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Works91 from public data
- 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 VOT2015 Challenge Results902
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
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- Target Response Adaptation for Correlation Filter Tracking163
This work proposes a generic framework that can adaptively change the target response from frame to frame, so that the tracker is less sensitive to the cases where circular shifts do not reliably approximate translations.
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- Computationally Budgeted Continual Learning: What Does Matter?95
It is found that, under compute-constrained setting, traditional CL approaches, with no exception, fail to outperform a simple minimal baseline that samples uniformly from memory, suggesting that most existing CL methods are particularly too computationally expensive for realistic budgeted deployment.
- Real-Time Evaluation in Online Continual Learning: A New Hope77
It is shown that a simple baseline outperforms state-of-the-art CL methods under this evaluation, questioning the applicability of existing methods in realistic settings and suggesting that the majority of existing CL literature is tailored to a specific class of streams that is not practical.
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- SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?73
This work provides an analysis on CLIP models trained on synthetic data, providing insights on the data generation strategy, number of samples required, scaling trends, and resulting properties.
- Open Problems in Machine Unlearning for AI Safety63
This paper identifies key limitations that prevent unlearning from serving as a comprehensive solution for AI safety, particularly in managing dual-use knowledge in sensitive domains like cybersecurity and chemical, biological, radiological, and nuclear safety.
- When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations59
It is shown that despite the continuous embedding space being more expressive than the discrete token space, soft-prompting and prefix-tuning are potentially less expressive than full fine-tuning, even with the same number of learnable parameters.
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- Combating Adversaries with Anti-adversaries43
The anti-adversary layer is proposed, which generates an input perturbation in the opposite direction of the adversarial one and feeds the classifier a perturbed version of the input.
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- On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective38
The main finding is that the decision boundaries of a simple network of the form (Affine, ReLU, Affine) are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes.
- Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models34
A supervised learning framework called Bi-Factorial Preference Optimization is proposed, which re-parameterizes a joint RLHF objective of both safety and helpfulness into a single supervised learning objective and significantly outperforms existing approaches in both safety and helpfulness.
- Mixture of Experts Made Intrinsically Interpretable30
MoE-X, a Mixture-of-Experts (MoE) language model designed to be interpretable, achieves performance comparable to dense models while significantly improving interpretability, with interpretability surpassing even sparse autoencoder (SAE)-based approaches.
- ToolTweak: An Attack on Tool Selection in LLM-based Agents29
This paper presents ToolTweak, a lightweight automatic attack that increases selection rates from a baseline of around 20% to as high as 81%, with strong transferability between open-source and closed-source models and evaluation of two defenses: paraphrasing and perplexity filtering.
- Prompting a Pretrained Transformer Can Be a Universal Approximator29
It is demonstrated that much smaller pretrained models than previously thought can be universal approximators when prefixed and offer Jackson-type bounds on the length of the prefix needed to approximate a function to a desired precision.
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- Gabor Layers Enhance Network Robustness27
It is observed that architectures enhanced with Gabor layers gain a consistent boost in robustness over regular models and preserve high generalizing test performance, even though these layers come at a negligible increase in the number of parameters.
- Near to Mid-term Risks and Opportunities of Open-Source Generative AI23
It is argued for the responsible open sourcing of generative AI models in the near and medium term and differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions.
- Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?23
This work proposes a novel metric for measuring adaptation of Online Continual Learning algorithms based on the accuracy on the near-future samples, where spurious correlations are removed, and finds that better generalization can be achieved by retaining and reusing past seen information.
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 2026-10-13. 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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