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Found 611 papers, 352 papers with code
RSGT: Relational Structure Guided Temporal Relation Extraction
In this paper, we propose RSGT: Relational Structure Guided Temporal Relation Extraction to extract the relational structure features that can fit for both inter-sentence and intra-sentence relations.
Ranked #1 on
Temporal Relation Classification
on MATRES
Rotation-robust Intersection over Union for 3D Object Detection
In this paper, we propose a Rotation-robust Intersection over Union ($ extit{RIoU}$) for 3D object detection, which aims to jointly learn the overlap of rotated bounding boxes.
Temporal Coherence or Temporal Motion: Which is More Critical for Video-based Person Re-identification?
Specifically, we disentangle the video representation into the temporal coherence and motion parts and randomly change the scale of the temporal motion features as the adversarial noise.
BMInf: An Efficient Toolkit for Big Model Inference and Tuning
In recent years, large-scale pre-trained language models (PLMs) containing billions of parameters have achieved promising results on various NLP tasks.
Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification
Experimental results demonstrate that our DCML method explores credible and valuable training data and improves the performance of unsupervised domain adaptation.
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation
The knowledge selector generally constructs a query based on the dialogue context and selects the most appropriate knowledge to help response generation.
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation
Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection.
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach
In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models.
RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining
Large-scale pretrained language models have achieved SOTA results on NLP tasks.
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction
In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems.
Unsupervised Dependency Graph Network
We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task.
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation
To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotional consensus and utilize some external unpaired data.
Structural Deep Metric Learning for Room Layout Estimation
We employ a metric model and a layout encoder to map the RGB images and the ground-truth layouts to the embedding space, respectively, and a layout decoder to map the embeddings to the corresponding layouts, where the whole framework is trained in an end-to-end manner.
MovieChats: Chat like Humans in a Closed Domain
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild
Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents.
Spatial Geometric Reasoning for Room Layout Estimation via Deep Reinforcement Learning
Unlike most existing works that define room layout on a 2D image, we model the layout in 3D as a configuration of the camera and the room.
ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis
The task aims to extract the structured sentiment information (e. g., holder, target, expression and sentiment polarity) in a text.
Deep Hashing with Active Pairwise Supervision
n this paper, we propose a Deep Hashing method with Active Pairwise Supervision(DH-APS).
欺骗类动词的句法语义研究(On the Syntax and Semantics of Verbs of Cheating)
“欺骗是一种常见的社会现象, 但对欺骗类动词的研究十分有限。本文筛选“欺骗”类动词的单句并对其进行大规模的句法依存和语义依存分析。研究显示,“欺骗”类动词在句中作为从属词时, 可作为不同的句法成分和语义角色, 同时此类动词在句法功能上表现出高度的相似性。作为支配词的“欺骗”类动词, 承担不同句法功能时, 表现出不同的句法共现模式。语义上, 本文详细描述、解释了该类动词在语义密度、主客体角色、情境角色和事件关系等维度的语义依存特点。“欺骗”类动词的句法语义虽具有多样性, 但主要的句型为主谓宾句式, 而该句式中最常用的语义搭配模式是施事对涉事进行欺骗行为, 并对涉事产生影响。本研究结合依存语法和框架语义学, 融合定量统计和定性分析探究欺骗类动词的句法语义, 深化了对欺骗行为言语线索以及言说动词的研究。”
Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning
Prompt-based paradigm has shown its competitive performance in many NLP tasks.
A Dual-Space Framework for General Knowledge Distillation of Large Language Models
However, we reveal that the current white-box KD framework exhibits two limitations: a) bridging probability distributions from different output spaces will limit the similarity between the teacher model and the student model; b) this framework cannot be applied to LLMs with different vocabularies.
Deep Reasoning Translation via Reinforcement Learning
In this paper, we introduce DeepTrans, a deep reasoning translation model that learns free translation via reinforcement learning.
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning
Automatic math correction aims to check students' solutions to mathematical problems via artificial intelligence technologies.
RDTF: Resource-efficient Dual-mask Training Framework for Multi-frame Animated Sticker Generation
In this paper, we argue that under constrained resources, training a smaller video generation model from scratch using only million-level samples can outperform parameter-efficient tuning on larger models in downstream applications: the core lies in the effective utilization of data and curriculum strategy.
