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- LAVT: Language-Aware Vision Transformer for Referring Image Segmentation569
This work shows that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network.
- Is synthetic data from generative models ready for image recognition?443
This work extensively study whether and how synthetic images generated from state-of-the-art text-to-image generation models can be used for image recognition tasks, and focuses on two perspectives: synthetic data for improving classification models in data-scarce settings (i.e. zero-shot and few-shot), and syntheticData for large-scale model pre-training for transfer learning.
- Memory in the Age of AI Agents288
This work aims to provide an up-to-date landscape of current agent memory research, clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering, and articulate a forward-looking perspective on emerging research frontiers.
- A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models254
This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models, image-text matching models, and text- to-image generation models.
- Stable Virtual Camera: Generative View Synthesis with Diffusion Models208
Stable Virtual Camera (Seva), a generalist diffusion model that creates novel views of a scene, given any number of input views and target cameras, is presented, which outperforms existing methods across different datasets and settings.
- Deep FusionNet for Point Cloud Semantic Segmentation196
A deep fusion network architecture (FusionNet) with a unique voxel-based \mini-PointNet" point cloud representation and a new feature aggregation module (fusion module) for large-scale 3D semantic segmentation.
- Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning191
A neural collapse inspired framework for FSCIL that holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion and experiments demonstrate that the proposed framework outperforms the state-of-the-art performances.
- TransMix: Attend to Mix for Vision Transformers140
TransMix is proposed, which mixes labels based on the attention maps of Vision Transformers, which can consistently improve various ViT-based models at scales on ImageNet classification.
- Towards Interpreting Visual Information Processing in Vision-Language Models129
This work studies the processing of visual tokens in the language model component of LLaVA, a prominent VLM, and finds that the model extracts object information from these refined representations at the last token position for prediction, mirroring the process in text-only language models for factual association tasks.
- SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness114
This work proposes an effective and efficient segmentation attack method, dubbed SegPGD, and proposes to apply it as the underlying attack method for segmentation adversarial training, which can boost the robustness of segmentation models.
- Vision-Language Models Do Not Understand Negation92
A data-centric approach wherein CLIP models are finetune on large-scale synthetic datasets containing millions of negated captions is explored, which can result in a 10% increase in recall on negated queries and a 28% boost in accuracy on multiple-choice questions with negated captions.
- Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation90
This work proposes a novel self-supervised approach to find interpretable latent directions for a given concept in an interpretable latent space of diffusion models as seman-tic concepts, and discovers vectors related to inappropriate concepts.
- Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction86
This work provides a principled energy minimisation framework that unifies the two problems of dense stereo reconstruction and object class segmentation and demonstrates that, by resolving ambiguities in real world data, joint optimisation of the two problems substantially improves performance.
- A Survey on Transferability of Adversarial Examples across Deep Neural Networks76
This survey explores the landscape of the adversarial transferability of adversarial examples and categorizes existing methodologies to enhance adversarial transferability, and discusses the fundamental principles guiding each approach.
- HFS: Hierarchical Feature Selection for Efficient Image Segmentation69
This paper proposes a real-time system, Hierarchical Feature Selection (HFS), that performs image segmentation at a speed of 50 frames-per-second, and can be used in a variety computer vision tasks that are built on top ofimage segmentation and superpixel extraction.
- 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.
- Real-Fake: Effective Training Data Synthesis Through Distribution Matching53
This work analyzes the principles underlying training data synthesis for supervised learning and elucidates a principled theoretical framework from the distribution-matching perspective that explicates the mechanisms governing synthesis efficacy.
- Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models48
The robustness of 11 widely-used adaptation methods across 4 vision-language datasets under multimodal corruptions is assessed and it is indicated that increasing the number of adaptation data and parameters does not guarantee enhanced robustness; instead it results in even lower robustness.
- Video Motion Transfer with Diffusion Transformers44
This work proposes DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers, and guides the latent denoising process in an optimization-based, training-free, manner.
- Interleaving Reasoning for Better Text-to-Image Generation40
This work introduces Interleaving Reasoning Generation (IRG), a framework that alternates between text-based thinking and image synthesis: the model first produces a text-based thinking to guide an initial image, then reflects on the result to refine fine-grained details, visual quality, and aesthetics while preserving semantics.
- Benchmarking Open-ended Audio Dialogue Understanding for Large Audio-Language Models40
An Audio Dialogue Understanding Benchmark (ADU-Bench) is proposed, which consists of 4 benchmark datasets that assess the open-ended audio dialogue ability for LALMs in 3 general scenarios, 12 skills, 9 multilingual languages, and 4 categories of ambiguity handling.
- Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?37
GPT4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source LLMs and MLLMs, and Llama2 and Qwen-VL-Chat are more robust compared to other open-source models.
- Can Multimodal Large Language Models Truly Perform Multimodal In-Context Learning?33
A simple yet effective approach is proposed, termed Mixed Modality In-Context Example Selection (MMICES), which considers both visual and language modalities when selecting demos, which revealed that multimodal ICL is predominantly driven by the textual content whereas the visual information in the demos has little influence.
- 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.
- DeepResearch Arena: The First Exam of LLMs' Research Abilities via Seminar-Grounded Tasks29
This work proposes a Multi-Agent Hierarchical Task Generation (MAHTG) system that extracts research-worthy inspirations from seminar transcripts and translates research-worthy inspirations into high-quality research tasks, ensuring the traceability of research task formulation while filtering noise.
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 2026-10-12. 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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