMs exhibit a substantial inconsistency between explicit and implicit biases, where explicit biases manifest as mild stereotypes while implicit biases show strong stereotypes.
DreamDrive: Generative 4D Scene Modeling from Street View Images
In this paper, we present DreamDrive, a 4D spatial-temporal scene generation approach that combines the merits of generation and reconstruction, to synthesize generalizable 4D driving scenes and dynamic driving videos with 3D consistency.
STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes
We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations.
P3S-Diffusion:A Selective Subject-driven Generation Framework via Point Supervision
Recent research in subject-driven generation increasingly emphasizes the importance of selective subject features.
Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models
In this work, we provide a detailed analysis of this issue and identify that unusually high attention entropy can be a key factor.
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering
Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer.
Extrapolated Urban View Synthesis Benchmark
Photorealistic simulators are essential for the training and evaluation of vision-centric autonomous vehicles (AVs).
Progressive Vision-Language Prompt for Multi-Organ Multi-Class Cell Semantic Segmentation with Single Branch
Given that multiple cell types exist across various organs, with subtle differences in cell size and shape, multi-organ, multi-class cell segmentation is particularly challenging.
DIVD: Deblurring with Improved Video Diffusion Model
However, due to the computational complexity and challenges inherent in adapting diffusion models, there is still uncertainty regarding the potential of video diffusion models in video deblurring tasks.
A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation
The first step is called reconstruction reflection.
CDEMapper: Enhancing NIH Common Data Element Normalization using Large Language Models
We propose CDEMapper, a large language model (LLM) powered mapping tool designed to assist in mapping local data elements to NIH CDEs.
Semantic Data Augmentation for Long-tailed Facial Expression Recognition
Then, for facial expression recognition in RAF-DB dataset, we use our augmentation method to balance the long-tailed distribution.
Training an Open-Vocabulary Monocular 3D Object Detection Model without 3D Data
However, training 3D models with labels directly derived from pseudo-LiDAR is inadequate due to imprecise boxes estimated from noisy point clouds and severely occluded objects.
Global Challenge for Safe and Secure LLMs Track 1
This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
GWQ: Gradient-Aware Weight Quantization for Large Language Models
Compared to current quantization methods, GWQ can be applied to multiple language models and achieves lower PPL on the WikiText2 and C4 dataset.
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration
In this paper, we propose an Explainable Behavior Cloning LLM Agent (EBC-LLMAgent), a novel approach that combines large language models (LLMs) with behavior cloning by learning demonstrations to create intelligent and explainable agents for autonomous mobile app interaction.
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection
We focus on 3D UIAD and multimodal UIAD, providing a comprehensive summary of unsupervised industrial anomaly detection in three modal settings.
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via deep residual network
In this paper, channel estimation (CE) of intelligent reflecting surface aided near-field (NF) multi-user communication is investigated.
Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations
The fundamental idea of label filling is to supervise the segmentation model by a subset of pixels with trustworthy labels, meanwhile filling labels of other pixels by mixed supervision.
Instruction-Driven Game Engine: A Poker Case Study
The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development.
MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes
Furthermore, we introduce an entropy-constrained Gaussian deformation technique that uses a deformation field to expand the action range of each Gaussian and integrates an opacity-based entropy loss to limit the number of Gaussians, thus forcing our model to use as few Gaussians as possible to fit a dynamic scene well.
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression
Through quantitative analysis of the quantization kernel, we find that these elements are crucial for maintaining the accuracy of quantized LLMs.
FDDM: Frequency-Decomposed Diffusion Model for Rectum Cancer Dose Prediction in Radiotherapy
To be specific, we design a Coarse Dose Prediction Module (CDPM) to first predict a coarse dose map and then utilize discrete wavelet transform to decompose the coarse dose map into a low-frequency subband and three high? frequency subbands.
Performance Analysis of Local Partial MMSE Precoding Based User-Centric Cell-Free Massive MIMO Systems and Deployment Optimization
Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks.
Unleashing the Potentials of Likelihood Composition for Multi-modal Language Models
In this framework, people can propose new basic composition methods and combine them to get the new mixed composition methods.
PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution
In particular, compared to recent NGswin, the PlainUSR-L is 16. 4x faster with competitive performance.
