Papers Contrastive Learning
“Contrastive Learning” 태그가 달린 논문 8,167편 · 필터 해제
Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs
Timely detection of crop stress is critical for sustaining yields under increasing drought frequency, yet conventional vegetation index thresholds or image-based clustering often fail to capture stress progression, limit…
Contrastive LearningSkeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining
We propose a novel pretraining strategy for skeleton-based zero-shot spatio-temporal action localization to estimate unseen actions for person instances while overcoming high annotation costs for training via new target …
Spatio-Temporal Action LocalizationContrastive LearningAdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval
Multi-vector representations have emerged as an effective paradigm for multimodal retrieval, representing each sample with multiple complementary embeddings to capture fine-grained cross-modal information. However, exist…
Contrastive LearningIntentQA: Intent Question Answering in Videos by Cognitive Context Reasoning
Video understanding requires intelligent agents to transcend mere recognition of visual facts and comprehend the underlying intents behind human actions (often termed the "dark matter" of social intelligence). To bridge …
Contrastive LearningQuestion AnsweringFinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks
Financial document parsing requires accuracy, structural consistency, and verifiability that current benchmarks often fail to reflect. We present FinixDoc, an end-to-end agentic parsing system for real-world financial do…
Reinforcement LearningContrastive LearningContrastive Representation-Guided Genetic Minority Oversampling for Imbalanced Time-Series Classification
Real-world time-series classification tasks often exhibit class imbalance, which can be extremely severe in some applications. To avoid training biased classifiers on imbalanced data, sampling is one of the most popular …
Contrastive LearningExplainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization
Explainable deepfake detection extends binary classification by requiring models to not only predict authenticity but also provide interpretable justifications. This expanded scope is critical in practice, where users li…
Binary ClassificationContrastive LearningDeepFake DetectionContrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination
Incomplete multimodal sentiment analysis has garnered significant attention in recent years. Existing approaches typically assume that data is missing at random or are designed specifically for certain missing patterns, …
Multimodal Sentiment AnalysisContrastive LearningFinetuning Strategies for Querying Sounds by Vocal Imitation
This technical report describes our winning submission to the AES AIMLA 2025 Challenge on querying sound effects by vocal imitation. We investigate two complementary fine-tuning strategies: contrastive learning with a fr…
Contrastive LearningCL4D: Contrastive Language-4D Pretraining for Vision-Language Reasoning in Dynamic Scenes
4D understanding and reasoning is a fundamental capability for embodied AI agents operating in dynamic physical environments. However, existing vision encoders are largely limited to static 2D images or 3D point clouds w…
Contrastive LearningPoint CloudsMorphoGP: A Nonparametric Framework for Predicting Equilibrium Beach Profiles Under Tidal Influence
The prediction of equilibrium beach profiles under tidal influence is of fundamental importance for sustainable coastal development, informing shoreline protection strategies and managing coastal ecosystems under changin…
Contrastive LearningQuantifying Event Impacts on Time Series via Multiscale Contrastive Learning
Shocks that spread through the web, such as cybersecurity breach disclosures, can abruptly disrupt financial time series and cause substantial abnormal losses. While these events are disclosed as discrete records through…
Contrastive LearningSpurCon: Weighted Supervised Contrastive Learning for Mitigating Spurious Cues in Medical Imaging
Despite the rapid progress of deep neural networks in visual recognition, their adoption in high-risk medical applications remains limited due to reliability and robustness concerns. Models may exploit spurious correlati…
Skin Cancer ClassificationContrastive LearningLooking Beyond the Scale: Do Surgical Skill Models Learn Transferable Representations Across Assessment Rubrics?
Vision-based surgical skill assessment has shown strong in-domain results, yet a fundamental question remains unasked: do these models learn transferable representations of surgical proficiency, or do they merely encode …
Contrastive LearningDelta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection
Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography (EEG) is portable and inexpensive, but its recordings are noisy, vary…
Alzheimer's Disease DetectionContrastive LearningFrom Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model
Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference. Although significant efforts are devoted to adapting VLMs at test…
Zero-Shot Image ClassificationContrastive LearningPOI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment
Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, whe…
Multimodal RecommendationCollaborative FilteringGraph Neural NetworkContrastive LearningFlatLab: A Unified Methodology Framework and Simulation-Based Benchmark for Robotic Manipulation of Flat Objects
Robotic manipulation of flat objects is challenging due to the ungraspable configurations and strong variations in object geometry and material. Existing methods rely on heuristic pre-manipulation and are often evaluated…
Contrastive LearningPoint CloudsS2Dialog: Multimodal Dialogue Retrieval with Semantic and Acoustic-Style Modeling
Multimodal dialogue retrieval aims to retrieve dialogues from multimodal dialogue banks that are similar to a target dialogue in terms of both textual semantics and acoustic conversational styles. Such dialogue-level ret…
Emotion Recognition in ConversationContrastive LearningSpeech SynthesisBias Mitigation in Face Recognition via Demographic-based Supervised Contrastive Learning
Face recognition systems have been shown to be biased toward certain demographic groups by exhibiting different error rates across gender, age, or ethnicity. Though the imbalance of the training data with respect to thes…
Contrastive LearningFace Recognition