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- Learning a no-reference quality metric for single-image super-resolution625
Three types of low-level statistical features in both spatial and frequency domains are designed to quantify super-resolved artifacts and a two-stage regression model is learned to predict the quality scores of super-resolution images without referring to ground-truth images.
- Single-Image Super-Resolution: A Benchmark615
A systematic benchmark evaluation for state-of-the-art single-image super-resolution algorithms based on conventional full-reference metrics and human subject studies to evaluate image quality based on visual perception is presented.
- Depth-Aware Video Frame Interpolation596
A video frame interpolation method which explicitly detects the occlusion by exploring the depth information, and develops a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones.
- VITAL: VIsual Tracking via Adversarial Learning577
The VITAL algorithm is presented, which uses a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes and identifies the mask that maintains the most robust features of the target objects over a long temporal span.
- CREST: Convolutional Residual Learning for Visual Tracking562
This paper proposes the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network, and applies residual learning to take appearance changes into account to reduce model degradation during online update.
- See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks537
This work introduces a novel network, called as CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view and proposes a unified and end-to-end trainable framework where different co-att attention variants can be derived for mining the rich context within videos.
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- Target-Aware Deep Tracking450
This paper develops a regression loss and a ranking loss to guide the generation of target-active and scale-sensitive features and proposes a novel scheme to learn target-aware features, which can better recognize the targets undergoing significant appearance variations than pre-trained deep features.
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- Unsupervised Deep Tracking416
The proposed unsupervised tracker achieves the baseline accuracy of fully supervised trackers, which require complete and accurate labels during training, and exhibits a potential in leveraging unlabeled or weakly labeled data to further improve the tracking accuracy.
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- Deep Regression Tracking with Shrinkage Loss243
This work proposes a novel shrinkage loss to penalize the importance of easy training data and applies residual connections to fuse multiple convolutional layers as well as their output response maps to balance training data.
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- Robust Visual Tracking via Hierarchical Convolutional Features208
This paper interprets the hierarchical features of convolutional layers as a nonlinear counterpart of an image pyramid representation and explicitly exploit these multiple levels of abstraction to represent target objects to improve the accuracy and robustness of visual tracking.
- Single-Model and Any-Modality for Video Object Tracking172
This work introduces Un-Track, a Unified Tracker of a single set of parameters for any modality, which surpasses both SOTA unified trackers and modality-specific counterparts, validating the effectiveness and practicality.
- Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking161
This paper proposes to learn multiple adaptive correlation filters with both long-term and short-term memory of target appearance for robust object tracking and demonstrates that the proposed algorithm performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness.
- HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness158
This paper proposes a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection, and uses a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately and introduces a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner.
- SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction132
SarseOcc is proposed, an efficient occupancy network inspired by sparse point cloud processing that achieves a remarkable 74.9% reduction on FLOPs over the dense baseline and improves accuracy, from 12.8% to 14.1% mIOU, which can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.
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- Unsupervised Deep Representation Learning for Real-Time Tracking125
This work proposes an unsupervised learning method for visual tracking that achieves the baseline accuracy of classic fully supervised trackers while achieving a real-time speed and exhibits a potential in leveraging more unlabeled or weakly labeled data to further improve the tracking accuracy.
- Visual Question Answering with Memory-Augmented Networks112
It is shown that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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