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- ImageNet Large Scale Visual Recognition Challenge43,796
The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.
- What’s the Point: Semantic Segmentation with Point Supervision1,128
This work takes a natural step from image-level annotation towards stronger supervision: it asks annotators to point to an object if one exists, and incorporates this point supervision along with a novel objectness potential in the training loss function of a CNN model.
- Art and the science of generative AI759
The generative capabilities of generative AI are likely to fundamentally alter the creative processes by which creators formulate ideas and put them into production, and may be many sectors of society may be reimagined.
- End-to-End Learning of Action Detection from Frame Glimpses in Videos630
A fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions and uses REINFORCE to learn the agent's decision policy.
- Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos479
A novel variant of long short-term memory deep networks is defined for modeling these temporal relations via multiple input and output connections and it is shown that this model improves action labeling accuracy and further enables deeper understanding tasks ranging from structured retrieval to action prediction.
- Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation434
A simple but surprisingly effective visual recognition benchmark for studying bias mitigation, and a simple but similarly effective alternative to the inference-time Reducing Bias Amplification method of Zhao et al., and design a domain-independent training technique that outperforms all other methods.
- Human Uncertainty Makes Classification More Robust411
It is shown that, while contemporary classifiers fail to exhibit human-like uncertainty on their own, explicit training on this dataset closes this gap, supports improved generalization to increasingly out-of-training-distribution test datasets, and confers robustness to adversarial attacks.
- Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy364
This paper examines ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods, and considers three key factors within the person subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology.
- REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets230
The key aim of the work is to tackle the machine learning bias problem early in the pipeline, and enables the preemptive analysis of large-scale datasets.
- Attribute Learning in Large-Scale Datasets223
This work learns 20 visual attributes and uses them in a zero-shot transfer learning experiment as well as to make visual connections between semantically unrelated object categories.
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- Object-Centric Spatial Pooling for Image Classification213
A framework that learns object detectors using only image-level class labels, or so-called weak labels is proposed, comparable in accuracy with state-of-the-art weakly supervised detection methods and significantly outperforms SPM-based pooling in image classification.
- "Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction210
A mixed-methods study with 20 end-users of a real-world AI application, the Merlin bird identification app, found that participants desire practically useful information that can improve their collaboration with the AI, more so than technical system details.
- Fair Attribute Classification through Latent Space De-biasing195
This work uses GANs to generate realistic-looking images, and perturb these images in the underlying latent space to generate training data that is balanced for each protected attribute, and empirically demonstrates that target classifiers trained on the augmented dataset exhibit a number of both quantitative and qualitative benefits.
- CornerNet-Lite: Efficient Keypoint based Object Detection187
CornerNet-Lite is a combination of two efficient variants of CornerNet: Corner net-Saccade, which uses an attention mechanism to eliminate the need for exhaustively processing all pixels of the image, and CornerNet-Squeeze, which introduces a new compact backbone architecture that addresses the two critical use cases in efficient object detection.
- Understanding and Evaluating Racial Biases in Image Captioning181
Differences in caption performance, sentiment, and word choice between images of lighter versus darker-skinned people are found to be greater in modern captioning systems compared to older ones, thus leading to concerns that without proper consideration and mitigation these differences will only become increasingly prevalent.
- Scalable multi-label annotation176
An algorithm that exploits correlation, hierarchy, and sparsity of the label distribution is proposed that results in up to 6x reduction in human computation time compared to the naive method of querying a human annotator for the presence of every object in every image.
- HIVE: Evaluating the Human Interpretability of Visual Explanations149
HIVE (Human Interpretability of Visual Explanations), a novel human evaluation framework that assesses the utility of explanations to human users in AI-assisted decision making scenarios, and enables falsifiable hypothesis testing, cross-method comparison, and human-centered evaluation of visual interpretability methods is introduced.
- What Actions are Needed for Understanding Human Actions in Videos?139
This work analyzes datasets, evaluation metrics, algorithms, and potential future directions to discover that fine-grained understanding of objects and pose when combined with temporal reasoning is likely to yield substantial improvements in algorithmic accuracy.
- Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation134
This work grapple with questions that arise along three stages of the machine learning pipeline when incorporating intersectionality as multiple demographic attributes: which demographic attributes to include as dataset labels, how to handle the progressively smaller size of subgroups during model training, and how to move beyond existing evaluation metrics when benchmarking model fairness for more subgroups.
- Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies121
It is found that the presence of explanations increases reliance on both correct and incorrect responses, however, users observe less reliance on incorrect responses when sources are provided or when explanations exhibit inconsistencies.
- SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition106
SpatialSense is a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques, and provides a path forward to advancing the spatial reasoning capabilities ofComputer vision systems.
- Toward More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls106
The curriculum designed to achieve goals of the pilot program are to increase interest in AI, contextualize technically rigorous AI concepts through societal impact, and address barriers that could discourage 10th grade girls from pursuing computer science.
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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 2026-10-12. 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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