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Found 12 papers, 4 papers with code
Adversarial Attack and Defense in Deep Ranking
In this paper, we propose two attacks against deep ranking systems, i. e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
In this paper, we introduce an Action-Context Separation Network (ACSNet) that explicitly takes into account context for accurate action localization.
Ranked #7 on
Weakly Supervised Action Localization
on THUMOS’14
Video Polyp Segmentation
Weakly Supervised Action Localization
+2
Practical Relative Order Attack in Deep Ranking
In this paper, we formulate a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order among a selected set of candidates according to an attacker-specified permutation, with limited interference to other unrelated candidates.
Practical Order Attack in Deep Ranking
The objective of this paper is to formalize and practically implement a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order of a selected set of candidates according to a permutation vector predefined by the attacker, with only limited interference to other unrelated candidates.
Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization
Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision.
Ranked #12 on
Weakly Supervised Action Localization
on THUMOS14
Vocal Bursts Valence Prediction
Weakly Supervised Action Localization
+2
Adversarial Ranking Attack and Defense
In this paper, we propose two attacks against deep ranking systems, i. e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations.
Ladder Loss for Coherent Visual-Semantic Embedding
For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way -- relevant or irrelevant, and all "irrelevant" candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query.
Bimodal Stereo: Joint Shape and Pose Estimation from Color-Depth Image Pair
In the pipeline, the estimated shape is refined by the shape prior from the given depth map under the estimated pose.
Visual Attribute-augmented Three-dimensional Convolutional Neural Network for Enhanced Human Action Recognition
Visual attributes in individual video frames, such as the presence of characteristic objects and scenes, offer substantial information for action recognition in videos.
Low-Latency Human Action Recognition with Weighted Multi-Region Convolutional Neural Network
Termed "Weighted Multi-Region Convolutional Neural Network" (WMR ConvNet), the proposed system is LSTM-free, and is based on 2D ConvNet that does not require the accumulation of video frames for 3D ConvNet filtering.
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition
Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs).
Video-based Sign Language Recognition without Temporal Segmentation
Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data.