D2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens
In contrast, diffusion models take advantage of the continuous-valued tokenizer to achieve better generation quality but are subject to low efficiency and complexity.
EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models
While multimodal large language models demonstrate strong performance in complex reasoning tasks, they pose significant challenges related to model complexity during deployment, especially for resource-limited devices.
DiffMoE: Dynamic Token Selection for Scalable Diffusion Transformers
Diffusion models have demonstrated remarkable success in various image generation tasks, but their performance is often limited by the uniform processing of inputs across varying conditions and noise levels.
Ranked #15 on
Text-to-Image Generation
on GenEval
UniGoal: Towards Universal Zero-shot Goal-oriented Navigation
Specifically, we conduct graph matching between the scene graph and goal graph at each time instant and propose different strategies to generate long-term goal of exploration according to different matching states.
LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning
To deal with this issue, we propose a simple yet effective framework that dynamically improves the embedding model's representation learning for negative pairs based on their discriminative difficulty.
Fence Theorem: Preprocessing is Dual-Objective Semantic Structure Isolator in 3D Anomaly Detection
3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design.
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering
Multimodal large language models (MLLMs) have garnered widespread attention from researchers due to their remarkable understanding and generation capabilities in visual language tasks (e. g., visual question answering).
DBudgetKV: Dynamic Budget in KV Cache Compression for Ensuring Optimal Performance
To alleviate memory burden during inference of large language models (LLMs), numerous studies have focused on compressing the KV cache by exploring aspects such as attention sparsity.
Exploiting Prefix-Tree in Structured Output Interfaces for Enhancing Jailbreak Attacking
We investigate LLMs' safety mechanisms and their recent applications, revealing a new threat model targeting structured output interfaces, which enable attackers to manipulate the inner logit during LLM generation, requiring only API access permissions.
Control-CLIP: Decoupling Category and Style Guidance in CLIP for Specific-Domain Generation
As a result, generation models tend to fail on prompts like "a photo of a cat in Pokemon style" in terms of simply producing images depicting "a photo of a cat".
Warmup-Distill: Bridge the Distribution Mismatch between Teacher and Student before Knowledge Distillation
However, the conventional KD methods endure the distribution mismatch issue between the teacher and student models, leading to the poor performance of distillation.
APB: Accelerating Distributed Long-Context Inference by Passing Compressed Context Blocks across GPUs
While long-context inference is crucial for advancing large language model (LLM) applications, its prefill speed remains a significant bottleneck.
Enhancing Cross-Tokenizer Knowledge Distillation with Contextual Dynamical Mapping
As for the cross-tokenizer KD, the differences in the tokenizers give rise to two fundamental challenges: (1) sequence misalignment caused by divergent tokenization strategies, and (2) mismatched vocabulary size and composition.
Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding
To this end, we propose text-guided pruning (TGP) and completion-based addition (CBA) to deeply fuse 3D scene representation and text features in an efficient way by gradual region pruning and target completion.
The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding
In a systematic way, we investigate a widely asked question: Do LLMs really understand what they say?, which relates to the more familiar term Stochastic Parrot.
Understanding LLMs' Fluid Intelligence Deficiency: An Analysis of the ARC Task
While LLMs have exhibited strong performance on various NLP tasks, it is noteworthy that most of these tasks rely on utilizing the vast amount of knowledge encoded in LLMs' parameters, rather than solving new problems without prior knowledge.
Semantic to Structure: Learning Structural Representations for Infringement Detection
Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly infringes on creators' rights.
LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information
Finally, we apply step-level DPO using the collected stepwise preference pairs.
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
Large Language Models (LLMs) have shown remarkable potential in reasoning while they still suffer from severe factual hallucinations due to timeliness, accuracy, and coverage of parametric knowledge.
NUDT4MSTAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild
This paper introduces NUDT4MSTAR, a large-scale SAR dataset for remote sensing target recognition in the wild, including 40 vehicle target types and various imaging conditions across 5 realistic scenes.
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space
Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise.
Personalized Language Model Learning on Text Data Without User Identifiers
In many practical natural language applications, user data are highly sensitive, requiring anonymous uploads of text data from mobile devices to the cloud without user identifiers.
