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- 3,070
- Ego4D: Around the World in 3,000 Hours of Egocentric Video2,124
Ego4D, a massive-scale egocentric video dataset and benchmark suite, is introduced and a host of new benchmark challenges centered around understanding the first-person visual experience in the past, present, and future are presented.
- CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society1,998
This paper proposes a novel communicative agent framework named role-playing, using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions, and conducts comprehensive studies on instruction-following cooperation in multi-agent settings.
- A Benchmark and Simulator for UAV Tracking1,912
A new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator that can be coupled with tracking methods to easily extend existing real-world datasets.
- DeepGCNs: Can GCNs Go As Deep As CNNs?1,662
This work presents new ways to successfully train very deep GCNs by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures, and building a very deep 56-layer GCN.
- Can GCNs Go as Deep as CNNs1,662
This work presents new ways to successfully train very deep GCNs by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures, and building a very deep 56-layer GCN.
- 1,403
- PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies1,280
This work revisits the classical PointNet++ through a systematic study of model training and scaling strategies, and proposes PointNeXt, the next version of PointNets, which can be flexibly scaled up and outperforms state-of-the-art methods on both 3D classification and segmentation tasks.
- TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild1,171
This work presents TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild, which covers a wide selection of object classes in broad and diverse context and provides an extensive benchmark on TrackingNet by evaluating more than 20 trackers.
- The Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
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- 665
- Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives640
To push the frontier of first-person video understanding of skilled human activity, a suite of benchmark tasks and their annotations are presented, including fine-grained activity understanding, proficiency estimation, cross-view translation, and 3D hand/body pose.
- G-TAD: Sub-Graph Localization for Temporal Action Detection516
This work proposes a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem.
- Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors493
A two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both 2D and 3D priors, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.
- Self-Supervised Learning by Cross-Modal Audio-Video Clustering484
Cross-Modal Deep Clustering (XDC), a novel self-supervised method that leverages unsupervised clustering in one modality as a supervisory signal for the other modality, is proposed, which is the first self- supervised learning method that outperforms large-scale fully- Supervised pretraining for action recognition on the same architecture.
- 478
- SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network473
Extensive experiments on the challenging COCO dataset demonstrate the effectiveness of the proposed MTGAN method in restoring a clear super-resolved image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods.
- DAPs: Deep Action Proposals for Action Understanding442
Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos, is introduced, which outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize.
- Robust Visual Tracking via Structured Multi-Task Sparse Learning433
The results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity.
- FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model381
This work uses off-the-shelf pretrained networks, such as a face detection model, to construct time-independent energy functions, which guide the generation process without requiring training, and has a broader range of applications than existing training-free methods.
- MVTN: Multi-View Transformation Network for 3D Shape Recognition313
The Multi-View Transformation Network (MVTN) is introduced that regresses optimal view-points for 3D shape recognition, building upon advances in differentiable rendering and can provide network robustness against rotation and occlusion in the 3D domain.
- SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos311
This paper introduces SoccerNet, a benchmark for action spotting in soccer videos, and shows that the best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%.
- Egocentric Video-Language Pretraining306
The recently released Ego4D dataset is exploited to pioneer Egocentric VLP along three directions, and a novel pretraining objective is proposed, dubbed EgoNCE, which adapts video-text contrastive learning to the egocentric domain by mining egOCentric-aware positive and negative samples.
- 305
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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