5 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Controlling Embedding Spaces with Text-Conditioned Transformations
Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani +2
Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity retrieval and cross-modal zero-shot classification. These embeddings compre…
SafeR-CLIP: Mitigating NSFW Content in Vision-Language Models While Preserving Pre-Trained Knowledge
Adeel Yousaf, Joseph Fioresi, James Beetham +2
Improving the safety of vision-language models like CLIP via fine-tuning often comes at a steep price, causing significant drops in their generalization performance. We find this t…
Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
We introduce a novel formulation of visual privacy preservation for video foundation models that operates entirely in the latent space. While spatio-temporal features learned by fo…
VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues
Sirnam Swetha, Rohit Gupta, Parth Parag Kulkarni +5
Video Question Answering (VideoQA) has made significant strides by leveraging multimodal learning to align visual and textual modalities. However, current benchmarks overwhelmingly…
ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
Bias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains underexplored in action recognition…
TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
Video anomaly detection (VAD) without human monitoring is a complex computer vision task that can have a positive impact on society if implemented successfully. While recent advanc…