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls
Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks.
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters
Theory-of-Mind (ToM) is a fundamental psychological capability that allows humans to understand and interpret the mental states of others.
Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection
Specifically, we first construct a semantic graph that well captures the relations among entity tokens and sentences.
WalkVLM:Aid Visually Impaired People Walking by Vision Language Model
Moreover, in blind walking task, it is necessary to perform real-time streaming video parsing and generate concise yet informative reminders, which poses a great challenge for VLMs that suffer from redundant responses and low inference efficiency.
DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
Using Qwen2. 5 and LLama-3. 1 as the backbones, DRT-o1 models can learn the thought process during machine translation, and outperform vanilla LLMs as well as existing O1-like LLMs, showing their effectiveness The project is available at https://github. com/krystalan/DRT-o1
Preventing Local Pitfalls in Vector Quantization via Optimal Transport
Vector-quantized networks (VQNs) have exhibited remarkable performance across various tasks, yet they are prone to training instability, which complicates the training process due to the necessity for techniques such as subtle initialization and model distillation.
Ranked #2 on
Image Reconstruction
on ImageNet
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension
To provide a comprehensive evaluation, PunchBench incorporates diverse question formats and image-captions from various domains.
GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction
3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings.
Doe-1: Closed-Loop Autonomous Driving with Large World Model
In this paper, we explore a closed-loop framework for autonomous driving and propose a large Driving wOrld modEl (Doe-1) for unified perception, prediction, and planning.
Owl-1: Omni World Model for Consistent Long Video Generation
As videos are observations of the underlying evolving world, we propose to model the long-term developments in a latent space and use VGMs to film them into videos.
POINTS1.5: Building a Vision-Language Model towards Real World Applications
Due to the scarcity of open-source Chinese datasets for vision-language models, we collect numerous images from the Internet and annotate them using a combination of manual and automatic methods.
GPD-1: Generative Pre-training for Driving
We represent each scene with ego, agent, and map tokens and formulate autonomous driving as a unified token generation problem.
Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model
To address these limitations, we propose a Spatial-Temporal simulAtion for drivinG (Stag-1) model to reconstruct real-world scenes and design a controllable generative network to achieve 4D simulation.
Compound Gaussian Radar Clutter Model With Positive Tempered Alpha-Stable Texture
This work develops a flexible-tailed CG model to improve generality in clutter modeling, by introducing the positive tempered $\alpha$-stable (PT$\alpha$S) distribution to model clutter texture.
Densing Law of LLMs
This paper introduces the concept of ``\textit{capacity density}'' as a new metric to evaluate the quality of the LLMs across different scales and describes the trend of LLMs in terms of both effectiveness and efficiency.
EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding
3D occupancy prediction provides a comprehensive description of the surrounding scenes and has become an essential task for 3D perception.
Retrieval-Augmented Machine Translation with Unstructured Knowledge
In machine translation (MT), previous work typically retrieves in-context examples from paired MT corpora, or domain-specific knowledge from knowledge graphs, to enhance models' MT ability.
From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents
We categorize the simulations into three types: (1) Individual Simulation, which mimics specific individuals or demographic groups; (2) Scenario Simulation, where multiple agents collaborate to achieve goals within specific contexts; and (3) Society Simulation, which models interactions within agent societies to reflect the complexity and variety of real-world dynamics.
XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation
In our approach, we developed a mask generator based on the denoising UNet from a pre-trained diffusion model, leveraging its capability for precise textual control over dense pixel representations and enhancing the open-world adaptability of the generated masks.
MaDiNet: Mamba Diffusion Network for SAR Target Detection
The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments.
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models
In this paper, we propose CRAT, a novel multi-agent translation framework that leverages RAG and causality-enhanced self-reflection to address these challenges.
MiniPLM: Knowledge Distillation for Pre-Training Language Models
For effectiveness, MiniPLM leverages the differences between large and small LMs to enhance the difficulty and diversity of the training data, helping student LMs acquire versatile and sophisticated knowledge.
GlobalMamba: Global Image Serialization for Vision Mamba
In this paper, we propose a global image serialization method to transform the image into a sequence of causal tokens, which contain global information of the 2D image.
