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Works118 from public data
- Improving Transferability of Adversarial Examples With Input Diversity1,524
This work proposes to improve the transferability of adversarial examples by creating diverse input patterns by applying random transformations to the input images at each iteration, and shows that the proposed attack method can generate adversarialExamples that transfer much better to different networks than existing baselines.
- Adversarial Examples for Semantic Segmentation and Object Detection1,057
This paper proposes a novel algorithm named Dense Adversary Generation (DAG), which applies to the state-of-the-art networks for segmentation and detection, and finds that the adversarial perturbations can be transferred across networks with different training data, based on different architectures, and even for different recognition tasks.
- Feature Denoising for Improving Adversarial Robustness1,031
It is suggested that adversarial perturbations on images lead to noise in the features constructed by these networks, and new network architectures are developed that increase adversarial robustness by performing feature denoising.
- Adversarial Examples Improve Image Recognition634
This work proposes AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting, and shows that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger.
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- Adversarial Attacks and Defences Competition364
In this chapter, the structure and organization of the NIPS 2017 competition is described and the solutions developed by several of the top-placing teams are described.
- SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning267
This approach introduces an experience-based distillation mechanism to build a hierarchical skill library SkillBank, an adaptive retrieval strategy for general and task-specific heuristics, and a recursive evolution mechanism that allows the skill library to co-evolve with the agent's policy during reinforcement learning.
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- Single-Shot Object Detection with Enriched Semantics228
A novel single shot object detection network named Detection with Enriched Semantics (DES) is proposed, to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module.
- Universal Physical Camouflage Attacks on Object Detectors226
This paper proposes to learn an adversarial pattern to effectively attack all instances belonging to the same object category, referred to as Universal Physical Camouflage Attack (UPC), which crafts camouflage by jointly fooling the region proposal network, as well as misleading the classifier and the regressor to output errors.
- HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing218
This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits, and proposes two evaluation metrics, Alignment and Coherence, to quantitatively assess the quality of image edit pairs using GPT-4V and DALL-E 3.
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- Learning Transferable Adversarial Examples via Ghost Networks171
Ghost Networks is proposed to improve the transferability of adversarial examples by reproducing the NeurIPS 2017 adversarial competition, and outperforms the No.1 attack submission by a large margin, demonstrating its effectiveness and efficiency.
- SimpleMem: Efficient Lifelong Memory for LLM Agents149
SimpleMem is introduced, an efficient memory framework based on semantic lossless compression that consistently outperforms baseline approaches in accuracy, retrieval efficiency, and inference cost, demonstrating a superior balance between performance and efficiency.
- PatchAttack: A Black-Box Texture-Based Attack with Reinforcement Learning142
The proposed PatchAttack is query efficient and can break models for both targeted and non-targeted attacks, and circumvents state-of-the-art adversarial defense methods successfully.
- How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs116
This work introduces a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness, and proposes a straightforward attack strategy for misleading VLLMs to produce visual-unrelated responses.
- MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine108
This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities with multigranular annotations for more than 65 diseases, and proposes LLaVA-Tri by pretraining LLaVA on MedTrinity-25M, surpassing representative SOTA multimodal large language models.
- Audio-Visual LLM for Video Understanding100
This paper introduces Audio-Visual LLM, a novel Multimodal Large Language Model designed for holistic video understanding through integrated visual and auditory inputs, using uniquely designed modality-specific tokens to selectively activate the corresponding visual and auditory encoders.
- VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models99
This work introduces VideoHallucer, the first comprehensive benchmark for hallucination detection in large video-language models (LVLMs), which categorizes hallucinations into two main types: intrinsic and extrinsic, offering further subcategories for detailed analysis.
- Diffusion Models as Masked Autoencoders98
This work revisits generatively pre-training visual representations in light of recent interest in denoising diffusion models and condition diffusion models on masked input and formulate diffusion models as masked autoencoders (DiffMAE).
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- What If We Recaption Billions of Web Images with LLaMA-3?89
The empirical results confirm that this enhanced dataset, Recap-DataComp-1B, offers substantial benefits in training advanced vision-language models, and the generated images exhibit a significant improvement in alignment with users' text instructions, especially in following complex queries.
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- DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation88
This work proposes to boost the representation learning of a multi-camera BEV based student detector by training it to imitate the features of a well-trained LiDAR based teacher detector, leading to the state-of-the-art performance on the popular benchmark nuScenes.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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