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Works67 from public data
- Involution: Inverting the Inherence of Convolution for Visual Recognition418
The proposed involution operator could be leveraged as fundamental bricks to build the new generation of neural networks for visual recognition, powering different deep learning models on several prevalent benchmarks, including ImageNet classification, COCO detection and segmentation, together with Cityscapes segmentation.
- Generative Adversarial Networks in Time Series: A Systematic Literature Review334
A classified GAN variants designed for time series related applications are proposed, in which GANs deal with discrete time series and continuous time series data.
- Generative Adversarial Networks in Computer Vision268
The objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress toward critical computer vision application requirements and discuss the most compelling applications in computer vision.
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- ACTION-Net: Multipath Excitation for Action Recognition247
This work designs a generic and effective module that can be embedded into 2D CNNs to form a simple yet effective ACTION-Net with very limited extra computational cost.
- Avalanche: an End-to-End Library for Continual Learning225
The proposed Avalanche, an open-source end-to-end library for continual learning research based on PyTorch is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.
- Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM199
The term lifelong SLAM is used here to address SLAM problems in an ever-changing environment over a long period of time and the OpenLORIS-Scene datasets are released, which are collected in real-world indoor scenes, for multiple times in each place to include scene changes in real life.
- MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis194
The proposed MINE is a continuous depth generalization of the Multiplane Images (MPI) by introducing the NEural radiance fields (NeRF) to perform novel view synthesis and depth estimation via dense 3D reconstruction from a single image.
- Beyond Text-Visual Attention: Exploiting Visual Cues for Effective Token Pruning in VLMs171
This work proposes VisPruner, a plug-and-play method that utilizes visual cues for more effective token pruning in LVLMs, significantly outperforming existing methods based on text-visual attention.
- Generative Adversarial Networks: A Survey and Taxonomy.94
An in-depth review of GAN-related research in the literature is provided, and an account of the architecture-variant and loss-variants, which have been proposed to handle these three challenges from two perspectives are provided.
- OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning84
A new lifelong robotic vision dataset ("OpenLORIS-Object") collected via RGB-D cameras is provided and the results demonstrate that the object recognition task in the ever-changing difficulty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs.
- Learning from Temporal Gradient for Semi-supervised Action Recognition79
This paper introduces temporal gradient as an additional modality for more attentive feature extraction in semi-supervised video action recognition and explicitly distills the fine-grained motion representations from temporal gradient and imposes consistency across different modalities.
- On Learning Contrastive Representations for Learning with Noisy Labels76
A novel contrastive regularization function is proposed to learn robust contrastive representations of data on which the classifier is hard to memorize the label noise under the CE loss and it is revealed that the learned representations keep information related to true labels and discard informationrelated to corrupted labels.
- MC-LLaVA: Multi-Concept Personalized Vision-Language Model58
Comprehensive experiments demonstrate that MC-LLaVA achieves impressive multi-concept personalized responses, paving the way for VLMs to become better user assistants.
- CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions57
The first Continual Learning in Computer Vision challenge held at CVPR in 2020 has been one of the first opportunities to evaluate different continual learning algorithms on a common hardware with a large set of shared evaluation metrics and 3 different settings based on the realistic CORe50 video benchmark.
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- Learning the Superpixel in a Non-iterative and Lifelong Manner45
This work targets at redefining the CNN-based superpixel segmentation as a lifelong clustering task and pro-poses an unsupervised CNN- based method called LNS-Net, which can learn superpixel in a non-iterative and lifelong manner without any manual labels.
- Challenges in Task Incremental Learning for Assistive Robotics45
All the three most reported lifelong/continual learning algorithms have failed with the increase in encountered batches across various metrics with indistinguishable performance comparing to the naive training mode, highlighting the current challenges in lifelong object recognition for assistive robots to operate in real-world dynamic scene.
- Weakly Supervised Object Localization as Domain Adaption40
A novel perspective is provided that models WSOL as a domain adaption (DA) task, where the score estimator trained on the source/image domain is tested on the target/pixel domain to locate objects and a DA-WSOL pipeline is designed to better engage DA approaches into WSOL to enhance localization performance.
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- Evaluating the Small-World-Ness of a Sampled Network: Functional Connectivity of Entorhinal-Hippocampal Circuitry36
An efficient framework is presented for reconstructing functional connectivity using experimental spike-train data and it is shown that it is feasible to adjust the estimated small-world-ness results based on the number of neurons recorded to provide a more accurate reference of the network property.
- Background-Aware Classification Activation Map for Weakly Supervised Object Localization32
This work proposes a novel mechanism called the background-aware classification activation map (B-CAM) to add background awareness for WSOL training, which trained a background classifier with image-level annotation to produce adaptive background scores when determining the binary localization mask.
- Unifying Nonlocal Blocks for Neural Networks28
By concerning the property of spectral, this paper proposes an efficient and robust spectral nonlocal block, which can be more robust and flexible to catch long-range dependencies when inserted into deep neural networks than the existing nonlocal blocks.
- Reduced-Rank Linear Dynamical Systems25
This work proposes Reduced-Rank Linear Dynamical Systems (RRLDS), to automatically retrieve the intrinsic dimensionality of the latent space during model learning and can robustly learn latent space from short-length, noisy, count-valued data and significantly improve the prediction performance over the state-of-the-art methods.
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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 2026-10-12. 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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