V2M: Visual 2-Dimensional Mamba for Image Representation Learning
Mamba has garnered widespread attention due to its flexible design and efficient hardware performance to process 1D sequences based on the state space model (SSM).
On the token distance modeling ability of higher RoPE attention dimension
We further demonstrate the correlation between the efficiency of length extrapolation and the extension of the high-dimensional attention allocation of these heads.
DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory
Large language models (LLMs) have achieved reasonable quality improvements in machine translation (MT).
Q-VLM: Post-training Quantization for Large Vision-Language Models
On the contrary, we mine the cross-layer dependency that significantly influences discretization errors of the entire vision-language model, and embed this dependency into optimal quantization strategy searching with low search cost.
SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation
Existing zero-shot object navigation methods prompt LLM with the text of spatially closed objects, which lacks enough scene context for in-depth reasoning.
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models
The performance of Large Language Models (LLMs) is substantially influenced by the pretraining corpus, which consists of vast quantities of unsupervised data processed by the models.
Exploring the Benefit of Activation Sparsity in Pre-training
SSD adaptively switches between the Mixtures-of-Experts (MoE) based sparse training and the conventional dense training during the pre-training process, leveraging the efficiency of sparse training and avoiding the static activation correlation of sparse training.
OPONeRF: One-Point-One NeRF for Robust Neural Rendering
In this paper, we propose a One-Point-One NeRF (OPONeRF) framework for robust scene rendering.
MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation
Image generation models have encountered challenges related to scalability and quadratic complexity, primarily due to the reliance on Transformer-based backbones.
A Survey on the Honesty of Large Language Models
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to faithfully express their knowledge.
FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner
By integrating FlowTurbo into different flow-based models, we obtain an acceleration ratio of 53. 1%$\sim$58. 3% on class-conditional generation and 29. 8%$\sim$38. 5% on text-to-image generation.
Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog
Bug localization in Verilog code is a crucial and time-consuming task during the verification of hardware design.
AVG-LLaVA: A Large Multimodal Model with Adaptive Visual Granularity
Recently, when dealing with high-resolution images, dominant LMMs usually divide them into multiple local images and one global image, which will lead to a large number of visual tokens.
POINTS: Improving Your Vision-language Model with Affordable Strategies
To address these issues, we propose the following contributions: 1) We trained a robust baseline model using the latest advancements in vision-language models, introducing effective improvements and conducting comprehensive ablation and validation for each technique.
Ranked #63 on
Visual Question Answering
on MM-Vet
DC-Solver: Improving Predictor-Corrector Diffusion Sampler via Dynamic Compensation
Diffusion probabilistic models (DPMs) have shown remarkable performance in visual synthesis but are computationally expensive due to the need for multiple evaluations during the sampling.
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
Additionally, a well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG.
EmbodiedSAM: Online Segment Any 3D Thing in Real Time
In this paper, we aim to leverage Segment Anything Model (SAM) for real-time 3D instance segmentation in an online setting.
Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization
Traditional base station siting (BSS) methods rely heavily on drive testing and user feedback, which are laborious and require extensive expertise in communication, networking, and optimization.
MiniCPM-V: A GPT-4V Level MLLM on Your Phone
The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of AI research and industry, shedding light on a promising path toward the next AI milestone.
Ranked #7 on
Multiple-choice
on Neptune-Full
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
In the realm of modern mobile E-commerce, providing users with nearby commercial service recommendations through location-based online services has become increasingly vital.
UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation
To this end, we propose a Unified Test-Time Adaptation (UniTTA) benchmark, which is comprehensive and widely applicable.
Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching
With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success.
Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words
This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words), which assesses gender bias beyond binary gender.
Large Language Model for Verilog Generation with Code-Structure-Guided Reinforcement Learning
Recent advancements in large language models (LLMs) have sparked significant interest in the automatic generation of Register Transfer Level (RTL) designs, particularly using Verilog.
Patch-Level Training for Large Language Models
As Large Language Models (LLMs) achieve remarkable progress in language understanding and generation, their training efficiency has become a critical concern.
Mobius: A High Efficient Spatial-Temporal Parallel Training Paradigm for Text-to-Video Generation Task
However, the traditional 3D-Unet is a serial mode and the temporal layers follow the spatial layers, which will result in high GPU memory and training time consumption according to its serial feature flow.
