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Arpita Chowdhury

7 accepted papers

2026

AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models

CVPR 2026

The rise of vision foundation models (VFMs) calls for systematic evaluation. A common approach pairs VFMs with large language models (LLMs) as general-purpose heads, followed by evaluation on broad Visual Question Answering (VQA) benchmarks. However, this protocol has two key blind spots: (i) Instru

Cited by 7SourceScholar
2026

BioCAP: Exploiting Synthetic Captions Beyond Labels in Biological Foundation Models

ICLR 2026poster

This work investigates descriptive captions as an additional source of supervision for biological multimodal foundation models. Images and captions can be viewed as complementary samples from the latent morphospace of a species, each capturing certain biological traits. Incorporating captions during…

Cited by 0SourcecodeScholar
2025

Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation

CVPR 2025poster

Class activation map (CAM) has been widely used to highlight image regions that contribute to class predictions. Despite its simplicity and computational efficiency, CAM often struggles to identify discriminative regions that distinguish visually similar fine-grained classes. Prior efforts address t…

2025

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

CVPR 2025poster

We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bird species. Pre-trained ViTs, such as DINO, have demonstrated remarkable capabilitie…

2024

A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis

ICLR 2024poster

We present a novel usage of Transformers to make image classification interpretable. Unlike mainstream classifiers that wait until the last fully connected layer to incorporate class information to make predictions, we investigate a proactive approach, asking each class to search for itself in an im…

2024

Fine-Tuning is Fine, if Calibrated

NeurIPS 2024poster

Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing valuable knowledge the model had learned in pre-training. For example, fine-tuning a pre-trained classifier capable of r…

2024

MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs

NeurIPS 2024poster

The ability to compare objects, scenes, or situations is crucial for effective decision-making and problem-solving in everyday life. For instance, comparing the freshness of apples enables better choices during grocery shopping, while comparing sofa designs helps optimize the aesthetics of our livin…