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Found 6 papers, 4 papers with code
Learning Conditional Invariances through Non-Commutativity
Invariance learning algorithms that conditionally filter out domain-specific random variables as distractors, do so based only on the data semantics, and not the target domain under evaluation.
CLIPDrawX: Primitive-based Explanations for Text Guided Sketch Synthesis
With the goal of understanding the visual concepts that CLIP associates with text prompts, we show that the latent space of CLIP can be visualized solely in terms of linear transformations on simple geometric primitives like circles and straight lines.
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection
The introduction of the MUStARD dataset, and its emotion recognition extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon -- expressed not only in natural language text, but also through manners of speech (like tonality and intonation) and visual cues (facial expression).
Data-Free Sketch-Based Image Retrieval
For the first time, we identify that for data-scarce tasks like Sketch-Based Image Retrieval (SBIR), where the difficulty in acquiring paired photos and hand-drawn sketches limits data-dependent cross-modal learning algorithms, DFL can prove to be a much more practical paradigm.
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information.
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Fine-grained categories that largely share the same set of parts cannot be discriminated based on part information alone, as they mostly differ in the way the local parts relate to the overall global structure of the object.