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation
Among them, the target text decoder is used to alleviate the language alignment burden, and the image tokenizer converts long sequences of pixels into shorter sequences of visual tokens, preventing the model from focusing on low-level visual features.
Camera-LiDAR Cross-modality Gait Recognition
In this work, we propose the first cross-modality gait recognition framework between Camera and LiDAR, namely CL-Gait.
C-LLM: Learn to Check Chinese Spelling Errors Character by Character
To address this issue, we propose C-LLM, a Large Language Model-based Chinese Spell Checking method that learns to check errors Character by Character.
Multilingual Knowledge Editing with Language-Agnostic Factual Neurons
To address this issue, we first investigate how LLMs process multilingual factual knowledge and discover that the same factual knowledge in different languages generally activates a shared set of neurons, which we call language-agnostic factual neurons (LAFNs).
P-Tailor: Customizing Personality Traits for Language Models via Mixture of Specialized LoRA Experts
Personalized large language models (LLMs) have attracted great attention in many applications, such as intelligent education and emotional support.
Modeling Comparative Logical Relation with Contrastive Learning for Text Generation
Noting the data scarcity problem, we construct a Chinese Comparative Logical Relation Dataset (CLRD), which is a high-quality human-annotated dataset and challenging for text generation with descriptions of multiple entities and annotations on their comparative logical relations.
TasTe: Teaching Large Language Models to Translate through Self-Reflection
The evaluation results in four language directions on the WMT22 benchmark reveal the effectiveness of our approach compared to existing methods.
Vision Model Pre-training on Interleaved Image-Text Data via Latent Compression Learning
Recently, vision model pre-training has evolved from relying on manually annotated datasets to leveraging large-scale, web-crawled image-text data.
Think out Loud: Emotion Deducing Explanation in Dialogues
Besides, EDEN can help LLMs achieve better recognition of emotions and causes, which explores a new research direction of explainable emotion understanding in dialogues.
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and customizing their behaviors.
Learning 1D Causal Visual Representation with De-focus Attention Networks
The issue of "over-focus" hinders the model's ability to extract diverse visual features and to receive effective gradients for optimization.
Outdated Issue Aware Decoding for Reasoning Questions on Edited Knowledge
Consequently, on the reasoning questions, we discover that existing methods struggle to utilize the edited knowledge to reason the new answer, and tend to retain outdated responses, which are generated by the original models utilizing original knowledge.
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation
Multilingual neural machine translation models generally distinguish translation directions by the language tag (LT) in front of the source or target sentences.
FlowIE: Efficient Image Enhancement via Rectified Flow
Image enhancement holds extensive applications in real-world scenarios due to complex environments and limitations of imaging devices.
Language Generation with Strictly Proper Scoring Rules
Leveraging this strategy, we train language generation models using two classic strictly proper scoring rules, the Brier score and the Spherical score, as alternatives to the logarithmic score.
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective
Building upon this analysis, we propose employing the confidence of predicting EOS as a detector for under-translation, and strengthening the confidence-based penalty to penalize candidates with a high risk of under-translation.
Recent Advances of Foundation Language Models-based Continual Learning: A Survey
Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV).
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction
To address this, we propose an object-centric representation to describe 3D scenes with sparse 3D semantic Gaussians where each Gaussian represents a flexible region of interest and its semantic features.
NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation
Point Cloud Interpolation confronts challenges from point sparsity, complex spatiotemporal dynamics, and the difficulty of deriving complete 3D point clouds from sparse temporal information.
Rethinking Overlooked Aspects in Vision-Language Models
Recent advancements in large vision-language models (LVLMs), such as GPT4-V and LLaVA, have been substantial.
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis
Few works propose continual learning tasks for ABSA, which aim to learn the target domain's ability while maintaining the history domains' abilities.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion
In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance.
Joint Identity Verification and Pose Alignment for Partial Fingerprints
Currently, portable electronic devices are becoming more and more popular.
Sports Analysis and VR Viewing System Based on Player Tracking and Pose Estimation with Multimodal and Multiview Sensors
The visualization results showcase the immense potential of our sports visualization system on the domain of watching games on VR/AR devices.
