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Found 25 papers, 9 papers with code
Deep Deconfounded Content-based Tag Recommendation for UGC with Causal Intervention
We first establish a causal graph to represent the relations among uploader, UGC, and tag, where the uploaders are identified as confounders that spuriously correlate UGC and tag selections.
Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation
Recommending appropriate tags to items can facilitate content organization, retrieval, consumption and other applications, where hybrid tag recommender systems have been utilized to integrate collaborative information and content information for better recommendations.
Pyramid Feature Alignment Network for Video Deblurring
To better handle the challenges of complex and large motions, instead of aligning features at each scale separately, lower-scale motion information is used to guide the higher-scale motion estimation.
Deep Causal Reasoning for Recommendations
As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation.
Object-Relation Reasoning Graph for Action Recognition
By combining an object-level graph (OG) and a relation-level graph (RG), the proposed OR2G catches the attribute transitions of objects and reasons about the relationship transitions between objects simultaneously.
Make It Move: Controllable Image-to-Video Generation with Text Descriptions
With both controllable appearance and motion, TI2V aims at generating videos from a static image and a text description.
Optical-Flow-Reuse-Based Bidirectional Recurrent Network for Space-Time Video Super-Resolution
To solve the above problem of the existing methods, we propose a coarse-to-fine bidirectional recurrent neural network instead of using ConvLSTM to leverage knowledge between adjacent frames.
Optical Flow Estimation
Space-time Video Super-resolution
+1
Cross-modal Variational Auto-encoder for Content-based Micro-video Background Music Recommendation
Moreover, the multimodal information is fused by the product-of-experts (PoE) principle, where the semantic information in visual and textual modalities of the micro-video are weighted according to their variance estimations such that the modality with a lower noise level is given more weights.
Predicate correlation learning for scene graph generation
For a typical Scene Graph Generation (SGG) method, there is often a large gap in the performance of the predicates' head classes and tail classes.
Visual Relationship Forecasting in Videos
To evaluate the VRF task, we introduce two video datasets named VRF-AG and VRF-VidOR, with a series of spatio-temporally localized visual relation annotations in a video.
Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing
For the vehicle detection and tracking module, we adopted YOLOv5 and multi-scale tracking to localize the anomalies.
Variational Bandwidth Auto-encoder for Hybrid Recommender Systems
Moreover, by considering the fusion of collaborative and feature variables as a virtual communication channel from an information-theoretic perspective, we introduce a user-dependent channel to dynamically control the information allowed to be accessed from the feature embeddings.
Towards Visual Distortion in Black-Box Attacks
Constructing adversarial examples in a black-box threat model injures the original images by introducing visual distortion.
Multiple Video Frame Interpolation via Enhanced Deformable Separable Convolution
During the learning process, different intermediate time step can be involved as a control variable by means of an extension of coord-conv trick, allowing the estimated components to vary with different input temporal information.
Towards Mesh Saliency Detection in 6 Degrees of Freedom
Traditional 3D mesh saliency detection algorithms and corresponding databases were proposed under several constraints such as providing limited viewing directions and not taking the subject's movement into consideration.
Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework
As an emerging type of user-generated content, micro-video drastically enriches people's entertainment experiences and social interactions.
Learning Compact Reward for Image Captioning
Adversarial learning has shown its advances in generating natural and diverse descriptions in image captioning.
Learned Image Downscaling for Upscaling using Content Adaptive Resampler
The proposed resampler network generates content adaptive image resampling kernels that are applied to the original HR input to generate pixels on the downscaled image.
Ranked #1 on
Image Super-Resolution
on Set14 - 2x upscaling
Obj-GloVe: Scene-Based Contextual Object Embedding
Recently, with the prevalence of large-scale image dataset, the co-occurrence information among classes becomes rich, calling for a new way to exploit it to facilitate inference.
A Review-Driven Neural Model for Sequential Recommendation
Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level.
Person Re-Identification With Cascaded Pairwise Convolutions
In this paper, a novel deep architecture named BraidNet is proposed for person re-identification.
Multi-Level Fusion Based 3D Object Detection From Monocular Images
In this paper, we present an end-to-end deep learning based framework for 3D object detection from a single monocular image.
Ranked #12 on
Vehicle Pose Estimation
on KITTI Cars Hard
3D Object Detection
3D Object Detection From Monocular Images
+3
Macroblock Classification Method for Video Applications Involving Motions
We demonstrate that this low-computation-complexity method can efficiently catch the characteristics of the frame.
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos
In this paper, a new heat-map-based (HMB) algorithm is proposed for group activity recognition.
Intra-and-Inter-Constraint-based Video Enhancement based on Piecewise Tone Mapping
In this paper, we propose a new intra-and-inter-constraint-based video enhancement approach aiming to 1) achieve high intra-frame quality of the entire picture where multiple region-of-interests (ROIs) can be adaptively and simultaneously enhanced, and 2) guarantee the inter-frame quality consistencies among video frames.