RenderWorld: World Model with Self-Supervised 3D Label
End-to-end autonomous driving with vision-only is not only more cost-effective compared to LiDAR-vision fusion but also more reliable than traditional methods.
KVPruner: Structural Pruning for Faster and Memory-Efficient Large Language Models
The bottleneck associated with the key-value(KV) cache presents a significant challenge during the inference processes of large language models.
Block-Attention for Efficient RAG
In RAG scenarios, by defining each passage as a block, Block-Attention enables us to reuse the KV states of passages that have been seen before, thereby significantly reducing the latency and the computation overhead during inference.
ProteinBench: A Holistic Evaluation of Protein Foundation Models
Recent years have witnessed a surge in the development of protein foundation models, significantly improving performance in protein prediction and generative tasks ranging from 3D structure prediction and protein design to conformational dynamics.
RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning
Specifically, to segment a 3D query volume using a limited number of support image-label pairs that define a new segmentation task, we propose reverse propagation strategy as a query information selection mechanism.
LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models
Large language models (LLMs) face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences.
BTMuda: A Bi-level Multi-source unsupervised domain adaptation framework for breast cancer diagnosis
To address these limitations, unsupervised domain adaptation (UDA) methods have been used to transfer knowledge from one labeled source domain to the unlabeled target domain, yet these approaches suffer from severe domain shift issues and often ignore the potential benefits of leveraging multiple relevant sources in practical applications.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
CogVLM2: Visual Language Models for Image and Video Understanding
Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications.
Ranked #10 on
Visual Question Answering
on MM-Vet
A Survey on Facial Expression Recognition of Static and Dynamic Emotions
Facial expression recognition (FER) aims to analyze emotional states from static images and dynamic sequences, which is pivotal in enhancing anthropomorphic communication among humans, robots, and digital avatars by leveraging AI technologies.
ARANet: Attention-based Residual Adversarial Network with Deep Supervision for Radiotherapy Dose Prediction of Cervical Cancer
Radiation therapy is the mainstay treatment for cervical cancer, and its ultimate goal is to ensure the planning target volume (PTV) reaches the prescribed dose while reducing dose deposition of organs-at-risk (OARs) as much as possible.
Alleviating Class Imbalance in Semi-supervised Multi-organ Segmentation via Balanced Subclass Regularization
To alleviate this issue, we present a two-phase semi-supervised network (BSR-Net) with balanced subclass regularization for MoS.
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Financial LLMs hold promise for advancing financial tasks and domain-specific applications.
Boosting Open-Domain Continual Learning via Leveraging Intra-domain Category-aware Prototype
Despite recent progress in enhancing the efficacy of Open-Domain Continual Learning (ODCL) in Vision-Language Models (VLM), failing to (1) correctly identify the Task-ID of a test image and (2) use only the category set corresponding to the Task-ID, while preserving the knowledge related to each domain, cannot address the two primary challenges of ODCL: forgetting old knowledge and maintaining zero-shot capabilities, as well as the confusions caused by category-relatedness between domains.
Open Role-Playing with Delta-Engines
In this paper, we propose a new style of game-play to bridge self-expression and role-playing: \emph{open role-playing games (ORPGs)}, where players are allowed to craft and embody their unique characters in the game world.
S3PET: Semi-supervised Standard-dose PET Image Reconstruction via Dose-aware Token Swap
Our S3PET involves an un-supervised pre-training stage (Stage I) to extract representations from unpaired images, and a supervised dose-aware reconstruction stage (Stage II) to achieve LPET-to-SPET reconstruction by transferring the dose-specific knowledge between paired images.
LoFormer: Local Frequency Transformer for Image Deblurring
Due to the computational complexity of self-attention (SA), prevalent techniques for image deblurring often resort to either adopting localized SA or employing coarse-grained global SA methods, both of which exhibit drawbacks such as compromising global modeling or lacking fine-grained correlation.
Ranked #4 on
Image Deblurring
on GoPro
VILA$^2$: VILA Augmented VILA
In the self-augment step, the instruction-finetuned VLM recaptions its pretraining caption datasets and then retrains from scratch leveraging refined data.
Ranked #63 on
Visual Question Answering
on MM-Vet
Hi-EF: Benchmarking Emotion Forecasting in Human-interaction
We propose a novel Emotion Forecasting (EF) task grounded in the theory that an individuals emotions are easily influenced by the emotions or other information conveyed during interactions with another person.
