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Found 26 papers, 16 papers with code
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.
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 CNN or 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.
On the Robustness, Generalization, and Forgetting of Shape-Texture Debiased Continual Learning
Tremendous progress has been made in continual learning to maintain good performance on old tasks when learning new tasks by tackling the catastrophic forgetting problem of neural networks.
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).
What makes domain generalization hard?
Here we present SemanticDG (Semantic Domain Generalization): a benchmark with 15 photo-realistic domains with the same geometry, scene layout and camera parameters as the popular 3D ScanNet dataset, but with controlled domain shifts in lighting, materials, and viewpoints.
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.
Tuned Compositional Feature Replays for Efficient Stream Learning
CRUMB's memory blocks are tuned to enhance replay: a single feature map stored, reconstructed, and replayed by CRUMB mitigates forgetting during video stream learning more effectively than an entire image, even though it occupies only 3. 6% as much memory.
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.
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.