Latent Fingerprint Matching via Dense Minutia Descriptor
Latent fingerprint matching is a daunting task, primarily due to the poor quality of latent fingerprints.
Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense Registration
Fingerprint dense registration aims to finely align fingerprint pairs at the pixel level, thereby reducing intra-class differences caused by distortion.
Pose-Specific 3D Fingerprint Unfolding
The problem with this method is that there may be large elastic deformation between the unfolded rolled fingerprint and flat fingerprint, which affects the recognition rate.
Direct Regression of Distortion Field from a Single Fingerprint Image
However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose.
Regression of Dense Distortion Field from a Single Fingerprint Image
However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Automatic Knowledge Graph Construction for Judicial Cases
Building on these results, we detail the automatic construction process of case knowledge graphs for judicial cases, enabling the assembly of knowledge graphs for hundreds of thousands of judgments.
UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs
The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment.
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective
In this paper, we suggest that code comments are the natural logic pivot between natural language and code language and propose using comments to boost the code generation ability of code LLMs.
Accelerating Inference in Large Language Models with a Unified Layer Skipping Strategy
Recently, dynamic computation methods have shown notable acceleration for Large Language Models (LLMs) by skipping several layers of computations through elaborate heuristics or additional predictors.
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Low-light image enhancement (LLIE) aims to improve low-illumination images.
LOGO: A Long-Form Video Dataset for Group Action Quality Assessment
Action quality assessment (AQA) has become an emerging topic since it can be extensively applied in numerous scenarios.
DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh Recovery
The recovery of occluded human meshes presents challenges for current methods due to the difficulty in extracting effective image features under severe occlusion.
On Large Language Models' Hallucination with Regard to Known Facts
We reveal the different dynamics of the output token probabilities along the depths of layers between the correct and hallucinated cases.
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check
Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate responses, by augmenting large language models (LLMs) with the external vast and dynamic knowledge.
Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models
In the realm of vision-language understanding, the proficiency of models in interpreting and reasoning over visual content has become a cornerstone for numerous applications.
Ranked #130 on
Visual Question Answering
on MM-Vet
Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering
In addition, we generate the rationales to explain why these events have causal relations.
RCdpia: A Renal Carcinoma Digital Pathology Image Annotation dataset based on pathologists
The annotation of digital pathological slide data for renal cell carcinoma is of paramount importance for correct diagnosis of artificial intelligence models due to the heterogeneous nature of the tumor.
Learning Dual-Level Deformable Implicit Representation for Real-World Scale Arbitrary Super-Resolution
The appearance embedding models the characteristics of low-resolution inputs to deal with photometric variations at different scales, and the pixel-based deformation field learns RGB differences which result from the deviations between the real-world and simulated degradations at arbitrary coordinates.
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection.
Memory-based Adapters for Online 3D Scene Perception
To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability.
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models
Since large language models (LLMs) achieve significant success in recent years, the hallucination issue remains a challenge, numerous benchmarks are proposed to detect the hallucination.
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder
Information extraction (IE) aims to extract complex structured information from the text.
Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
In practice, the multifaceted nature of human preferences inadvertently introduces what is known as the "alignment tax" -a compromise where enhancements in alignment within one objective (e. g., harmlessness) can diminish performance in others (e. g., helpfulness).
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
To address this, we propose the Information Bottleneck-based Gradient (\texttt{IBG}) explanation framework for ABSA.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval.
Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models
Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain.
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
To overcome the limitations of existing data augmentation methods that compromise semantic integrity and address the uncertainty inherent in LLM-generated text, we leverage the distinctive characteristics of the NER task by augmenting the original data at both the contextual and entity levels.
Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions
This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more cohesively related than isolated.
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy
Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models.
3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection
To alleviate this issue, we employ maximum intensity projection (MIP) to decrease the dimensionality of 3D volume to 2D image for efficient annotation, and the 2D labels are utilized to provide guidance and oversight for training 3D vessel segmentation model.
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
In this work, we take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents.
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
Previously on the Stories: Recap Snippet Identification for Story Reading
Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand the ongoing plot.
NPSVC++: Nonparallel Classifiers Encounter Representation Learning
This paper focuses on a specific family of classifiers called nonparallel support vector classifiers (NPSVCs).