All rivers run into the sea: Unified Modality Brain-like Emotional Central Mechanism
In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emotions.
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model
Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks.
LaMI-DETR: Open-Vocabulary Detection with Language Model Instruction
Existing methods enhance open-vocabulary object detection by leveraging the robust open-vocabulary recognition capabilities of Vision-Language Models (VLMs), such as CLIP. However, two main challenges emerge:(1) A deficiency in concept representation, where the category names in CLIP's text space lack textual and visual knowledge.
Ranked #1 on
Open Vocabulary Object Detection
on LVIS v1.0
The Oscars of AI Theater: A Survey on Role-Playing with Language Models
This survey explores the burgeoning field of role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simulations facilitated by Large Language Models (LLMs).
Power Optimization and Deep Learning for Channel Estimation of Active IRS-Aided IoT
In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS.
Spatially-Variant Degradation Model for Dataset-free Super-resolution
Unlike existing dataset-free BISR methods that focus on obtaining a degradation kernel for the entire image, we are the first to explicitly design a spatially-variant degradation model for each pixel.
Learning with Alignments: Tackling the Inter- and Intra-domain Shifts for Cross-multidomain Facial Expression Recognition
Facial Expression Recognition (FER) holds significant importance in human-computer interactions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool
Therefore, we try to explore the collection of CoT datasets in videos to lead to video OpenQA and improve the reasoning ability of MLLMs.
xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart
With comprehensive experiments performed, this technical report highlights the potential of xLSTM-based architectures in advancing biomedical image analysis in both 2D and 3D.
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning
Many real-world robot learning problems, such as pick-and-place or arriving at a destination, can be seen as a problem of reaching a goal state as soon as possible.
Prompting Whole Slide Image Based Genetic Biomarker Prediction
Experimental results on two colorectal cancer datasets show the superiority of our method, achieving 91. 49% in AUC for MSI classification.
D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition
The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions from video content, underpinned by training on voluminous datasets.
Dynamic Facial Expression Recognition
Facial Expression Recognition
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution
To extract Uncertainty-based Degradation Representation from LR images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with Uncertainty Suppression Loss to suppress the inherent model uncertainty of the Degradation Extractor.
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications.
Asynchronous Large Language Model Enhanced Planner for Autonomous Driving
Despite real-time planners exhibiting remarkable performance in autonomous driving, the growing exploration of Large Language Models (LLMs) has opened avenues for enhancing the interpretability and controllability of motion planning.
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
To expedite the diffusion process, we further introduce an adversarial diffusive network with a reduced number of diffusion steps.
Joint Power Allocation and Beamforming Design for Active IRS-Aided Directional Modulation Secure Systems
To meet the requirements of the network performance, a power allocation (PA) strategy is proposed and adopted in the system.
AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models
Evaluating the alignment capabilities of large Vision-Language Models (VLMs) is essential for determining their effectiveness as helpful assistants.
AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring
Despite the recent progress in enhancing the efficacy of image deblurring, the limited decoding capability constrains the upper limit of State-Of-The-Art (SOTA) methods.
Ranked #1 on
Image Deblurring
on GoPro
SRFUND: A Multi-Granularity Hierarchical Structure Reconstruction Benchmark in Form Understanding
Accurately identifying and organizing textual content is crucial for the automation of document processing in the field of form understanding.
OUS: Scene-Guided Dynamic Facial Expression Recognition
By analyzing the Rigid Cognitive Problem, OUS successfully understands the complex relationship between scene context and emotional expression, closely aligning with human emotional understanding in real-world scenarios.
Ranked #5 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Facial Expression Recognition
DiffuBox: Refining 3D Object Detection with Point Diffusion
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving.
A New Method in Facial Registration in Clinics Based on Structure Light Images
To abundant traditional image methods with depth information, a method in registering with depth images and traditional clinical images was investigated.
Multi-Stream Keypoint Attention Network for Sign Language Recognition and Translation
The resulting framework is denoted as MSKA-SLR, which is expanded into a sign language translation (SLT) model through the straightforward addition of an extra translation network.
A Causal Explainable Guardrails for Large Language Models
Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases.
