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- Wanli OuyangSuggested from co-authorship
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Works13 from public data
- AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA93
A merge watermark information into the U-Net of Stable Diffusion Models via a watermark Low-Rank Adaptation (LoRA) module in a two-stage manner and a scaling matrix to achieve flexible message updates without retraining are proposed.
- Unsupervised Learning of Accurate Siamese Tracking73
A novel unsupervised tracking framework is presented, in which a differentiable region mask is proposed to select features as well as to implicitly penalize tracking errors on intermediate frames, and a mask-guided loss reweighting strategy to assign dynamic weights based on the quality of pseudo labels is proposed.
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- Towards Frame Rate Agnostic Multi-object Tracking18
A Frame Rate Agnostic MOT framework with a Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time and proposes a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information to aid identity matching across multi-frame-rate inputs, improving the capability of the learned model in handling complex motion-appearance relations in FraMOT.
- Decision Controller for Object Tracking With Deep Reinforcement Learning18
A decision controller (DC) which is generally applicable to both SOT and MOT tasks and learns an optimal decision-making policy with a deep reinforcement learning algorithm that maximizes long term tracking performance without supervision is proposed.
- Catch You Everything Everywhere: Guarding Textual Inversion via Concept Watermarking13
The novel concept watermarking is proposed, where watermark information is embedded into the target concept and then extracted from generated images based on the watermarked concept, showing great resilience to different diffusion sampling processes possibly chosen by malicious users, meanwhile preserving utility for normal use.
- Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning12
This work proposes TokenBuncher, the first effective defense specifically targeting RL-based harmful fine-tuning and realizes this defense through entropy-as-reward RL and a Token Noiser mechanism designed to prevent the escalation of harmful capabilities.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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