Papers Video Classification
“Video Classification” 태그가 달린 논문 495편 · 필터 해제
FeNN-DMA: A RISC-V SoC for SNN acceleration
Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks such as keyword spotting and video clas…
Video ClassificationKeyword SpottingBreakdance Video classification in the age of Generative AI
Large Vision Language models have seen huge application in several sports use-cases recently. Most of these works have been targeted towards a limited subset of popular sports like soccer, cricket, basketball etc; focusi…
Visual Question AnsweringVideo ClassificationCamera Movement Classification in Historical Footage: A Comparative Study of Deep Video Models
Camera movement conveys spatial and narrative information essential for understanding video content. While recent camera movement classification (CMC) methods perform well on modern datasets, their generalization to hist…
Video ClassificationState Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding
Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby improving the processing efficiency of v…
Video ClassificationDeep Attention-guided Adaptive Subsampling
Although deep neural networks have provided impressive gains in performance, these improvements often come at the cost of increased computational complexity and expense. In many cases, such as 3D volume or video classifi…
Video ClassificationUser to Video: A Model for Spammer Detection Inspired by Video Classification Technology
This article is inspired by video classification technology. If the user behavior subspace is viewed as a frame image, consecutive frame images are viewed as a video. Following this novel idea, a model for spammer detect…
Representation LearningVideo ClassificationA TRIANGLE Enables Multimodal Alignment Beyond Cosine Similarity
Multimodal learning plays a pivotal role in advancing artificial intelligence systems by incorporating information from multiple modalities to build a more comprehensive representation. Despite its importance, current st…
Video ClassificationText RetrievalConcepts in Motion: Temporal Concept Bottleneck Model for Interpretable Video Classification
Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but extending this paradigm to video remains challenging due to the difficulty o…
Video ClassificationImage ClassificationAnatomically Constrained Transformers for Cardiac Amyloidosis Classification
Cardiac amyloidosis (CA) is a rare cardiomyopathy, with typical abnormalities in clinical measurements from echocardiograms such as reduced global longitudinal strain of the myocardium. An alternative approach for detect…
Self-Supervised LearningVideo ClassificationMultimodal Hate Detection Using Dual-Stream Graph Neural Networks
Hateful videos present serious risks to online safety and real-world well-being, necessitating effective detection methods. Although multimodal classification approaches integrating information from several modalities ou…
Video ClassificationGraph Neural NetworkImages in Motion?: A First Look into Video Leakage in Collaborative Deep Learning
Federated learning (FL) allows multiple entities to train a shared model collaboratively. Its core, privacy-preserving principle is that participants only exchange model updates, such as gradients, and never their raw, s…
Image Super-ResolutionVideo ClassificationFederated LearningVideo-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
The rapid growth of visual content consumption across platforms necessitates automated video classification for age-suitability standards like the MPAA rating system (G, PG, PG-13, R). Traditional methods struggle with l…
Video ClassificationContrastive LearningPindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization
The field of visual and audio generation is burgeoning with new state-of-the-art methods. This rapid proliferation of new techniques underscores the need for robust solutions for detecting synthetic content in videos. In…
Video ClassificationAudio GenerationText as Any-Modality for Zero-Shot Classification by Consistent Prompt Tuning
The integration of prompt tuning with multimodal learning has shown significant generalization abilities for various downstream tasks. Despite advancements, existing methods heavily depend on massive modality-specific la…
Video ClassificationAudio ClassificationImage ClassificationRevealing Temporal Label Noise in Multimodal Hateful Video Classification
The rapid proliferation of online multimedia content has intensified the spread of hate speech, presenting critical societal and regulatory challenges. While recent work has advanced multimodal hateful video detection, m…
Video ClassificationHateClipSeg: A Segment-Level Annotated Dataset for Fine-Grained Hate Video Detection
Detecting hate speech in videos remains challenging due to the complexity of multimodal content and the lack of fine-grained annotations in existing datasets. We present HateClipSeg, a large-scale multimodal dataset with…
Video ClassificationFilter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation
Effective content moderation is essential for video platforms to safeguard user experience and uphold community standards. While traditional video classification models effectively handle well-defined moderation tasks, t…
Video ClassificationPolymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices
Real-time multi-label video classification on embedded devices is constrained by limited compute and energy budgets. Yet, video streams exhibit structural properties such as label sparsity, temporal continuity, and label…
Multi-Label ClassificationVideo ClassificationVLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents
Multimodal embedding models have been crucial in enabling various downstream tasks such as semantic similarity, information retrieval, and clustering over different modalities. However, existing multimodal embeddings lik…
Video Question AnsweringRepresentation LearningInformation RetrievalVideo ClassificationMultimodal Alignment with Cross-Attentive GRUs for Fine-Grained Video Understanding
Fine-grained video classification requires understanding complex spatio-temporal and semantic cues that often exceed the capacity of a single modality. In this paper, we propose a multimodal framework that fuses video, i…
Video Classification