Mid-level Representation for Visual Recognition
Visual Recognition is one of the fundamental challenges in AI, where the goal is to understand the semantics of visual data. Employing mid-level representation, in particular, shifted the paradigm in visual recognition. The mid-level image/video representation involves discovering and training a set of mid-level visual patterns (e.g., parts and attributes) and represent a given image/video utilizing them. The mid-level patterns can be extracted from images and videos using the motion and appearance information of visual phenomenas. This thesis targets employing mid-level representations for different high-level visual recognition tasks, namely (i)image understanding and (ii)video understanding. In the case of image understanding, we focus on object detection/recognition task. We investigate on discovering and learning a set of mid-level patches to be used for representing the images of an object category. We specifically employ the discriminative patches in a subcategory-aware webly-supervised fashion. We, additionally, study the outcomes provided by employing the subcategory-based models for undoing dataset bias.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionVideo UnderstandingSimilar Papers 제목 키워드 기반
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, car…
General ClassificationObjectObject RecognitionScene ClassificationA Novel Visual Representation on Text Using Diverse Conditional GAN for Visual Recognition
Abstract— Automatic image visual recognition can make full use of largely available images with text descriptions on social media platforms to build large-scale image labeled datasets. In this paper, we propose a nove…
Generative Adversarial NetworkSemantic SegmentationDetection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition
While low-level image features have proven to be effective representations for visual recognition tasks such as object recognition and scene classification, they are inadequate to capture complex semantic meaning require…
Event DetectionObjectobject-detectionObject Detection+3A vector quantized masked autoencoder for audiovisual speech emotion recognition
An important challenge in emotion recognition is to develop methods that can leverage unlabeled training data. In this paper, we propose the VQ-MAE-AV model, a self-supervised multimodal model that leverages masked autoe…
Contrastive LearningEmotion RecognitionRepresentation LearningSelf-Supervised Learning+1Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs
Audio-Visual Speech Recognition (AVSR) leverages both audio and visual modalities to enhance speech recognition robustness, particularly in noisy environments. Recent advancements in Large Language Models (LLMs) have dem…
Audio-Visual Speech RecognitionComputational EfficiencyRepresentation Learningspeech-recognition+2