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Found 14 papers, 9 papers with code
Steering Rectified Flow Models in the Vector Field for Controlled Image Generation
In this work, we first develop a theoretical and empirical understanding of the vector field dynamics of RFMs in efficiently guiding the denoising trajectory.
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model
Despite advancements in text-to-image models, generating images that precisely align with textual descriptions remains challenging due to misalignment in training data.
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives
However, the lack of compositional diversity in contemporary image-text datasets limits the compositional reasoning ability of CLIP.
Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
An ability to learn about new objects from a small amount of visual data and produce convincing linguistic justification about the presence/absence of certain concepts (that collectively compose the object) in novel scenarios is an important characteristic of human cognition.
$λ$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space
While LDMs offer distinct advantages, P-T2I methods' reliance on the latent space of these diffusion models significantly escalates resource demands, leading to inconsistent results and necessitating numerous iterations for a single desired image.
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations
The T2I prior model alone adds a billion parameters compared to the Latent Diffusion Models, which increases the computational and high-quality data requirements.
ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models
To quantify the ability of T2I models in learning and synthesizing novel visual concepts (a. k. a.
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
This paper introduces a novel approach to model fingerprinting that assigns responsibility for the generated images, thereby serving as a potential countermeasure to model misuse.
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering
Videos often capture objects, their visible properties, their motion, and the interactions between different objects.
Ranked #1 on
Counterfactual Planning
on CRIPP-VQA
Reasoning about Actions over Visual and Linguistic Modalities: A Survey
'Actions' play a vital role in how humans interact with the world and enable them to achieve desired goals.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
CinC-GAN for Effective F0 prediction for Whisper-to-Normal Speech Conversion
The CycleGAN-based method uses two different models, one for Mel Cepstral Coefficients (MCC) mapping, and another for F0 prediction, where F0 is highly dependent on the pre-trained model of MCC mapping.
AdaGAN: Adaptive GAN for Many-to-Many Non-Parallel Voice Conversion
In this paper, we propose a novel style transfer architecture, which can also be extended to generate voices even for target speakers whose data were not used in the training (i. e., case of zero-shot learning).
Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN
Short-term rainfall forecasting, also known as precipitation nowcasting has become a potentially fundamental technology impacting significant real-world applications ranging from flight safety, rainstorm alerts to farm irrigation timings.
