Vision-Language Models (VLMs) have shown strong performance across a wide range of downstream vision tasks, thanks to the complementary information contained in the respective domains. Despite the performance gains, most of these approaches rely on aligning these domains using the cosine similarity metric, which fails...
Ankita Das, Ambarish Parthasarathy, S. Channappayya et al.· 0 citations
In computational neuroscience, the statistical nature of primate visual responses has long served as a benchmark for efficient coding. Specifically, previous works demonstrated that in the primate Anterior Inferotemporal (AIT) cortex, population sparseness $(S_{p})$ significantly exceeds single-neuron sparseness $\left...
Ankit Sharma, S. Channappayya· International Conference on...· 0 citations
Action prediction from frames and videos is a well-studied problem. Models trained with a single modality, mostly vision, will fail in low-light conditions. Recent works have attempted to predict action categories using vision-language and audio-visual models. A challenge, however, is that some dataset annotations lack...
A. R, Ambarish Parthasarathy, Sucharitha Devarakonda et al.· International Conference on...· 0 citations
Understanding long-range videos remains a key challenge in computer vision due to high temporal redundancy and computational burden. Despite strong performance of recent models, they are constrained in terms of scalability and generalization when applied to longer video sequences. In this work, we present Keyframe-base...
Rahul Kumar, S. Channappayya· International Conference on...· 0 citations
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