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Found 9 papers, 2 papers with code
A Strong Baseline for Temporal Video-Text Alignment
In this paper, we consider the problem of temporally aligning the video and texts from instructional videos, specifically, given a long-term video, and associated text sentences, our goal is to determine their corresponding timestamps in the video.
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance.
Multi-modal Prompting for Low-Shot Temporal Action Localization
In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instances from arbitrary categories within some untrimmed videos, even not seen at training time.
WEKA-Based: Key Features and Classifier for French of Five Countries
This paper describes a French dialect recognition system that will appropriately distinguish between different regional French dialects.
Channel Pruned YOLOv5-based Deep Learning Approach for Rapid and Accurate Outdoor Obstacles Detection
To demonstrate the practicality of the pruning method, we select the YOLOv5 model for experiments and provide a data set of outdoor obstacles to show the effect of model.
Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification
We consider a novel clustering task in which clusters can have compositional relationships, e. g., one cluster contains images of rectangles, one contains images of circles, and a third (compositional) cluster contains images with both objects.
Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers
We propose a new method for speaker diarization that can handle overlapping speech with 2+ people.
Compositional Embeddings for Multi-Label One-Shot Learning
We present a compositional embedding framework that infers not just a single class per input image, but a set of classes, in the setting of one-shot learning.
Compositional Embeddings: Joint Perception and Comparison of Class Label Sets
We explore the idea of compositional set embeddings that can be used to infer not just a single class, but the set of classes associated with the input data (e. g., image, video, audio signal).