Language-Image Models with 3D Understanding
Our experiments on outdoor benchmarks demonstrate that Cube-LLM significantly outperforms existing baselines by 21. 3 points of AP-BEV on the Talk2Car dataset for 3D grounded reasoning and 17. 7 points on the DriveLM dataset for complex reasoning about driving scenarios, respectively.
I$^3$Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis
Based on this observation, we propose an Inter-Intra-slice Interpolation Network (I$^3$Net), which fully explores information from high in-plane resolution and compensates for low through-plane resolution.
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Specifically, we present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics.
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL.
Adaptive Prompt Learning with Negative Textual Semantics and Uncertainty Modeling for Universal Multi-Source Domain Adaptation
Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distribution) and class shifts (unknown target classes).
AccidentBlip: Agent of Accident Warning based on MA-former
In particular, AccidentBlip achieves SOTA performance in both accident detection and prediction tasks on the DeepAccident dataset.
Causal Deconfounding via Confounder Disentanglement for Dual-Target Cross-Domain Recommendation
As a result, dual-target CDR has to meet two challenges: (1) how to effectively decouple observed confounders, including single-domain confounders and cross-domain confounders, and (2) how to preserve the positive effects of observed confounders on predicted interactions, while eliminating their negative effects on capturing comprehensive user preferences.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids.
Task-Aware Encoder Control for Deep Video Compression
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task.
Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness
In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase.
Instruction-Driven Game Engines on Large Language Models
The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game rules and autonomously generate game-play processes.
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure.
Multi-stream Transmission for Directional Modulation Network via Distributed Multi-UAV-aided Multi-active-IRS
In this paper, single large-scale IRS is divided to multiple small IRSs and a novel multi-IRS-aided multi-stream DM network is proposed to achieve a point-to-point multi-stream transmission by creating $K$ ($\geq3$) DoFs, where multiple small IRSs are placed distributively via multiple unmanned aerial vehicles (UAVs).
EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting
Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsule robots.
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes.
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization
MSE-based models aim to improve objective metrics while generative models are leveraged to improve visual quality measured by subjective metrics.
Noise Dimension of GAN: An Image Compression Perspective
This trade-off depicts the best divergence we can achieve when noise is limited.
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage.
CAMSIC: Content-aware Masked Image Modeling Transformer for Stereo Image Compression
Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations.
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
In this paper, we provide a comprehensive review of existing FLHG methods, covering challenges, research progress, and future prospects.
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution
Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolution presents a compelling avenue.
A$^{3}$lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP
Specifically, our A$^{3}$lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional.
Dynamic Facial Expression Recognition
Facial Expression Recognition
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation
Semi-supervised learning is a sound measure to relieve the strict demand of abundant annotated datasets, especially for challenging multi-organ segmentation .
Dose Prediction Driven Radiotherapy Paramters Regression via Intra- and Inter-Relation Modeling
Deep learning has facilitated the automation of radiotherapy by predicting accurate dose distribution maps.
CAMixerSR: Only Details Need More "Attention"
To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR), prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing, and 2) design better super-resolution networks via token mixer refining.
Ranked #44 on
Image Super-Resolution
on Set14 - 4x upscaling
Boosting Neural Representations for Videos with a Conditional Decoder
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks.
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection
In autonomous driving, cooperative perception makes use of multi-view cameras from both vehicles and infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle viewpoint.
A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis
To bridge the distribution gap between user input behavior and model training datasets, we first construct a novel Coarse-Fine Granularity Prompts dataset (CFP) and propose a novel User-Friendly Fine-Grained Text Generation framework (UF-FGTG) for automated prompt optimization.
Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers
Diffusion Inverse Solvers (DIS) are designed to sample from the conditional distribution $p_{\theta}(X_0|y)$, with a predefined diffusion model $p_{\theta}(X_0)$, an operator $f(\cdot)$, and a measurement $y=f(x'_0)$ derived from an unknown image $x'_0$.
A Lightweight Inception Boosted U-Net Neural Network for Routability Prediction
As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design.
Adaptive Hypergraph Network for Trust Prediction
In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations.
Triplet-constraint Transformer with Multi-scale Refinement for Dose Prediction in Radiotherapy
Radiotherapy is a primary treatment for cancers with the aim of applying sufficient radiation dose to the planning target volume (PTV) while minimizing dose hazards to the organs at risk (OARs).
Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies
The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language fluency and reasoning capacities.
DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models
In this paper, for the first time, we systematically explore the detectability of the poisoned noise input for the backdoored diffusion models, an important performance metric yet little explored in the existing works.
Image2Points:A 3D Point-based Context Clusters GAN for High-Quality PET Image Reconstruction
To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images.
Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data
This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data.
GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment
Due to the subjective nature of image quality assessment (IQA), assessing which image has better quality among a sequence of images is more reliable than assigning an absolute mean opinion score for an image.
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding
In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces several significant challenges: (i) the inherent complexity of 3D scenes due to the diverse object configurations, their rich attributes, and intricate relationships; (ii) the scarcity of paired 3D vision-language data to support grounded learning; and (iii) the absence of a unified learning framework to distill knowledge from grounded 3D data.
Idempotence and Perceptual Image Compression
However, we find that theoretically: 1) Conditional generative model-based perceptual codec satisfies idempotence; 2) Unconditional generative model with idempotence constraint is equivalent to conditional generative codec.
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests.
A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters
To address the limitation, we propose a SIM-GAT model to predict spatiotemporal visitation flows between community business clusters and their trade areas.
TeleChat Technical Report
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
To address this challenging problem, we propose a novel Causal OOD Heterogeneous graph Few-shot learning model, namely COHF.
Energy based diffusion generator for efficient sampling of Boltzmann distributions
Sampling from Boltzmann distributions, particularly those tied to high-dimensional and complex energy functions, poses a significant challenge in many fields.
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning
Open-World Few-Shot Learning (OFSL) is a critical field of research concentrating on the precise identification of target samples in environments with scarce data and unreliable labels thus possessing substantial practical significance.
Pixel-level Semantic Correspondence through Layout-aware Representation Learning and Multi-scale Matching Integration
Establishing precise semantic correspondence across object instances in different images is a fundamental and challenging task in computer vision.
PARA-Drive: Parallelized Architecture for Real-time Autonomous Driving
Recent works have proposed end-to-end autonomous vehicle (AV) architectures comprised of differentiable modules achieving state-of-the-art driving performance.
RepAn: Enhanced Annealing through Re-parameterization
The simulated annealing algorithm aims to improve model convergence through multiple restarts of training.
Set Prediction Guided by Semantic Concepts for Diverse Video Captioning
Each caption in the set is attached to a concept combination indicating the primary semantic content of the caption and facilitating element alignment in set prediction.
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
Specifically, we propose a plug-in reparameterized dynamic unit (RDU) to promote the performance and inference cost trade-off.
Object Attribute Matters in Visual Question Answering
In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding.
Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning
In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth.
CogAgent: A Visual Language Model for GUI Agents
People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.
Ranked #4 on
on
Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models
We propose Causal Context Generation, Causal-CoG, which is a prompting strategy that engages contextual information to enhance precise VQA during inference.
Large Language Models for Intent-Driven Session Recommendations
Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions.
Unified learning-based lossy and lossless JPEG recompression
Most image compression algorithms only consider uncompressed original image, while ignoring a large number of already existing JPEG images.
An Embodied Generalist Agent in 3D World
However, several significant challenges remain: (i) most of these models rely on 2D images yet exhibit a limited capacity for 3D input; (ii) these models rarely explore the tasks inherently defined in 3D world, e. g., 3D grounding, embodied reasoning and acting.
ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e. g., coming up with the right arguments for calling routines), requiring a deeper comprehension of complex file interactions.
Explainable History Distillation by Marked Temporal Point Process
To efficiently solve \acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the solution to the program by a model called \acrfull{model}.
PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids
In recent years, the scientific community has become increasingly interested on peptides with non-canonical amino acids due to their superior stability and resistance to proteolytic degradation.
High-performance Power Allocation Strategies for Active IRS-aided Wireless Network
Due to its slow linear convergence from iterative GA, the proposed ESMPI-GA is high-complexity.
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task.
Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding
In the forward process, DiffDose transforms dose distribution maps into pure Gaussian noise by gradually adding small noise and a noise predictor is simultaneously trained to estimate the noise added at each timestep.
CogVLM: Visual Expert for Pretrained Language Models
We introduce CogVLM, a powerful open-source visual language foundation model.
