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Found 35 papers, 13 papers with code
Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval
Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on a two-stream structure.
MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets
Driven by powerful image diffusion models, recent research has achieved the automatic creation of 3D objects from textual or visual guidance.
MuPT: A Generative Symbolic Music Pretrained Transformer
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music.
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs.
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
To address the aforementioned limitations, this paper undertakes a comprehensive comparison of model capabilities at various pretraining intermediate checkpoints.
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt
Our method aims to minimize the representation distance of different languages by regarding the image as a central language.
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics.
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs).
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
By conducting a detailed analysis of real multi-turn dialogue data, we construct a three-tier hierarchical ability taxonomy comprising 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks.
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!
Large language models (LLMs) need to undergo safety alignment to ensure safe conversations with humans.
MLAD: A Unified Model for Multi-system Log Anomaly Detection
In spite of the rapid advancements in unsupervised log anomaly detection techniques, the current mainstream models still necessitate specific training for individual system datasets, resulting in costly procedures and limited scalability due to dataset size, thereby leading to performance bottlenecks.
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources.
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning
To bridge the gap among different languages, we propose a cross-lingual instruction fine-tuning framework (xCOT) to transfer knowledge from high-resource languages to low-resource languages.
LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection
Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps).
M2C: Towards Automatic Multimodal Manga Complement
In other words, the Multimodal Manga Complement (M2C) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for vision and language understanding.
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characters.
OWL: A Large Language Model for IT Operations
However, there is a lack of specialized LLMs for IT operations.
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages.
GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking
However, the retrieved passages are not ideal for guiding answer generation because of the discrepancy between retrieval and generation, i. e., the candidate passages are all treated equally during the retrieval procedure without considering their potential to generate a proper answer.
Multilingual Entity and Relation Extraction from Unified to Language-specific Training
In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages.
ICD-Face: Intra-class Compactness Distillation for Face Recognition
Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR).
GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds
In contrast to previous 3D MAE frameworks, which either design a complex decoder to infer masked information from maintained regions or adopt sophisticated masking strategies, we instead propose a much simpler paradigm.
3D-QueryIS: A Query-based Framework for 3D Instance Segmentation
Previous top-performing methods for 3D instance segmentation often maintain inter-task dependencies and the tendency towards a lack of robustness.
LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation
To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages.
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction
Experiments on five benchmarks validate the effectiveness of LogLG for detecting anomalies on unlabeled log data and demonstrate that LogLG, as the state-of-the-art weakly supervised method, achieves significant performance improvements compared to existing methods.
Deep 3D Vessel Segmentation based on Cross Transformer Network
In CTN, a transformer module is constructed in parallel to a U-Net to learn long-distance dependencies between different anatomical regions; and these dependencies are communicated to the U-Net at multiple stages to endow it with global awareness.
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance
Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption.
CoupleFace: Relation Matters for Face Recognition Distillation
In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective face recognition distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework.
Inter-class Discrepancy Alignment for Face Recognition
In this study, we make a key observation that the local con-text represented by the similarities between the instance and its inter-class neighbors1plays an important role forFR.
DAM: Discrepancy Alignment Metric for Face Recognition
To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.
A Unified End-to-End Framework for Efficient Deep Image Compression
Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework
Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters.
Knowledge Distillation via Route Constrained Optimization
However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.
Correlation Congruence for Knowledge Distillation
Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level.
Dynamic Multi-path Neural Network
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.
