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Works151 from public data
- A Metric Learning Reality Check579
Flaws in the experimental methodology of numerous metric learning papers are found, and it is shown that the actual improvements over time have been marginal at best.
- What Makes Fake Images Detectable? Understanding Properties that Generalize489
This work uses a patch-based classifier with limited receptive fields to visualize which regions of fake images are more easily detectable and shows a technique to exaggerate these detectable properties and demonstrates that, even when the image generator is adversarially finetuned against a fake image classifier, it is still imperfect and leaves detectable artifacts in certain image patches.
- AdaViT: Adaptive Vision Transformers for Efficient Image Recognition355
AdaViT is introduced, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition.
- Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors334
A systematic study of adversarial attacks on state-of-the-art object detection frameworks, and a detailed study of physical world attacks using printed posters and wearable clothes, to quantify the performance of such attacks with different metrics.
- MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding329
This study proposes to process videos in an online manner and store past video information in a memory bank, which allows the model to reference historical video content for long-term analysis without exceeding LLMs' context length constraints or GPU memory limits.
- Enhancing Adversarial Example Transferability With an Intermediate Level Attack312
The Intermediate Level Attack (ILA) is introduced, which attempts to fine-tune an existing adversarial example for greater black-box transferability by increasing its perturbation on a pre-specified layer of the source model, improving upon state-of-the-art methods.
- Cross-X Learning for Fine-Grained Visual Categorization277
This paper proposes Cross-X learning, a simple yet effective approach that exploits the relationships between different images and between different network layers for robust multi-scale feature learning and involves two novel components: a cross-category cross-semantic regularizer that guides the extracted features to represent semantic parts and aCross-layerRegularizer that improves the robustness of multi- scale features by matching the prediction distribution across multiple layers.
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- ObjectFormer for Image Manipulation Detection and Localization240
This paper proposes ObjectFormer, a method to detect and localize image manipulations that outperforms state-of-the-art tampering detection and localization methods, and uses a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions.
- Detecting Deep-Fake Videos from Appearance and Behavior231
A biometric-based forensic technique for detecting face-swap deep fakes that combines a static biometric based on facial recognition with a temporal, behavioral biometricbased on facial expressions and head movements and is shown to be efficacy across several large-scale video datasets.
- Graph Inductive Biases in Transformers without Message Passing230
The Graph Inductive bias Transformer (GRIT) is proposed -- a new Graph Transformer that incorporates graph inductive biases without using message passing and achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.
- On Feature Normalization and Data Augmentation168
This paper proposes Moment Exchange, an implicit data augmentation method that encourages the model to utilize the moment information also for recognition models, and replaces the moments of the learned features of one training image by those of another, and also interpolate the target labels to extract training signal from the moments.
- HNeRV: A Hybrid Neural Representation for Videos166
With content-adaptive embeddings and redesigned architecture, HNeRV outperforms implicit methods in video regression tasks for both reconstruction quality and convergence speed, and shows better internal generalization.
- Generate, Segment, and Refine: Towards Generic Manipulation Segmentation162
A manipulated image generation process that creates true positives using currently available datasets is introduced, and a novel generator for creating examples that force the algorithm to focus on boundary artifacts during training is proposed.
- Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning149
Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, is introduced, intending to train an alignment between the patch tokens of the vision encoder and the CLS token of the text encoder, to transfer seamlessly to the task of open vocabulary semantic segmentation.
- On the Robustness of Large Multimodal Models Against Image Adversarial Attacks140
It is found that in general LMMs are not robust to visual adversarial inputs, but the findings suggest that context provided to the model via prompts—such as questions in a QA pair—helps to mitigate the effects of visual adversarial inputs.
- Detecting Everything in the Open World: Towards Universal Object Detection128
UniDetector is proposed, a universal object detector that has the ability to recognize enormous categories in the open world, and surpasses the traditional supervised baselines by more than 4% on average without seeing any corresponding images.
- Quantization Guided JPEG Artifact Correction127
This work creates a novel architecture which is parameterized by the JPEG files quantization matrix, which allows a single model to achieve state-of-the-art performance over models trained for specific quality settings.
- Curriculum Manager for Source Selection in Multi-source Domain Adaptation127
This paper proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS), which outperforms other methods on four well-known benchmarks by significant margins.
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- Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation99
It is argued that the labeled target data needs to be distinguished for effective SSDA, and proposed to explicitly decompose the SSDA task into two sub-tasks: a semi-supervised learning (SSL) task in the target domain and an unsupervised domain adaptation (UDA) task across domains.
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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.
- MTFormer: Multi-task Learning via Transformer and Cross-Task Reasoning95
It is demonstrated that models with transformer structures are more appropriate for MTL than convolutional neural networks (CNNs), and a novel transformer-based architecture named MTFormer is proposed, which achieves state-of-the-art results with limited network parameters and computations.
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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 2026-10-13. 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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