Ranked #4 on
Visual Question Answering (VQA)
on InfiMM-Eval
Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context
Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects.
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
Bandwidth-efficient Inference for Neural Image Compression
With neural networks growing deeper and feature maps growing larger, limited communication bandwidth with external memory (or DRAM) and power constraints become a bottleneck in implementing network inference on mobile and edge devices.
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries.
Gene-induced Multimodal Pre-training for Image-omic Classification
Histology analysis of the tumor micro-environment integrated with genomic assays is the gold standard for most cancers in modern medicine.
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions.
Mind vs. Mouth: On Measuring Re-judge Inconsistency of Social Bias in Large Language Models
We propose that this re-judge inconsistency can be similar to the inconsistency between human's unaware implicit social bias and their aware explicit social bias.
A Unified Framework for 3D Point Cloud Visual Grounding
This elaborate design enables 3DRefTR to achieve both well-performing 3DRES and 3DREC capacities with only a 6% additional latency compared to the original 3DREC model.
Leveraging Large Language Models for Pre-trained Recommender Systems
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs.
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs.
Polymerized Feature-based Domain Adaptation for Cervical Cancer Dose Map Prediction
Recently, deep learning (DL) has automated and accelerated the clinical radiation therapy (RT) planning significantly by predicting accurate dose maps.
Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction
To obtain high-quality positron emission tomography (PET) scans while reducing radiation exposure to the human body, various approaches have been proposed to reconstruct standard-dose PET (SPET) images from low-dose PET (LPET) images.
Conditional Perceptual Quality Preserving Image Compression
We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information.
TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms
Specifically, the TriDo-Former consists of two cascaded networks, i. e., a sinogram enhancement transformer (SE-Former) for denoising the input LPET sinograms and a spatial-spectral reconstruction transformer (SSR-Former) for reconstructing SPET images from the denoised sinograms.
Cross-heterogeneity Graph Few-shot Learning
In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and edges.
Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance
Sifting through their design documents, we identify four common dimensions that drive the development of provenance-based intrusion detection systems (PIDSes): scope (can PIDSes detect modern attacks that infiltrate across application boundaries?
Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra
To improve the accuracy of color image completion with missing entries, we present a recovery method based on generalized higher-order scalars.
Continual Learning in Predictive Autoscaling
In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.
Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation
In DIDA-CDR, we first propose an interpolative data augmentation approach to generating both relevant and diverse augmented user representations to augment sparser domain and explore potential user preferences.
AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception
Driver distraction has become a significant cause of severe traffic accidents over the past decade.
SL: Stable Learning in Source-Free Domain Adaption for Medical Image Segmentation
In this challenge defined as Source-Free UDA, the previous UDA medical methods are limited.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling
Modeling discourse -- the linguistic phenomena that go beyond individual sentences, is a fundamental yet challenging aspect of natural language processing (NLP).
Copy Is All You Need
The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary.
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different system implementations.
Improving the Transferability of Time Series Forecasting with Decomposition Adaptation
However, in time series forecasting, it is difficult to obtain enough data, which limits the performance of neural forecasting models.
A Unified Framework for Online Data-Driven Predictive Control with Robust Safety Guarantees
Despite great successes, model predictive control (MPC) relies on an accurate dynamical model and requires high onboard computational power, impeding its wider adoption in engineering systems, especially for nonlinear real-time systems with limited computation power.
Extended Neighboring Extremal Optimal Control with State and Preview Perturbations
In this work, an extended NE (ENE) framework is developed to systematically adapt the nominal control to both state and preview perturbations.
Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network
Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS.
MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations
In this paper, we present a novel dataset (MedNgage), which consists of patient-nurse conversations about cancer symptom management.
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution.
Joint Uplink and Downlink Resource Allocation Towards Energy-efficient Transmission for URLLC
With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements.
PandaGPT: One Model To Instruction-Follow Them All
To do so, PandaGPT combines the multimodal encoders from ImageBind and the large language models from Vicuna.
Privacy-preserving Adversarial Facial Features
In this paper, we propose an adversarial features-based face privacy protection (AdvFace) approach to generate privacy-preserving adversarial features, which can disrupt the mapping from adversarial features to facial images to defend against reconstruction attacks.
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.
Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation
In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution.
Image Segmentation
Semi-supervised Medical Image Segmentation
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