On Prompt-Driven Safeguarding for Large Language Models
In this work, we investigate how LLMs' behavior (i. e., complying with or refusing user queries) is affected by safety prompts from the perspective of model representation.
Path Choice Matters for Clear Attribution in Path Methods
Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust.
MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer
Developing generative models for interleaved image-text data has both research and practical value.
Generative Multi-Modal Knowledge Retrieval with Large Language Models
Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications.
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks.
LowRankOcc: Tensor Decomposition and Low-Rank Recovery for Vision-based 3D Semantic Occupancy Prediction
In this paper we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction.
Domain Similarity-Perceived Label Assignment for Domain Generalized Underwater Object Detection
The inherent characteristics and light fluctuations of water bodies give rise to the huge difference between different layers and regions in underwater environments.
MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA
Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world.
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields.
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.
Mathematical Language Models: A Survey
In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics.
LiCamPose: Combining Multi-View LiDAR and RGB Cameras for Robust Single-frame 3D Human Pose Estimation
Several methods have been proposed to estimate 3D human pose from multi-view images, achieving satisfactory performance on public datasets collected under relatively simple conditions.
HumanReg: Self-supervised Non-rigid Registration of Human Point Cloud
In this paper, we present a novel registration framework, HumanReg, that learns a non-rigid transformation between two human point clouds end-to-end.
LiDAR-based Person Re-identification
Camera-based person re-identification (ReID) systems have been widely applied in the field of public security.
Fixed-length Dense Descriptor for Efficient Fingerprint Matching
In fingerprint matching, fixed-length descriptors generally offer greater efficiency compared to minutiae set, but the recognition accuracy is not as good as that of the latter.
SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction
Our SelfOcc outperforms the previous best method SceneRF by 58. 7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes.
LiDAR-HMR: 3D Human Mesh Recovery from LiDAR
In recent years, point cloud perception tasks have been garnering increasing attention.
Ranked #1 on
3D Human Pose Estimation
on SLOPER4D
LDConv: Linear deformable convolution for improving convolutional neural networks
In response to the above questions, the Linear Deformable Convolution (LDConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance.
MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships.
Distilling Rule-based Knowledge into Large Language Models
The current paradigm of knowledge learning for LLMs is mainly based on learning from examples, in which LLMs learn the internal rule implicitly from a certain number of supervised examples.
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases.
Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction
However, in the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics.
TEAL: Tokenize and Embed ALL for Multi-modal Large Language Models
Specifically, for the input from any modality, TEAL first discretizes it into a token sequence with the off-the-shelf tokenizer and embeds the token sequence into a joint embedding space with a learnable embedding matrix.
Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding
Contemporary translation engines based on the encoder-decoder framework have made significant strides in development.
Plot Retrieval as an Assessment of Abstract Semantic Association
Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots.
Universal Multi-modal Multi-domain Pre-trained Recommendation
There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems.
Fast Shapley Value Estimation: A Unified Approach
Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks.
MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory
Due to the high price and heavy energy consumption of GPUs, deploying deep models on IoT devices such as microcontrollers makes significant contributions for ecological AI.
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules
Pre-trained language models (PLMs) have achieved remarkable results on NLP tasks but at the expense of huge parameter sizes and the consequent computational costs.
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language
With the thriving of pre-trained language model (PLM) widely verified in various of NLP tasks, pioneer efforts attempt to explore the possible cooperation of the general textual information in PLM with the personalized behavioral information in user historical behavior sequences to enhance sequential recommendation (SR).
Boosting Inference Efficiency: Unleashing the Power of Parameter-Shared Pre-trained Language Models
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resource-constrained environments, enabling substantial reductions in model storage and memory costs without significant performance compromise.
DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning
The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars.
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners
Active learning (AL), which aims to construct an effective training set by iteratively curating the most formative unlabeled data for annotation, has been widely used in low-resource tasks.
C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network
In addition, word-level optimization based on numbers ignores the semantics of reports and medical images, and the generated reports often cannot achieve good performance.
Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information
However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences.
Skip-Plan: Procedure Planning in Instructional Videos via Condensed Action Space Learning
By this means, our model explores all sorts of reliable sub-relations within an action sequence in the condensed action space.
