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- SoftTriple Loss: Deep Metric Learning Without Triplet Sampling446
The SoftTriple loss is proposed to extend the SoftMax loss with multiple centers for each class, equivalent to a smoothed triplet loss where each class has a single center.
- TransZero: Attribute-Guided Transformer for Zero-Shot Learning206
This paper proposes an attribute-guided Transformer network to learn the attribute localization for discriminative visual-semantic embedding representations in ZSL, termed TransZero, which achieves a new state-of-the-art on three ZSL benchmarks.
- Deep CTR Prediction in Display Advertising154
A novel deep neural network (DNN) based model is introduced that directly predicts the CTR of an image ad based on raw image pixels and other basic features in one step.
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- Robust Optimization over Multiple Domains80
This work proposes a framework of robust optimization over multiple domains and proposes to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency, and analyzes the convergence rate for convex and non-convex models.
- Digging into Uncertainty in Self-supervised Multi-view Stereo75
A novel Uncertainty reduction Multi-view Stereo (U-MVS) framework for self-supervised learning, which achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents.
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- PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation35
A robust contrastive-based S4 framework, termed the Probabilistic Representation Contrastive Learning (PRCL) framework, to enhance the robustness of the unsupervised training process and introduces Global Distribution Prototypes (GDP) by gathering all PRs throughout the whole training process.
- An Efficient Training Approach for Very Large Scale Face Recognition35
This work proposes a novel training approach, termed Faster Face Classification (F2C), to alleviate time and cost without sacrificing the performance, and proves that the speed is faster than state-of-the-art FC-based methods in terms of recognition accuracy and hardware costs.
- The 3rd Anti-UAV Workshop & Challenge: Methods and Results32
A brief summary of the 3rd Anti-UAV Workshop&Challenge is provided including brief introductions to the top three methods in each track including brief introduction to the bottom three methods.
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- FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio25
This paper abstracts the process of people hearing speech, extracting meaningful cues, and creating vari-ous dynamically audio-consistent talking faces, termed Lis-tening and Imagining, into the task of high-fidelity diverse talking faces generation from a single audio, and intro-duce the Controllable Coherent Frame generation.
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- UniFace++: Revisiting a Unified Framework for Face Reenactment and Swapping via 3D Priors4
This work delves into the intrinsic connections between the intrinsic connections between face reenactment and swapping from a more general training paradigm perspective, introducing a novel diffusion-based unified method UniFace++ and redefining both as target-oriented reconstruction tasks.
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- DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception2
DyRoNet is introduced, a low-rank enhanced dynamic routing framework designed for streaming perception in autonomous driving systems that establishes a new benchmark for streaming perception but also provides valuable engineering insights for future work.
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Publication data from OpenAlex; 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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