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Found 8 papers, 4 papers with code
A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning
Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning.
Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis
Based on TDC, we propose the temporal dynamic concept modeling network (TDCMN) to learn an accurate and complete concept representation for efficient untrimmed video analysis.
Progressive Multi-resolution Loss for Crowd Counting
Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth.
Bias-Conflict Sample Synthesis and Adversarial Removal Debias Strategy for Temporal Sentence Grounding in Video
Temporal Sentence Grounding in Video (TSGV) is troubled by dataset bias issue, which is caused by the uneven temporal distribution of the target moments for samples with similar semantic components in input videos or query texts.
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e. g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.
Ranked #5 on
Object Detection
on UAVDT
Less Is More: Picking Informative Frames for Video Captioning
We propose a plug-and-play PickNet to perform informative frame picking in video captioning.
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision
In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer.
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network
Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.
