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- MixFormer: End-to-End Tracking with Iterative Mixed Attention911
This paper proposes a compact tracking framework, termed as MixFormer, built upon transformers, to utilize the flexibility of attention operations, and proposes a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
- 618
- Residual Feature Distillation Network for Lightweight Image Super-Resolution610
The feature distillation connection (FDC) is proposed that is functionally equivalent to the channel splitting operation while being more lightweight and flexible and can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distilling network (RFDN).
- 537
- TDN: Temporal Difference Networks for Efficient Action Recognition506
A new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition, which provides a simple and principled temporal modeling framework and could be instantiated with the existing CNNs at a small extra computational cost.
- Learning Actor Relation Graphs for Group Activity Recognition306
This paper proposes to build a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture the appearance and position relation between actors, and performs extensive experiments on two standard group activity recognition datasets.
- SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes257
A new multi-object tracking framework, termed as MixSort, introducing a MixFormer-like structure as an auxiliary association model to prevailing tracking-by-detection trackers is proposed, which achieves state-of-the-art performance on SportsMOT and MOT17.
- Relaxed Transformer Decoders for Direct Action Proposal Generation230
A simple and efficient framework (RTD-Net) for direct action proposal generation, by re-purposing a Transformer-alike architecture, which is more efficient than previous proposal generation methods, without non-maximum suppression post-processing.
- Robust Object Modeling for Visual Tracking203
A robust object modeling framework for visual tracking (ROMTrack) is proposed, which simultaneously models the inherent template and the hybrid template features, thus resulting in a more robust object modeling framework.
- Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding190
A Mutual Matching Network (MMN) is presented, to directly model the similarity between language queries and video moments in a joint embedding space and suggests that metric learning is still a promising method for temporal grounding via capturing the essential cross-modal correlation in aointing space.
- Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation180
This paper proposes a new module to explicitly extract motion and appearance information via a unified operation and rethink the information process in inter-frame attention and reuse its attention map for both appearance feature enhancement and motion information extraction.
- Boundary-Aware Cascade Networks for Temporal Action Segmentation178
A new boundary-aware cascade network is presented by introducing a new cascading paradigm, called Stage Cascade, and a general and principled smoothing operation is designed, termed as local barrier pooling, to aggregate local predictions by leveraging semantic boundary information.
- MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions157
This paper aims to present a new multi-person dataset of spatio-temporal localized sports actions, coined as MultiSports, which it hopes can serve as a standard benchmark for spatio/temporal action detection in the future.
- Actions as Moving Points138
This paper presents a conceptually simple, computationally efficient, and more precise action tubelet detection framework, termed as MovingCenter Detector (MOC-detector), by treating an action instance as a trajectory of moving points.
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- Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution134
A novel sequential attention branch is proposed, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details in a fast and memory-efficient network.
- Depth-aware salient object detection using anisotropic center-surround difference133
The results compared with several state-of-the-art 2D and depth-aware methods show that the proposed saliency-based object segmentation method has the most satisfactory overall performance.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- Self Supervision to Distillation for Long-Tailed Visual Recognition127
It is shown that soft label can serve as a powerful solution to incorporate label correlation into a multi-stage training scheme for long-tailed recognition, as well as a new distillation label generation module guided by self-supervision.
- LIP: Local Importance-Based Pooling118
A unified framework over the common downsampling layers of convolutional neural networks from a view of local aggregation based on importance is presented, and a simple, general, and effective pooling operation based on local importance modeling, termed as Local Importance-based Pooling (LIP).
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- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results101
The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
- Multi-modal deep feature learning for RGB-D object detection98
By introducing the modality-correlated feature representation, the proposed multi-modal RGB-D object detection approach is substantially superior to the state-of-the-art competitors.
- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
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 2026-10-14. 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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