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- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- A Twofold Siamese Network for Real-Time Object Tracking672
The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks and proposes a channel attention mechanism for the semantic branch.
- Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs639
Despite its compact 3.8-billion-parameter size, this experimental version of Phi-4-Mini achieves reasoning performance on par with or surpassing significantly larger models, including DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Llama-8B.
- Compressive data gathering for large-scale wireless sensor networks628
This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks and shows the efficiency and robustness of the proposed scheme.
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- PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network412
This paper proposes a phase-and-harmonics-aware deep neural network (DNN), named PHASEN, which has the ability to handle detailed phase patterns and to utilize harmonic patterns, and outperforms previous methods by a large margin on four metrics.
- SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking263
The greatest challenge facing visual object tracking is the simultaneous requirements on robustness and discrimination power, and a SiamFC-based tracker, named SPM-Tracker, is proposed, to address the two requirements in two separate matching stages.
- Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning238
The 7B model develops advanced reasoning skills-such as reflection, verification, and summarization-that are absent from the logic corpus after training on just 5K logic problems, and demonstrates generalization abilities to the challenging math benchmarks AIME and AMC.
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- Tracking by Instance Detection: A Meta-Learning Approach193
This work proposes a principled three-step approach to build a high-performance tracker based on two modern detectors RetinaNet and FCOS, and finds that model-agnostic meta-learning (MAML) offers a strategy to initialize the detector that satisfies the authors' needs.
- Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?128
An attention-free network called sMLPNet is built based on the existing MLP-based vision models, replacing the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module that significantly reduces the number of model parameters and computational complexity.
- Make It Move: Controllable Image-to-Video Generation with Text Descriptions121
A Motion Anchor-based video GEnerator (MAGE) with an innovative motion anchor (MA) structure to store appearance-motion aligned representation is proposed to model the uncertainty and increase the diversity of TI2V task.
- StableAnimator: High-Quality Identity-Preserving Human Image Animation114
StableAnimator is presented, the first end-to-end ID-preserving video diffusion framework, which synthesizes high-quality videos without any post-processing, conditioned on a reference image and a sequence of poses.
- CCEdit: Creative and Controllable Video Editing via Diffusion Models97
This work presents CCEdit, a versatile generative video editing framework based on diffusion models that employs a novel trident network structure that separates structure and appearance control, ensuring precise and creative editing capabilities.
- ART•V: Auto-Regressive Text-to-Video Generation with Diffusion Models92
This work presents ART•V, an efficient framework for autoregressive video generation with diffusion models, and proposes masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting.
- Joint Time-Frequency and Time Domain Learning for Speech Enhancement86
A cross-domain framework named TFT-Net, which takes time-frequency spectrogram as input and produces time-domain waveform as output is presented, which achieves the highest SDR and SSNR among state-of-the-art methods on two major speech enhancement benchmarks.
- Seamless rate adaptation for wireless networking76
This paper designs a Seamless Rate Adaptation for wireless networking which achieves smooth rate adjustment in a broad dynamic range of channel conditions and proposes rate compatible modulation (RCM), in which modulation signals are incrementally generated from information bits through weighted mapping.
- Towards a Better Match in Siamese Network Based Visual Object Tracker59
This paper proposes two simple yet effective mechanisms, namely angle estimation and spatial masking, to address issues of Siamese network based trackers, and produces a tracker, named Siam-BM, which is the best-performing realtime tracker to date.
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- StableAvatar: Infinite-Length Audio-Driven Avatar Video Generation52
StableAvatar is presented, the first end-to-end video diffusion transformer that synthesizes infinite-length high-quality videos without post-processing and introduces a novel Time-step-aware Audio Adapter that prevents error accumulation via time-step-aware modulation.
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- FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis46
FloVD leverages optical flow to represent the motions of the camera and moving objects and adopts a two-stage video synthesis pipeline consisting of optical flow generation and flow-conditioned video synthesis.
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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 2026-10-14. 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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