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Found 24 papers, 10 papers with code
MLP-AMDC: An MLP Architecture for Adaptive-Mask-based Dual-Camera snapshot hyperspectral imaging
In order to improve the inference speed of the reconstruction network, this paper proposes An MLP Architecture for Adaptive-Mask-based Dual-Camera (MLP-AMDC) to replace the transformer structure of the network.
GeT: Generative Target Structure Debiasing for Domain Adaptation
Despite the competitive performance, these pseudo labeling methods rely heavily on the source domain to generate pseudo labels for the target domain and therefore still suffer considerably from source data bias.
Improving Scene Graph Generation with Superpixel-Based Interaction Learning
(ii) We explore intra-entity and cross-entity interactions among the superpixels to enrich fine-grained interactions between entities at an earlier stage.
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection
This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme.
Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs
In this paper, we propose a Time-variant Relation-aware TRansformer (TR$^2$), which aims to model the temporal change of relations in dynamic scene graphs.
Indeterminate Probability Neural Network
Besides, for our proposed neural network framework, the output of neural network is defined as probability events, and based on the statistical analysis of these events, the inference model for classification task is deduced.
Iterative Proposal Refinement for Weakly-Supervised Video Grounding
Weakly-Supervised Video Grounding (WSVG) aims to localize events of interest in untrimmed videos with only video-level annotations.
Unsupervised Feature Representation Learning for Domain-generalized Cross-domain Image Retrieval
This limitation motivates us to present the first attempt at domain-generalized unsupervised cross-domain image retrieval (DG-UCDIR) aiming at facilitating image retrieval between any two unseen domains in an unsupervised way.
LocVTP: Video-Text Pre-training for Temporal Localization
To further enhance the temporal reasoning ability of the learned feature, we propose a context projection head and a temporal aware contrastive loss to perceive the contextual relationships.
CA-UDA: Class-Aware Unsupervised Domain Adaptation with Optimal Assignment and Pseudo-Label Refinement
However, source domain bias that deteriorates the pseudo-labels can still exist since the shared network of the source and target domains are typically used for the pseudo-label selections.
SpatioTemporal Focus for Skeleton-based Action Recognition
Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information from the body joints and parts.
Unsupervised Pre-training for Temporal Action Localization Tasks
These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification and clip-level localization.
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).
On Pursuit of Designing Multi-modal Transformer for Video Grounding
Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression.
Deep Motion Prior for Weakly-Supervised Temporal Action Localization
In this paper, we analyze that the motion cues behind the optical flow features are complementary informative.
Optical Flow Estimation
Weakly-supervised Temporal Action Localization
+1
Long-Short Temporal Modeling for Efficient Action Recognition
Efficient long-short temporal modeling is key for enhancing the performance of action recognition task.
SRF-Net: Selective Receptive Field Network for Anchor-Free Temporal Action Detection
Current mainstream one-stage TAD approaches localize and classify action proposals relying on pre-defined anchors, where the location and scale for action instances are set by designers.
All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection
Compared to the traditional single-stage segmentation network, our NASK conducts the detection in a coarse-to-fine manner with the first stage segmentation spotting the rectangle text proposals and the second one retrieving compact representations.
RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection
Upon the frame, an Interaction Intensifier Module and a Correlation Parsing Module are carefully designed, where: a) interactive semantics from humans can be exploited and passed to objects to intensify interactions, b) interactive correlations among humans, objects and interactions are integrated to promote predictions.
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning
In this paper, we argue that learning by comparing helps identify these hard snippets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short.
Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification
RGB-Infrared person re-identification (RGB-IR Re-ID) aims to match persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the surveillance system under poor light conditions.
PAN: Towards Fast Action Recognition via Learning Persistence of Appearance
In contrast to optical flow, our PA focuses more on distilling the motion information at boundaries.
Ranked #2 on
Action Recognition
on Jester (Gesture Recognition)
Non-Autoregressive Coarse-to-Fine Video Captioning
However, mainstream video captioning methods suffer from slow inference speed due to the sequential manner of autoregressive decoding, and prefer generating generic descriptions due to the insufficient training of visual words (e. g., nouns and verbs) and inadequate decoding paradigm.
Learning Representations for Predicting Future Activities
Foreseeing the future is one of the key factors of intelligence.
