Search Results for author:
Found 28 papers, 20 papers with code
Make Me Happier: Evoking Emotions Through Image Diffusion Models
For the first time, we present a novel challenge of emotion-evoked image generation, aiming to synthesize images that evoke target emotions while retaining the semantics and structures of the original scenes.
TTA-Nav: Test-time Adaptive Reconstruction for Point-Goal Navigation under Visual Corruptions
Our "plug-and-play" method incorporates a top-down decoder to a pre-trained navigation model.
Adaptive Visual Scene Understanding: Incremental Scene Graph Generation
To address the lack of continual learning methodologies in SGG, we introduce the comprehensive Continual ScenE Graph Generation (CSEGG) dataset along with 3 learning scenarios and 8 evaluation metrics.
Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory
Here, we introduce a comprehensive Working Memory (WorM) benchmark dataset for this purpose.
Integrating Curricula with Replays: Its Effects on Continual Learning
Our study takes initial steps in examining the impact of integrating curricula with replay methods on continual learning in three specific aspects: the interleaved frequency of replayed exemplars with training data, the sequence in which exemplars are replayed, and the strategy for selecting exemplars into the replay buffer.
Training-free Object Counting with Prompts
However, the vanilla mask generation method of SAM lacks class-specific information in the masks, resulting in inferior counting accuracy.
Ranked #1 on
Object Counting
on PASCAL VOC
Object-centric Learning with Cyclic Walks between Parts and Whole
To capture compositional entities of the scene, we proposed cyclic walks between perceptual features extracted from vision transformers and object entities.
Learning to Learn: How to Continuously Teach Humans and Machines
For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication.
Efficient Zero-shot Visual Search via Target and Context-aware Transformer
Target modulation is computed as patch-wise local relevance between the target and search images, whereas contextual modulation is applied in a global fashion.
Reason from Context with Self-supervised Learning
To better accommodate the object-centric nature of current downstream tasks such as object recognition and detection, various methods have been proposed to suppress contextual biases or disentangle objects from contexts.
Human or Machine? Turing Tests for Vision and Language
To address this question, we turn to the Turing test and systematically benchmark current AIs in their abilities to imitate humans.
The Role of Robust Generalization in Continual Learning: Better Transfer and Less Forgetting
To the best of our knowledge, no existing technique can accomplish all of these objectives simultaneously.
Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task
However, CL on VQA involves not only the expansion of label sets (new Answer sets).
Improving generalization by mimicking the human visual diet
We present a new perspective on bridging the generalization gap between biological and computer vision -- mimicking the human visual diet.
Label-Efficient Online Continual Object Detection in Streaming Video
Remarkably, with only 25% annotated video frames, our method still outperforms the base CL learners, which are trained with 100% annotations on all video frames.
Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
To elucidate the mechanisms responsible for asymmetry in visual search, we propose a computational model that takes a target and a search image as inputs and produces a sequence of eye movements until the target is found.
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes
Our model captures useful information for contextual reasoning, enabling human-level performance and better robustness in out-of-context conditions compared to baseline models across OCD and other out-of-context datasets.
Tuned Compositional Feature Replays for Efficient Stream Learning
To address the limited number of existing online stream learning datasets, we introduce 2 new benchmarks by adapting existing datasets for stream learning.
Look Twice: A Generalist Computational Model Predicts Return Fixations across Tasks and Species
Primates constantly explore their surroundings via saccadic eye movements that bring different parts of an image into high resolution.
What am I Searching for: Zero-shot Target Identity Inference in Visual Search
Using those error fixations, we developed a model (InferNet) to infer what the target was.
Putting visual object recognition in context
To model the role of contextual information in visual recognition, we systematically investigated ten critical properties of where, when, and how context modulates recognition, including the amount of context, context and object resolution, geometrical structure of context, context congruence, and temporal dynamics of contextual modulation.
Prototype Recalls for Continual Learning
Without tampering with the performance on initial tasks, our method learns novel concepts given a few training examples of each class in new tasks.
Variational Prototype Replays for Continual Learning
In each classification task, our method learns a set of variational prototypes with their means and variances, where embedding of the samples from the same class can be represented in a prototypical distribution and class-representative prototypes are separated apart.
Lift-the-flap: what, where and when for context reasoning
Context reasoning is critical in a wide variety of applications where current inputs need to be interpreted in the light of previous experience and knowledge.
What am I Searching for: Zero-shot Target Identity Inference in Visual Search
Using those error fixations, we developed a model (InferNet) to infer what the target was.
Egocentric Spatial Memory
Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspective.
Egocentric Spatial Memory Network
During the exploration, our proposed ESM network model updates belief of the global map based on local observations using a recurrent neural network.
Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks
Through competition with discriminator, the generator progressively improves quality of the future frames and thus anticipates future gaze better.
