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Found 11 papers, 2 papers with code
Detecting Human-Object Interactions via Functional Generalization
We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner.
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Vehicle Key-Point and Orientation Estimation
Vehicle Re-Identification
Spatial Priming for Detecting Human-Object Interactions
The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object.
The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory
We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset.
To Boost or not to Boost: On the Limits of Boosted Neural Networks
Boosting is a method for finding a highly accurate hypothesis by linearly combining many ``weak" hypotheses, each of which may be only moderately accurate.
Self-Denoising Neural Networks for Few Shot Learning
This architecture, which we call a Self-Denoising Neural Network (SDNN), can be applied easily to most modern convolutional neural architectures, and can be used as a supplement to many existing few-shot learning techniques.
SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining
On average, we improve by $2. 6$, $3. 9$ and $9. 6$ mAP over previous state-of-the-art methods on three splits of increasing sparsity on COCO.
MOST: Multiple Object localization with Self-supervised Transformers for object discovery
In this work, we present Multiple Object localization with Self-supervised Transformers (MOST) that uses features of transformers trained using self-supervised learning to localize multiple objects in real world images.
Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning
We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video conditioned on the text and the generated image.
SelfEval: Leveraging the discriminative nature of generative models for evaluation
In this work, we show that text-to-image generative models can be 'inverted' to assess their own text-image understanding capabilities in a completely automated manner.
Ranked #62 on
Visual Reasoning
on Winoground
InstanceDiffusion: Instance-level Control for Image Generation
Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image.
Ranked #3 on
Conditional Text-to-Image Synthesis
on COCO-MIG
