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Found 15 papers, 12 papers with code
Textual Prompt Guided Image Restoration
In this paper, an effective textual prompt guided image restoration model has been proposed.
CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation
Regarding the growing nature of real-world data, such an offline training paradigm on ever-expanding data is unsustainable, because models lack the continual learning ability to accumulate knowledge constantly.
You Can Mask More For Extremely Low-Bitrate Image Compression
Extensive experiments have demonstrated that our approach outperforms recent state-of-the-art methods in R-D performance, visual quality, and downstream applications, at very low bitrates.
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information.
Causality-based CTR Prediction using Graph Neural Networks
This paper is dedicated to developing a causality-based CTR prediction model in the GNNs framework (Causal-GNN) integrating representations of feature graph, user graph and ad graph in the context of online advertising.
CTP:Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation
Regarding the growing nature of real-world data, such an offline training paradigm on ever-expanding data is unsustainable, because models lack the continual learning ability to accumulate knowledge constantly.
Anchor-free temporal action localization via Progressive Boundary-aware Boosting
The TCM aggregates the temporal context information and provides features for the IBM and the FPBM.
Accelerate adversarial training with loss guided propagation for robust image classification
In this work, we propose an efficient adversarial training method with loss guided propagation (ATLGP) to accelerate the adversarial training process.
An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions
For the cloth-changing problem, video-based ReID is rarely studied due to the lack of a suitable cloth-changing benchmark, and gait recognition is often researched under controlled conditions.
Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification
Existing RA work can be generally divided into unsupervised methods and fully-supervised methods.
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks.
Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow
We embed a correction layer in skip-connection and leverage the appearance flows in different layers to pre-correct the image features.
A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
In this paper, we propose a novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.
Two-Step Quantization for Low-Bit Neural Networks
In this paper, we propose a simple yet effective Two-Step Quantization (TSQ) framework, by decomposing the network quantization problem into two steps: code learning and transformation function learning based on the learned codes.
Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition
To make it end-to-end and do not rely on any sophisticated body joint detection algorithm, we further propose a two-stream bilinear model which can learn the guidance from the body joints and capture the spatio-temporal features simultaneously.
