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Discovering Hidden Visual Concepts Beyond Linguistic Input in Infant Learning
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Analyzes the infant-trained CVCL model via CLIP-Dissect neuron labeling and a training-free NeuronClassiFier to show hidden visual concept neurons recognize objects beyond the parental-speech vocabulary, and compares its layer-wise representations with CLIP and ImageNet models.
Problem
Whether a model trained on an infant's egocentric video and transcribed parental speech acquires visual concepts beyond the words it heard, and how its internal representations differ from broadly trained vision models.
Approach
Apply CLIP-Dissect neuron labeling with a large combined concept set to the infant CVCL vision encoder, then select the neuron most aligned with a target label and classify images by its activation, enabling recognition of out-of-vocabulary concepts without training.
Key results
- NeuronClassifier with CVCL neurons reaches 79.50% in-vocab and 76.81% out-of-vocab 4-way accuracy, versus 36.18% in-vocab for vanilla CVCL.
- Out-of-vocabulary concepts have higher mean AoA (6.82) than in-vocabulary concepts (4.99), t(82)=4.64, p<0.0001.
- CVCL's shallow layers show high CKA similarity to CLIP and ResNeXt50 but diverge in the final layer; CVCL covers slightly under 50% of Konkle classes via neuron labeling versus 28% for randomized.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace vanilla image-text similarity with NeuronClassifier (visual concept neurons, training-free) | CVCL-ResNeXt50 · Konkle object dataset 4-way, in-vocab | in-vocab accuracy: 36.18 to 79.50 | Helped |
| Use NeuronClassifier to classify beyond the training vocabulary | CVCL-ResNeXt50 · Konkle object dataset 4-way, out-of-vocab | out-of-vocab accuracy: reported as 76.81±0.35; vanilla cannot be computed (no text encoder for these classes) | Helped |
| Out-of-vocab concept neurons vs in-vocab concepts in CVCL | CVCL visual concept neurons · SAYCam-S vocabulary AoA ratings (Kuperman) | mean AoA (years): in-vocab 4.99 vs out-of-vocab 6.82 (t(82)=4.64, p<0.0001) | No clear effect |
| Switch from vanilla to NeuronClassifier on CLIP-ResNet50 | CLIP-ResNet50 · Konkle object dataset 4-way, all | accuracy: 97.42 to 89.79 | Hurt |
| Class coverage via neuron labeling: broadly pretrained vs infant models | CVCL vs CLIP/ResNeXt · Konkle class coverage | percent of classes covered: CVCL slightly under 50%; CVCL-Randomized about 28%; CLIP and ResNeXt over 50% | Hurt |
| Layer-wise CKA similarity: CVCL deeper layers vs ImageNet/CLIP | CVCL · ImageNet validation set, CKA | layer similarity | Hurt |
| Count of object and scene concept neurons in deeper layers | CVCL vs ImageNet ResNeXt50 · Broden concept categories | number of unique visual concepts | Hurt |
| Maximum number of classes with NeuronClassifier on CVCL limited by class coverage | CVCL-ResNeXt50 · n-way classification scaling | max classes classifiable: maximum 31 classes | Hurt |
Surprising findings
- CVCL with NeuronClassifier classifies out-of-vocabulary classes (76.81%) nearly as well as in-vocabulary ones (79.50%).
- CLIP-ResNet50 underperforms in the NeuronClassifier setting (89.79% overall) compared with its vanilla text-based classification (97.42%).
Limitations
- Training data (SAYCam-S) could not be accessed, so analysis relies on the pretrained model only.
- Neuron labels can be spurious and not all neurons are interpretable.
- CVCL with the method can classify at most 31 classes due to limited class coverage.
Experimental setup
Datasets: SAYCam-S Konkle object dataset ImageNet validation set Broden Kuperman age-of-acquisition norms
Benchmarks: Konkle object dataset 4-way classification
Metrics: classification accuracy class coverage CKA similarity age of acquisition mean difference
Baselines: CVCL vanilla n-way classification CLIP-ResNet50 ResNeXt50 DINO-S-ResNeXt50 CVCL-Randomized
Builds on: CVCL CLIP-Dissect Network Dissection CLIP Centered Kernel Alignment
Closest papers by meaning
- Learning from an Infant’s Visual Experience (AAAI 2024 · similarity 0.88)
- BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models (CVPR 2026 · similarity 0.87)
- BabyVision: Visual Reasoning Beyond Language (ICML 2026 · similarity 0.87)
- NeuronEye: Query-Guided Visual Concept Activation for Vision-Language Reasoning (NeurIPS 2026 · similarity 0.86)
- Alignment of CNN and Human Judgments of Geometric and Topological Concepts (AAAI 2025 · similarity 0.86)
- Concept-RuleNet: Grounded Multi-Agent Neurosymbolic Reasoning in Vision Language Models (AAAI 2026 · similarity 0.85)
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