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Works206 from public data
- Transforming Model Prediction for Tracking433
This work trains the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets, achieving an AUC of 68.5% on the challenging LaSOT [14] dataset.
- Learning Target Candidate Association to Keep Track of What Not to Track388
This work proposes a training strategy that combines partial annotations with self-supervision to keep track of distractor objects in order to continue tracking the target, and introduces a learned association network to propagate the identities of all target candidates from frame-to-frame.
- Covariance Pooling for Facial Expression Recognition181
The benefits of using a manifold network structure for covariance pooling to improve facial expression recognition are explored and the advantage of pooling image-set features temporally by stacking the designed manifold network of covariance Pooling on top of convolutional network layers is demonstrated.
- 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.
- Structured Bird’s-Eye-View Traffic Scene Understanding from Onboard Images158
This work studies the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image, and shows that the method can be extended to detect dynamic objects on the BEV plane.
- Sliced Wasserstein Generative Models150
This paper proposes to approximate SWDs with a small number of parameterized orthogonal projections in an end-to-end deep learning fashion and designs two types of differentiable SWD blocks to equip modern generative frameworks---Auto-Encoders and Generative Adversarial Networks.
- Source-free Depth for Object Pop-out142
This work adapts depth inference models for object segmentation using the objects’ "pop-out" prior in 3D to adapt the inferred depth maps such that objects can be localized using only 3D information.
- 130
- Complexity Experts are Task-Discriminative Learners for Any Image Restoration128
The proposed MoCE-IR model outperforms state-of-the-art methods, affirming its efficiency and practical applicability and introducing “complexity experts” – flexible expert blocks with varying computational complexity and receptive fields.
- 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.
- Unsupervised Domain Adaptation for Nighttime Aerial Tracking125
A unique object discovery approach is provided to generate training patches from raw nighttime tracking videos to tackle the domain discrepancy and demonstrate the robustness and domain adaptability of the proposed framework in nighttime aerial tracking.
- A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials91
A continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models is suggested, which is clearly more challenging than the existing benchmarks, and offers a suitable evaluation avenue to the future research.
- Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation66
An approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting and an Iterative Self-Learning (ISL) training scheme, which exploits semantic pseudo-labels to provide extra supervision on the target domain.
- Topology Preserving Local Road Network Estimation from Single Onboard Camera Image60
This paper aims at extracting the local road network topology, directly in the bird’ s-eye- view (BEV), all in a complex urban set-ting, using a set of directed lane curves and their interactions, which are captured using their intersection points.
- SceneSplat: Gaussian Splatting-Based Scene Understanding with Vision-Language Pretraining59
This work introduces SceneSplat, to their knowledge the first large-scale 3D indoor scene understanding approach that operates natively on 3DGS, and proposes a self-supervised learning scheme that unlocks rich 3D feature learning from unlabeled scenes.
- 59
- Understanding Bird’s-Eye View of Road Semantics Using an Onboard Camera54
This work studies scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera and proposes a novel architecture that combines these three aspects, which significantly surpasses the current state-of-the-art.
- Deformable Neural Radiance Fields using RGB and Event Cameras51
A novel method to model the deformable neural radiance fields using RGB and event cameras using RGB and event cameras using the asynchronous stream of events and calibrated sparse RGB frames is developed.
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- Robust RGB-D Fusion for Saliency Detection48
This work proposes a robust RGB-D fusion method that benefits from layer-wise, and trident spatial, attention mechanisms, and demonstrates that the proposed fusion method performs consistently better than the state-of-the-art fusion alternatives.
- Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing45
This paper proposes a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors.
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- Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets43
This paper aims at learning category-specific 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category, using the symmetric linear basis shapes without assuming the plane of symmetry to be known.
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- Soft Contrastive Learning for Visual Localization41
This paper argues that any artificial division based on proximity measure is undesirable, due to the inherently ambiguous supervision for images near proximity threshold, and proposes a novel technique that uses soft positive/negative assignments of images for contrastive learning, avoiding the aforementioned problem.
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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